diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..b394645
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,24 @@
+meta_datasets/
+__pycache__
+assets
+cache
+tmp
+.ropeproject
+train_loop.png
+scripts/tmp_data/train.txt
+scripts/tmp_data/valid.txt
+scripts/tmp_data/test.txt
+wavenet_models/
+snippets/tmp
+sbatch_logs/
+*last.ckpt
+*.pth
+taming_transformers.egg-info/
+logs
+!melgan/logs
+*vggishish16.pt
+data/ffhq
+data/celebahq
+melgan/logs/*e*
+data/backup_links
+data/backup_demo
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000..0ee7bc5
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,281 @@
+FROM ubuntu:18.04
+
+# RUN rm /etc/apt/sources.list.d/cuda.list && rm /etc/apt/sources.list.d/nvidia-ml.list
+
+RUN apt-get update
+RUN apt-get install -y sudo
+
+RUN adduser --disabled-password --gecos '' ubuntu
+RUN adduser ubuntu sudo
+RUN echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers
+USER ubuntu
+
+SHELL ["/bin/bash", "-c"]
+
+RUN sudo apt-get -qq install curl vim git zip libglib2.0-0 libsndfile1 libsm6 libxext6 libxrender-dev
+
+WORKDIR /home/ubuntu/
+
+RUN curl -O https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
+RUN bash ./Miniconda3-latest-Linux-x86_64.sh -b
+ENV PATH="/home/ubuntu/miniconda3/bin:$PATH"
+RUN echo ". /home/ubuntu/miniconda3/etc/profile.d/conda.sh" >> ~/.profile
+RUN conda init bash
+RUN conda config --set auto_activate_base false
+
+RUN echo $'name: specvqgan\n\
+channels:\n\
+  - pytorch\n\
+  - conda-forge\n\
+  - defaults\n\
+dependencies:\n\
+  - _libgcc_mutex=0.1=conda_forge\n\
+  - _openmp_mutex=4.5=1_llvm\n\
+  - abseil-cpp=20210324.0=h9c3ff4c_0\n\
+  - absl-py=0.12.0=pyhd8ed1ab_0\n\
+  - aiohttp=3.7.4=py38h497a2fe_0\n\
+  - altair=4.1.0=py_1\n\
+  - appdirs=1.4.4=pyh9f0ad1d_0\n\
+  - argh=0.26.2=pyh9f0ad1d_1002\n\
+  - argon2-cffi=20.1.0=py38h497a2fe_2\n\
+  - arrow-cpp=4.0.0=py38hd6878d3_0_cpu\n\
+  - astor=0.8.1=pyh9f0ad1d_0\n\
+  - async-timeout=3.0.1=py_1000\n\
+  - async_generator=1.10=py_0\n\
+  - attrs=20.3.0=pyhd3deb0d_0\n\
+  - audioread=2.1.9=py38h578d9bd_0\n\
+  - autopep8=1.5.6=pyhd3eb1b0_0\n\
+  - aws-c-cal=0.4.5=h76129ab_8\n\
+  - aws-c-common=0.5.2=h7f98852_0\n\
+  - aws-c-event-stream=0.2.7=h6bac3ce_1\n\
+  - aws-c-io=0.9.1=ha5b09cb_1\n\
+  - aws-checksums=0.1.11=h99e32c3_3\n\
+  - aws-sdk-cpp=1.8.151=hceb1b1e_1\n\
+  - backcall=0.2.0=pyh9f0ad1d_0\n\
+  - backports=1.0=py_2\n\
+  - backports.functools_lru_cache=1.6.4=pyhd8ed1ab_0\n\
+  - base58=2.1.0=pyhd8ed1ab_0\n\
+  - blas=1.0=mkl\n\
+  - bleach=3.3.0=pyh44b312d_0\n\
+  - blinker=1.4=py_1\n\
+  - boto3=1.17.59=pyhd8ed1ab_0\n\
+  - botocore=1.20.59=pyhd8ed1ab_1\n\
+  - brotli=1.0.9=h9c3ff4c_4\n\
+  - brotlipy=0.7.0=py38h497a2fe_1001\n\
+  - bzip2=1.0.8=h7f98852_4\n\
+  - c-ares=1.17.1=h7f98852_1\n\
+  - ca-certificates=2021.5.30=ha878542_0\n\
+  - cachetools=4.2.2=pyhd8ed1ab_0\n\
+  - certifi=2021.5.30=py38h578d9bd_0\n\
+  - cffi=1.14.5=py38ha65f79e_0\n\
+  - chardet=4.0.0=py38h578d9bd_1\n\
+  - click=7.1.2=pyh9f0ad1d_0\n\
+  - cryptography=3.4.7=py38ha5dfef3_0\n\
+  - cudatoolkit=11.1.1=h6406543_8\n\
+  - cycler=0.10.0=py_2\n\
+  - defusedxml=0.7.1=pyhd8ed1ab_0\n\
+  - entrypoints=0.3=pyhd8ed1ab_1003\n\
+  - ffmpeg=4.3.1=hca11adc_2\n\
+  - flake8=3.9.0=pyhd3eb1b0_0\n\
+  - freetype=2.10.4=h0708190_1\n\
+  - fsspec=2021.4.0=pyhd8ed1ab_0\n\
+  - future=0.18.2=py38h578d9bd_3\n\
+  - gettext=0.19.8.1=h0b5b191_1005\n\
+  - gflags=2.2.2=he1b5a44_1004\n\
+  - gitdb=4.0.7=pyhd8ed1ab_0\n\
+  - gitpython=3.1.15=pyhd8ed1ab_0\n\
+  - glog=0.4.0=h49b9bf7_3\n\
+  - gmp=6.2.1=h58526e2_0\n\
+  - gnutls=3.6.13=h85f3911_1\n\
+  - google-auth=1.28.0=pyh44b312d_0\n\
+  - google-auth-oauthlib=0.4.1=py_2\n\
+  - grpc-cpp=1.37.0=h36de60a_1\n\
+  - grpcio=1.37.0=py38hdd6454d_0\n\
+  - idna=2.10=pyh9f0ad1d_0\n\
+  - imageio=2.9.0=py_0\n\
+  - imageio-ffmpeg=0.4.3=pyhd8ed1ab_0\n\
+  - importlib-metadata=4.0.1=py38h578d9bd_0\n\
+  - ipykernel=5.5.5=py38hd0cf306_0\n\
+  - ipython=7.22.0=py38hd0cf306_0\n\
+  - ipython_genutils=0.2.0=py_1\n\
+  - ipywidgets=7.6.3=pyhd3deb0d_0\n\
+  - jedi=0.18.0=py38h578d9bd_2\n\
+  - jinja2=2.11.3=pyh44b312d_0\n\
+  - jmespath=0.10.0=pyh9f0ad1d_0\n\
+  - joblib=1.0.1=pyhd8ed1ab_0\n\
+  - jpeg=9b=h024ee3a_2\n\
+  - jsonschema=3.2.0=pyhd8ed1ab_3\n\
+  - jupyter_client=6.1.12=pyhd8ed1ab_0\n\
+  - jupyter_core=4.7.1=py38h578d9bd_0\n\
+  - jupyterlab_pygments=0.1.2=pyh9f0ad1d_0\n\
+  - jupyterlab_widgets=1.0.0=pyhd8ed1ab_1\n\
+  - kiwisolver=1.3.1=py38h1fd1430_1\n\
+  - krb5=1.17.2=h926e7f8_0\n\
+  - lame=3.100=h7f98852_1001\n\
+  - lcms2=2.12=h3be6417_0\n\
+  - ld_impl_linux-64=2.35.1=hea4e1c9_2\n\
+  - libcurl=7.76.1=hc4aaa36_1\n\
+  - libedit=3.1.20191231=he28a2e2_2\n\
+  - libev=4.33=h516909a_1\n\
+  - libevent=2.1.10=hcdb4288_3\n\
+  - libffi=3.3=h58526e2_2\n\
+  - libflac=1.3.3=h9c3ff4c_1\n\
+  - libgcc-ng=9.3.0=h2828fa1_19\n\
+  - libgfortran-ng=7.5.0=h14aa051_19\n\
+  - libgfortran4=7.5.0=h14aa051_19\n\
+  - libiconv=1.16=h516909a_0\n\
+  - libllvm10=10.0.1=he513fc3_3\n\
+  - libnghttp2=1.43.0=h812cca2_0\n\
+  - libogg=1.3.4=h7f98852_1\n\
+  - libopus=1.3.1=h7f98852_1\n\
+  - libpng=1.6.37=h21135ba_2\n\
+  - libprotobuf=3.15.8=h780b84a_0\n\
+  - librosa=0.8.0=pyh9f0ad1d_0\n\
+  - libsndfile=1.0.31=h9c3ff4c_1\n\
+  - libsodium=1.0.18=h36c2ea0_1\n\
+  - libssh2=1.9.0=ha56f1ee_6\n\
+  - libstdcxx-ng=9.3.0=h6de172a_19\n\
+  - libthrift=0.14.1=he6d91bd_1\n\
+  - libtiff=4.1.0=h2733197_1\n\
+  - libutf8proc=2.6.1=h7f98852_0\n\
+  - libuv=1.41.0=h7f98852_0\n\
+  - libvorbis=1.3.7=h9c3ff4c_0\n\
+  - llvm-openmp=11.1.0=h4bd325d_1\n\
+  - llvmlite=0.36.0=py38h4630a5e_0\n\
+  - lz4-c=1.9.3=h9c3ff4c_0\n\
+  - markdown=3.3.4=pyhd8ed1ab_0\n\
+  - markupsafe=1.1.1=py38h497a2fe_3\n\
+  - matplotlib-base=3.4.1=py38hcc49a3a_0\n\
+  - mccabe=0.6.1=py38_1\n\
+  - mistune=0.8.4=py38h497a2fe_1003\n\
+  - mkl=2020.4=h726a3e6_304\n\
+  - mkl-service=2.3.0=py38h1e0a361_2\n\
+  - mkl_fft=1.3.0=py38h5c078b8_1\n\
+  - mkl_random=1.2.0=py38hc5bc63f_1\n\
+  - multidict=5.1.0=py38h497a2fe_1\n\
+  - nbclient=0.5.3=pyhd8ed1ab_0\n\
+  - nbconvert=6.0.7=py38h578d9bd_3\n\
+  - nbformat=5.1.3=pyhd8ed1ab_0\n\
+  - ncurses=6.2=h58526e2_4\n\
+  - nest-asyncio=1.5.1=pyhd8ed1ab_0\n\
+  - nettle=3.6=he412f7d_0\n\
+  - ninja=1.10.2=h4bd325d_0\n\
+  - notebook=6.3.0=pyha770c72_1\n\
+  - numba=0.53.1=py38h0e12cce_0\n\
+  - numpy=1.19.2=py38h54aff64_0\n\
+  - numpy-base=1.19.2=py38hfa32c7d_0\n\
+  - oauthlib=3.0.1=py_0\n\
+  - olefile=0.46=pyh9f0ad1d_1\n\
+  - omegaconf=2.0.6=py38h578d9bd_0\n\
+  - openh264=2.1.1=h780b84a_0\n\
+  - openssl=1.1.1k=h7f98852_0\n\
+  - orc=1.6.7=heec2584_1\n\
+  - packaging=20.9=pyh44b312d_0\n\
+  - pandas=1.2.4=py38h1abd341_0\n\
+  - pandoc=2.12=h7f98852_0\n\
+  - pandocfilters=1.4.2=py_1\n\
+  - parquet-cpp=1.5.1=2\n\
+  - parso=0.8.2=pyhd8ed1ab_0\n\
+  - pexpect=4.8.0=pyh9f0ad1d_2\n\
+  - pickleshare=0.7.5=py_1003\n\
+  - pillow=8.2.0=py38he98fc37_0\n\
+  - pip=21.1=pyhd8ed1ab_0\n\
+  - pooch=1.3.0=pyhd8ed1ab_0\n\
+  - prometheus_client=0.10.1=pyhd8ed1ab_0\n\
+  - prompt-toolkit=3.0.18=pyha770c72_0\n\
+  - protobuf=3.15.8=py38h709712a_0\n\
+  - ptyprocess=0.7.0=pyhd3deb0d_0\n\
+  - pyarrow=4.0.0=py38hc9229eb_0_cpu\n\
+  - pyasn1=0.4.8=py_0\n\
+  - pyasn1-modules=0.2.7=py_0\n\
+  - pycodestyle=2.6.0=pyhd3eb1b0_0\n\
+  - pycparser=2.20=pyh9f0ad1d_2\n\
+  - pydeck=0.5.0=pyh9f0ad1d_0\n\
+  - pyflakes=2.2.0=pyhd3eb1b0_0\n\
+  - pygments=2.8.1=pyhd8ed1ab_0\n\
+  - pyjwt=2.0.1=pyhd8ed1ab_1\n\
+  - pyopenssl=20.0.1=pyhd8ed1ab_0\n\
+  - pyparsing=2.4.7=pyh9f0ad1d_0\n\
+  - pyrsistent=0.17.3=py38h497a2fe_2\n\
+  - pysocks=1.7.1=py38h578d9bd_3\n\
+  - pysoundfile=0.10.3.post1=pyhd3deb0d_0\n\
+  - python=3.8.8=hffdb5ce_0_cpython\n\
+  - python-dateutil=2.8.1=py_0\n\
+  - python_abi=3.8=1_cp38\n\
+  - pytorch=1.8.1=py3.8_cuda11.1_cudnn8.0.5_0\n\
+  - pytorch-lightning=1.2.10=pyhd8ed1ab_0\n\
+  - pytz=2021.1=pyhd8ed1ab_0\n\
+  - pyyaml=5.4.1=py38h497a2fe_0\n\
+  - pyzmq=22.0.3=py38h2035c66_1\n\
+  - re2=2021.04.01=h9c3ff4c_0\n\
+  - readline=8.1=h46c0cb4_0\n\
+  - requests=2.25.1=pyhd3deb0d_0\n\
+  - requests-oauthlib=1.3.0=pyh9f0ad1d_0\n\
+  - resampy=0.2.2=py_0\n\
+  - rsa=4.7.2=pyh44b312d_0\n\
+  - s2n=1.0.0=h9b69904_0\n\
+  - s3transfer=0.4.2=pyhd8ed1ab_0\n\
+  - scikit-learn=0.24.1=py38ha9443f7_0\n\
+  - scipy=1.6.2=py38h91f5cce_0\n\
+  - send2trash=1.5.0=py_0\n\
+  - setuptools=49.6.0=py38h578d9bd_3\n\
+  - six=1.15.0=pyh9f0ad1d_0\n\
+  - smmap=3.0.5=pyh44b312d_0\n\
+  - snappy=1.1.8=he1b5a44_3\n\
+  - sqlite=3.35.5=h74cdb3f_0\n\
+  - streamlit=0.80.0=pyhd8ed1ab_0\n\
+  - tensorboard=2.4.1=pyhd8ed1ab_0\n\
+  - tensorboard-plugin-wit=1.8.0=pyh44b312d_0\n\
+  - terminado=0.9.4=py38h578d9bd_0\n\
+  - testpath=0.4.4=py_0\n\
+  - threadpoolctl=2.1.0=pyh5ca1d4c_0\n\
+  - tk=8.6.10=h21135ba_1\n\
+  - toml=0.10.2=pyhd8ed1ab_0\n\
+  - toolz=0.11.1=py_0\n\
+  - torchaudio=0.8.1=py38\n\
+  - torchmetrics=0.3.1=pyhd8ed1ab_0\n\
+  - torchvision=0.9.1=py38_cu111\n\
+  - tornado=6.1=py38h497a2fe_1\n\
+  - tqdm=4.60.0=pyhd8ed1ab_0\n\
+  - traitlets=5.0.5=py_0\n\
+  - typing-extensions=3.7.4.3=0\n\
+  - typing_extensions=3.7.4.3=py_0\n\
+  - tzlocal=2.1=pyh9f0ad1d_0\n\
+  - urllib3=1.26.4=pyhd8ed1ab_0\n\
+  - validators=0.18.2=pyhd3deb0d_0\n\
+  - watchdog=2.0.3=py38h578d9bd_0\n\
+  - wcwidth=0.2.5=pyh9f0ad1d_2\n\
+  - webencodings=0.5.1=py_1\n\
+  - werkzeug=1.0.1=pyh9f0ad1d_0\n\
+  - wheel=0.36.2=pyhd3deb0d_0\n\
+  - widgetsnbextension=3.5.1=py38h578d9bd_4\n\
+  - x264=1!161.3030=h7f98852_1\n\
+  - xz=5.2.5=h516909a_1\n\
+  - yaml=0.2.5=h516909a_0\n\
+  - yarl=1.6.3=py38h497a2fe_1\n\
+  - zeromq=4.3.4=h9c3ff4c_0\n\
+  - zipp=3.4.1=pyhd8ed1ab_0\n\
+  - zlib=1.2.11=h516909a_1010\n\
+  - zstd=1.4.9=ha95c52a_0\n\
+  - pip:\n\
+    - albumentations==0.5.2\n\
+    - decorator==4.4.2\n\
+    - imgaug==0.4.0\n\
+    - networkx==2.5.1\n\
+    - opencv-python==4.1.2.30\n\
+    - opencv-python-headless==4.5.1.48\n\
+    - pywavelets==1.1.1\n\
+    - scikit-image==0.18.1\n\
+    - shapely==1.7.1\n\
+    - test-tube==0.7.5\n\
+    - tifffile==2021.4.8\n\
+
+' >> conda_env.yml
+
+RUN conda env create -f conda_env.yml
+RUN conda clean -afy
+RUN rm ./Miniconda3-latest-Linux-x86_64.sh
+
+SHELL ["conda", "run", "-n", "specvqgan", "/bin/bash", "-c"]
+
+ENTRYPOINT ["conda", "run", "--no-capture-output", "-n", "specvqgan"]
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..30a1a4f
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2021 Vladimir Iashin
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..cdeca14
--- /dev/null
+++ b/README.md
@@ -0,0 +1,557 @@
+# Taming Visually Guided Sound Generation
+• [[Project Page](https://v-iashin.github.io/SpecVQGAN)]
+• [arXiv (Coming Soon)]
+• [[Poster](https://v-iashin.github.io/images/specvqgan/poster.pdf)]
+•
+
+<img src="https://github.com/v-iashin/v-iashin.github.io/raw/master/images/specvqgan/specvqgan_vggsound_samples.jpg" alt="Generated Samples Using our Model" width="900">
+
+Listen for the samples on our [project page](https://v-iashin.github.io/SpecVQGAN).
+
+# Overview
+We propose to tame the visually guided sound generation by shrinking a training dataset to a set of representative vectors aka. a codebook.
+These codebook vectors can, then, be controllably sampled to form a novel sound given a set of visual cues as a prime.
+
+The codebook is trained on spectrograms similarly to [VQGAN](https://arxiv.org/abs/2012.09841) (an upgraded [VQVAE](https://arxiv.org/abs/1711.00937)).
+We refer to it as **Spectrogram VQGAN**
+
+<img src="https://github.com/v-iashin/v-iashin.github.io/raw/master/images/specvqgan/codebook.svg" alt="Spectrogram VQGAN" width="900">
+
+Once the spectrogram codebook is trained, we can train a **transformer** (a variant of [GPT-2](https://openai.com/blog/better-language-models/)) to autoregressively sample the codebook entries as tokens conditioned on a set of visual features
+
+<img src="https://github.com/v-iashin/v-iashin.github.io/raw/master/images/specvqgan/transformer.svg" alt="Vision-based Conditional Cross-modal Autoregressive Sampler" width="900">
+
+This approach allows training a spectrogram generation model which produces long, relevant, and high-fidelity sounds while supporting tens of data classes.
+
+- [Taming Visually Guided Sound Generation](#taming-visually-guided-sound-generation)
+- [Overview](#overview)
+- [Environment Preparation](#environment-preparation)
+  - [Conda](#conda)
+  - [Docker](#docker)
+- [Data](#data)
+  - [Download](#download)
+  - [Extract Features Manually](#extract-features-manually)
+- [Pretrained Models](#pretrained-models)
+  - [Codebooks](#codebooks)
+  - [Transformers](#transformers)
+  - [VGGish-ish, Melception, and MelGAN](#vggish-ish-melception-and-melgan)
+- [Training](#training)
+  - [Training a Spectrogram Codebook](#training-a-spectrogram-codebook)
+  - [Training a Transformer](#training-a-transformer)
+    - [VAS Transformer](#vas-transformer)
+    - [VGGSound Transformer](#vggsound-transformer)
+    - [Controlling the Condition Size](#controlling-the-condition-size)
+  - [Training VGGish-ish and Melception](#training-vggish-ish-and-melception)
+  - [Training MelGAN](#training-melgan)
+- [Evaluation](#evaluation)
+- [Sampling Tool](#sampling-tool)
+- [The Neural Audio Codec Demo](#the-neural-audio-codec-demo)
+- [Citation](#citation)
+- [Acknowledgments](#acknowledgments)
+
+<!-- The link to this section is used in demo.ipynb -->
+# Environment Preparation
+
+During experimentation, we used Linux machines with `conda` virtual environments, PyTorch 1.8 and CUDA 11.
+
+Start by cloning this repo
+```bash
+git clone https://github.com/v-iashin/SpecVQGAN.git
+```
+
+Next, install the environment.
+For your convenience, we provide both `conda` and `docker` environments.
+
+## Conda
+```bash
+conda env create -f conda_env.yml
+```
+Test your environment
+```bash
+conda activate specvqgan
+python -c "import torch; print(torch.cuda.is_available())"
+# True
+```
+
+## Docker
+Download the image from Docker Hub and test if CUDA is available:
+```bash
+docker run \
+    --mount type=bind,source=/absolute/path/to/SpecVQGAN/,destination=/home/ubuntu/SpecVQGAN/ \
+    --mount type=bind,source=/absolute/path/to/logs/,destination=/home/ubuntu/SpecVQGAN/logs/ \
+    --mount type=bind,source=/absolute/path/to/vggsound/features/,destination=/home/ubuntu/SpecVQGAN/data/vggsound/ \
+    --shm-size 8G \
+    -it --gpus '"device=0"' \
+    iashin/specvqgan:latest \
+    python
+>>> import torch; print(torch.cuda.is_available())
+# True
+```
+or build it yourself
+```bash
+docker build - < Dockerfile --tag specvqgan
+```
+
+# Data
+In this project, we used [VAS](https://github.com/PeihaoChen/regnet#download-datasets) and [VGGSound](www.robots.ox.ac.uk/~vgg/data/vggsound/) datasets.
+VAS can be downloaded directly using the link provided in the [RegNet](https://github.com/PeihaoChen/regnet#download-datasets) repository.
+For VGGSound, however, one might need to retrieve videos directly from YouTube.
+
+## Download
+The scripts will download features, check the `md5` sum, unpack, and do a clean-up for each part of the dataset:
+```bash
+cd ./data
+# 24GB
+bash ./download_vas_features.sh
+# 420GB (+ 420GB if you also need ResNet50 Features)
+bash ./download_vggsound_features.sh
+```
+The unpacked features are going to be saved in `./data/downloaded_features/*`.
+Move them to `./data/vas` and `./data/vggsound` such that the folder structure would match the structure of the demo files.
+By default, it will download `BN Inception` features, to download `ResNet50` features uncomment the lines in scripts `./download_*_features.sh`
+
+If you wish to download the parts manually, use the following URL templates:
+
+- `https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/vas/*.tar`
+- `https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/vggsound/*.tar`
+
+Also, make sure to check the `md5` sums provided in [`./data/md5sum_vas.md5`](./data/md5sum_vas.md5) and [`./data/md5sum_vggsound.md5`](./data/md5sum_vggsound.md5) along with file names.
+
+Note, we distribute features for the VGGSound dataset in 64 parts.
+Each part holds ~3k clips and can be used independently as a subset of the whole dataset (the parts are not class-stratified though).
+
+## Extract Features Manually
+
+For `BN Inception` features, we employ the same procedure as [RegNet](https://github.com/PeihaoChen/regnet#data-preprocessing).
+
+For `ResNet50` features, we rely on [video_features](https://v-iashin.github.io/video_features/models/resnet/)
+repository and used these commands:
+```bash
+# VAS (few hours on three 2080Ti)
+strings=("dog" "fireworks" "drum" "baby" "gun" "sneeze" "cough" "hammer")
+for class in "${strings[@]}"; do
+    python main.py \
+        --feature_type resnet50 \
+        --device_ids 0 1 2 \
+        --batch_size 86 \
+        --extraction_fps 21.5 \
+        --file_with_video_paths ./paths_to_mp4_${class}.txt \
+        --output_path ./data/vas/features/${class}/feature_resnet50_dim2048_21.5fps \
+        --on_extraction save_pickle
+done
+
+# VGGSound (6 days on three 2080Ti)
+python main.py \
+    --feature_type resnet50 \
+    --device_ids 0 1 2 \
+    --batch_size 86 \
+    --extraction_fps 21.5 \
+    --file_with_video_paths ./paths_to_mp4s.txt \
+    --output_path ./data/vggsound/feature_resnet50_dim2048_21.5fps \
+    --on_extraction save_pickle
+```
+Similar to `BN Inception`, we need to "tile" (cycle) a video if it is shorter than 10s. For
+`ResNet50` we achieve this by tiling the resulting frame-level features up to 215 on temporal dimension, e.g. as follows:
+```python
+feats = pickle.load(open(path, 'rb')).astype(np.float32)
+reps = 1 + (215 // feats.shape[0])
+feats = np.tile(feats, (reps, 1))[:215, :]
+with open(new_path, 'wb') as file:
+    pickle.dump(feats, file)
+```
+
+<!-- <details>
+<summary>Downloading VGGSound from Scratch</summary>
+
+1. We will rely on the AudioSet download script. To adapt it, we refactor `vggsound.csv` using the following script such that can be used in a AudioSet downloader:
+
+```python
+import pandas as pd
+
+VGGSOUND_PATH = './data/vggsound.csv'
+VGGSOUND_REF_PATH = './data/vggsound_ref.csv'
+vggsound_meta = pd.read_csv(VGGSOUND_PATH, names=['YTID', 'start_seconds', 'positive_labels', 'split'])
+vggsound_meta['end_seconds'] = vggsound_meta['start_seconds'] + 10
+vggsound_meta = vggsound_meta.drop(['split'], axis=1)
+vggsound_meta = vggsound_meta[['YTID', 'start_seconds', 'end_seconds', 'positive_labels']]
+print(list(vggsound_meta.columns))
+print(vggsound_meta.head())
+vggsound_meta.to_csv(VGGSOUND_REF_PATH, sep=',', index=None, header=None)
+```
+
+1. We also add 3 lines with `# placeholder` on top of the `vggsound_ref.csv` to match the style as AudioSet has
+some statistics there.
+1. Rent an instance (GoogleCloud/AWS/Pouta), allocate an IP. Disk 800GB: 300 GB for video and 90 for audio + zipping + OS
+1. `git clone https://github.com/marl/audiosetdl` and check out to `ebd89c5` commit.  This code provides a script to download AudioSet in parallel on several CPUs.
+1. Create a file with conda environment in `down_audioset.yaml` with content as follows:
+
+```yaml
+name: down_audioset
+channels:
+  - conda-forge
+  - yaafe
+  - defaults
+dependencies:
+  - _libgcc_mutex=0.1=main
+  - bzip2=1.0.8=h7b6447c_0
+  - ca-certificates=2020.6.24=0
+  - certifi=2020.6.20=py38_0
+  - ffmpeg=4.2.2=h20bf706_0
+  - freetype=2.10.2=h5ab3b9f_0
+  - gmp=6.1.2=h6c8ec71_1
+  - gnutls=3.6.5=h71b1129_1002
+  - lame=3.100=h7b6447c_0
+  - ld_impl_linux-64=2.33.1=h53a641e_7
+  - libedit=3.1.20191231=h7b6447c_0
+  - libffi=3.3=he6710b0_2
+  - libflac=1.3.1=0
+  - libgcc-ng=9.1.0=hdf63c60_0
+  - libogg=1.3.2=0
+  - libopus=1.3.1=h7b6447c_0
+  - libpng=1.6.37=hbc83047_0
+  - libstdcxx-ng=9.1.0=hdf63c60_0
+  - libvpx=1.7.0=h439df22_0
+  - mad=0.15.1b=he1b5a44_0
+  - ncurses=6.2=he6710b0_1
+  - nettle=3.4.1=hbb512f6_0
+  - openh264=2.1.0=hd408876_0
+  - openssl=1.1.1g=h516909a_0
+  - pip=20.1.1=py38_1
+  - python=3.8.3=hcff3b4d_2
+  - python_abi=3.8=1_cp38
+  - readline=8.0=h7b6447c_0
+  - setuptools=47.3.1=py38_0
+  - sqlite=3.32.3=h62c20be_0
+  - tk=8.6.10=hbc83047_0
+  - wheel=0.34.2=py38_0
+  - x264=1!157.20191217=h7b6447c_0
+  - xz=5.2.5=h7b6447c_0
+  - zlib=1.2.11=h7b6447c_3
+  - pip:
+    - cffi==1.14.0
+    - multiprocessing-logging==0.2.4
+    - numpy==1.19.0
+    - pafy==0.5.3.1
+    - pycparser==2.20
+    - pysoundfile==0.9.0.post1
+    - scipy==1.5.1
+    - sk-video==1.1.8
+    - sox==1.3.3
+    - youtube-dl==2020.6.16.1
+```
+
+1. Install the environment: `conda env create -f down_audioset.yml`
+1. Open `download_audioset.py` in the cloned repo (`audiosetdl`) and comment the following lines as AudioSet has 3 parts:
+
+```python
+    # download_subset(balanced_train_segments_path, data_dir, ffmpeg_path, ffprobe_path,
+    #                 num_workers, **ffmpeg_cfg)
+    # download_subset(unbalanced_train_segments_path, data_dir, ffmpeg_path, ffprobe_path,
+    #                 num_workers, **ffmpeg_cfg))
+```
+
+6. `(down_audioset) $ python download_audioset.py ./vggsound -e ./vggsound_ref.csv -n 8 -nr 2 -f $(which ffmpeg) -fp $(which ffprobe) -lp ./log.txt --verbose`
+- The script will download the dataset into `./vggsound` folder (mp4 videos and flac audios).
+- It will use 8 cores in parallel. Also checkout other arguments
+- I observed a trade-off between number of CPUs and how quickly the IP will be banned by YouTube
+- during the first pass I changed 3 IPs. Might be because I was using `-nr 10` and too many CPUs (8) or
+    because I started downloading vids from the same csv -> same videos were checked again -> too many requests
+- I found it useful to shuffle the csv data
+`cp vggsound_ref.csv vggsound_noshuf.csv && shuf -o vggsound_ref.csv vggsound_ref_notshuf.csv`
+Remember you have 3 lines on top of `vggsound_ref.csv` which are now suffled? Find them and put on top.
+- Don't run it from your local machine!
+- The authors of VGGsound used .wav audios but the script downloads .flac. You may want to adjust the script
+    such that it will do it automatically. You may also somehow skip audios and just extracted them later from
+    .mp4.
+- It will take some time. In my case it was one week, including reruns on shuffled data.
+
+</details> -->
+
+# Pretrained Models
+Unpack the pre-trained models to `./logs/` directory.
+
+## Codebooks
+| Trained on | Evaluated on | FID ↓ | Avg. MKL ↓ |                                                                                                                                                          Link / MD5SUM |
+| ---------: | -----------: | ----: | ---------: | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|   VGGSound |     VGGSound |   1.0 |        0.8 | [7ea229427297b5d220fb1c80db32dbc5](https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/models/2021-05-19T22-16-54_vggsound_codebook.tar.gz) |
+|        VAS |          VAS |   6.0 |        1.0 |      [0024ad3705c5e58a11779d3d9e97cc8a](https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/models/2021-06-06T19-42-53_vas_codebook.tar.gz) |
+
+Run [Sampling Tool](#sampling-tool) to see the reconstruction results for available data.
+
+## Transformers
+
+The setting **(a)**: the transformer is trained on *VGGSound* to sample from the *VGGSound* codebook:
+<!-- NOTE: Mind the streamlit visualization as well if more models will be released -->
+| Condition |     Features | FID ↓ | Avg. MKL ↓ | Sample Time️ ↓ |                                                                                                                                                             Link / MD5SUM |
+| --------: | -----------: | ----: | ---------: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|  No Feats |              |  13.5 |        9.7 |           7.7 | [b1f9bb63d831611479249031a1203371](https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/models/2021-06-20T16-35-20_vggsound_transformer.tar.gz) |
+|    1 Feat | BN Inception |   8.6 |        7.7 |           7.7 | [f2fe41dab17e232bd94c6d119a807fee](https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/models/2021-06-03T11-18-51_vggsound_transformer.tar.gz) |
+|    1 Feat |     ResNet50 | 11.5* |       7.3* |           7.7 | [27a61d4b74a72578d13579333ed056f6](https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/models/2021-07-30T21-03-22_vggsound_transformer.tar.gz) |
+|   5 Feats | BN Inception |   9.4 |        7.0 |           7.9 | [b082d894b741f0d7a1af9c2732bad70f](https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/models/2021-06-03T09-34-10_vggsound_transformer.tar.gz) |
+|   5 Feats |     ResNet50 | 11.3* |       7.0* |           7.9 | [f4d7105811589d441b69f00d7d0b8dc8](https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/models/2021-07-30T21-34-25_vggsound_transformer.tar.gz) |
+| 212 Feats | BN Inception |   9.6 |        6.8 |          11.8 | [79895ac08303b1536809cad1ec9a7502](https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/models/2021-06-03T07-27-58_vggsound_transformer.tar.gz) |
+| 212 Feats |     ResNet50 | 10.5* |       6.9* |          11.8 | [b222cc0e7aeb419f533d5806a08669fe](https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/models/2021-07-30T21-34-41_vggsound_transformer.tar.gz) |
+
+\* – calculated on 1 sampler per video the test set instead of 10 samples per video as the rest.
+Evaluating a model on a larger number of samples per video is an expensive procedure.
+When evaluative on 10 samples per video, one might expect that the values might improve a bit (~+0.1).
+
+The setting **(b)**: the transformer is trained on *VAS* to sample from the *VGGSound* codebook
+| Condition |     Features | FID ↓ | Avg. MKL ↓ | Sample Time️ ↓ |                                                                                                                                                        Link / MD5SUM |
+| --------: | -----------: | ----: | ---------: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|  No Feats |              |  33.7 |        9.6 |           7.7 | [e6b0b5be1f8ac551700f49d29cda50d7](https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/models/2021-06-20T16-34-36_vas_transformer.tar.gz) |
+|    1 Feat | BN Inception |  38.6 |        7.3 |           7.7 | [a98a124d6b3613923f28adfacba3890c](https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/models/2021-06-03T06-32-51_vas_transformer.tar.gz) |
+|    1 Feat |     ResNet50 | 26.5* |       6.7* |           7.7 | [37cd48f06d74176fa8d0f27303841d94](https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/models/2021-07-29T11-47-40_vas_transformer.tar.gz) |
+|   5 Feats | BN Inception |  29.1 |        6.9 |           7.9 | [38da002f900fb81275b73e158e919e16](https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/models/2021-06-03T05-51-34_vas_transformer.tar.gz) |
+|   5 Feats |     ResNet50 | 22.3* |       6.5* |           7.9 | [7b6951a33771ef527f1c1b1f99b7595e](https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/models/2021-07-29T11-36-00_vas_transformer.tar.gz) |
+| 212 Feats | BN Inception |  20.5 |        6.0 |          11.8 | [1c4e56077d737677eac524383e6d98d3](https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/models/2021-06-03T05-38-40_vas_transformer.tar.gz) |
+| 212 Feats |     ResNet50 | 20.8* |       6.2* |          11.8 | [6e553ea44c8bc7a3310961f74e7974ea](https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/models/2021-07-29T11-52-28_vas_transformer.tar.gz) |
+
+\* – calculated on 10 sampler per video the validation set instead of 100 samples per video as the rest.
+Evaluating a model on a larger number of samples per video is an expensive procedure.
+When evaluative on 10 samples per video, one might expect that the values might improve a bit (~+0.1).
+
+The setting **(c)**: the transformer is trained on *VAS* to sample from the *VAS* codebook
+| Condition |     Features | FID ↓ | Avg. MKL ↓ | Sample Time ↓ |                                                                                                                                                        Link / MD5SUM |
+| --------: | -----------: | ----: | ---------: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|  No Feats |              |  28.7 |        9.2 |           7.6 | [ea4945802094f826061483e7b9892839](https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/models/2021-06-20T16-24-38_vas_transformer.tar.gz) |
+|    1 Feat | BN Inception |  25.1 |        6.6 |           7.6 | [8a3adf60baa049a79ae62e2e95014ff7](https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/models/2021-06-09T13-31-37_vas_transformer.tar.gz) |
+|    1 Feat |     ResNet50 | 25.1* |       6.3* |           7.6 | [a7a1342030653945e97f68a8112ed54a](https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/models/2021-07-29T14-59-49_vas_transformer.tar.gz) |
+|   5 Feats | BN Inception |  24.8 |        6.2 |           7.8 | [4e1b24207780eff26a387dd9317d054d](https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/models/2021-06-09T14-14-24_vas_transformer.tar.gz) |
+|   5 Feats |     ResNet50 | 20.9* |       6.1* |           7.8 | [78b8d42be19dd1b0a346b1f512967302](https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/models/2021-07-29T14-51-25_vas_transformer.tar.gz) |
+| 212 Feats | BN Inception |  25.4 |        5.9 |          11.6 | [4542632b3c5bfbf827ea7868cedd4634](https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/models/2021-06-09T15-17-18_vas_transformer.tar.gz) |
+| 212 Feats |     ResNet50 | 22.6* |       5.8* |          11.6 | [dc2b5cbd28ad98d2f9ca4329e8aa0f64](https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/models/2021-07-29T13-34-39_vas_transformer.tar.gz) |
+
+\* – calculated on 10 sampler per video the validation set instead of 100 samples per video as the rest.
+Evaluating a model on a larger number of samples per video is an expensive procedure.
+When evaluative on 10 samples per video, one might expect that the values might improve a bit (~+0.1).
+
+A transformer can also be trained to generate a spectrogram given a specific **class**.
+We also provide pre-trained models for all three settings:
+The setting **(c)**: the transformer is trained on *VAS* to sample from the *VAS* codebook
+| Setting | Codebook | Sampling for | FID ↓ | Avg. MKL ↓ | Sample Time ↓ |                                                                                                                                                             Link / MD5SUM |
+| ------: | -------: | -----------: | ----: | ---------: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|     (a) | VGGSound |     VGGSound |   7.8 |        5.0 |           7.7 | [98a3788ab973f1c3cc02e2e41ad253bc](https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/models/2021-06-03T00-43-28_vggsound_transformer.tar.gz) |
+|     (b) | VGGSound |          VAS |  39.6 |        6.7 |           7.7 |      [16a816a270f09a76bfd97fe0006c704b](https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/models/2021-06-08T14-41-19_vas_transformer.tar.gz) |
+|     (c) |      VAS |          VAS |  23.9 |        5.5 |           7.6 |      [412b01be179c2b8b02dfa0c0b49b9a0f](https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/models/2021-06-09T09-42-07_vas_transformer.tar.gz) |
+
+## VGGish-ish, Melception, and MelGAN
+
+These will be downloaded automatically during the first run.
+However, if you need them separately, here are the checkpoints
+- [VGGish-ish](https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/vggishish16.pt) (1.54GB, `197040c524a07ccacf7715d7080a80bd`) + Normalization Parameters (in `/specvqgan/modules/losses/vggishish/data/`)
+- [Melception](https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/melception-21-05-10T09-28-40.pt) (0.27GB, `a71a41041e945b457c7d3d814bbcf72d`) + Normalization Parameters (in `/specvqgan/modules/losses/vggishish/data/`)
+- [MelGAN](./vocoder/logs/vggsound)
+
+The reference performance of VGGish-ish and Melception:
+| Model      | Top-1 Acc | Top-5 Acc | mAP   | mAUC  |
+| ---------- | --------- | --------- | ----- | ----- |
+| VGGish-ish | 34.70     | 63.71     | 36.63 | 95.70 |
+| Melception | 44.49     | 73.79     | 47.58 | 96.66 |
+
+Run [Sampling Tool](#sampling-tool) to see Melception and MelGAN in action.
+
+# Training
+The training is done in **two** stages.
+First, a **spectrogram codebook** should be trained.
+Second, a **transformer** is trained to sample from the codebook
+The first and the second stages can be trained on the same or separate datasets as long as the process of spectrogram extraction is the same.
+
+## Training a Spectrogram Codebook
+To train a spectrogram codebook, we tried two datasets: VAS and VGGSound.
+We run our experiments on a relatively expensive hardware setup with four _40GB NVidia A100_ but the models
+can also be trained on one _12GB NVidia 2080Ti_ with smaller batch size.
+When training on four _40GB NVidia A100_, change arguments to `--gpus 0,1,2,3` and
+`data.params.batch_size=8` for the codebook and `=16` for the transformer.
+The training will hang a bit at `0, 2, 4, 8, ...` steps because of the logging.
+If folders with features and spectrograms are located elsewhere, the paths can be specified in
+`data.params.spec_dir_path`, `data.params.rgb_feats_dir_path`, and `data.params.flow_feats_dir_path`
+arguments but use the same format as in the config file e.g. notice the `*`
+in the path which globs class folders.
+
+```bash
+# VAS Codebook
+# mind the comma after `0,`
+python train.py --base configs/vas_codebook.yaml -t True --gpus 0,
+# or
+# VGGSound codebook
+python train.py --base configs/vggsound_codebook.yaml -t True --gpus 0,
+```
+
+## Training a Transformer
+A transformer (GPT-2) is trained to sample from the spectrogram codebook given a set of frame-level visual features.
+
+### VAS Transformer
+
+```bash
+# with the VAS codebook
+python train.py --base configs/vas_transformer.yaml -t True --gpus 0, \
+    model.params.first_stage_config.params.ckpt_path=./logs/2021-06-06T19-42-53_vas_codebook/checkpoints/epoch_259.ckpt
+# or with the VGGSound codebook which has 1024 codes
+python train.py --base configs/vas_transformer.yaml -t True --gpus 0, \
+    model.params.transformer_config.params.GPT_config.vocab_size=1024 \
+    model.params.first_stage_config.params.n_embed=1024 \
+    model.params.first_stage_config.params.ckpt_path=./logs/2021-05-19T22-16-54_vggsound_codebook/checkpoints/epoch_39.ckpt
+```
+
+### VGGSound Transformer
+
+```bash
+python train.py --base configs/vggsound_transformer.yaml -t True --gpus 0, \
+    model.params.first_stage_config.params.ckpt_path=./logs/2021-05-19T22-16-54_vggsound_codebook/checkpoints/epoch_39.ckpt
+```
+
+### Controlling the Condition Size
+The size of the visual condition is controlled by two arguments in the config file.
+The `feat_sample_size` is the size of the visual features resampled equidistantly from all available features (212) and `block_size` is the attention span.
+Make sure to use `block_size = 53 * 5 + feat_sample_size`.
+For instance, for `feat_sample_size=212` the `block_size=477`.
+However, the longer the condition, the more memory and more timely the sampling.
+By default, the configs are using `feat_sample_size=212` for VAS and `5` for VGGSound.
+Feel free to tweak it to your liking/application for example:
+```bash
+python train.py --base configs/vas_transformer.yaml -t True --gpus 0, \
+    model.params.transformer_config.params.GPT_config.block_size=318 \
+    data.params.feat_sampler_cfg.params.feat_sample_size=53 \
+    model.params.first_stage_config.params.ckpt_path=./logs/2021-06-06T19-42-53_vas_codebook/checkpoints/epoch_259.ckpt
+```
+The `No Feats` settings (without visual condition) are trained similarly to the settings with visual conditioning where the condition is replaced with random vectors.
+The optimal approach here is to use `replace_feats_with_random=true` along with `feat_sample_size=1` for example (VAS):
+```bash
+python train.py --base configs/vas_transformer.yaml -t True --gpus 0, \
+    data.params.replace_feats_with_random=true \
+    model.params.transformer_config.params.GPT_config.block_size=266 \
+    data.params.feat_sampler_cfg.params.feat_sample_size=1 \
+    model.params.first_stage_config.params.ckpt_path=./logs/2021-06-06T19-42-53_vas_codebook/checkpoints/epoch_259.ckpt
+```
+
+## Training VGGish-ish and Melception
+We include all necessary files for training both `vggishish` and `melception` in `./specvqgan/modules/losses/vggishish`.
+Run it on a 12GB GPU as
+```bash
+cd ./specvqgan/modules/losses/vggishish
+# vggish-ish
+python train_vggishish.py config=./configs/vggish.yaml device='cuda:0'
+# melception
+python train_melception.py config=./configs/melception.yaml device='cuda:1'
+```
+
+## Training MelGAN
+To train the vocoder, use this command:
+```bash
+cd ./vocoder
+python scripts/train.py \
+    --save_path ./logs/`date +"%Y-%m-%dT%H-%M-%S"` \
+    --data_path /path/to/melspec_10s_22050hz \
+    --batch_size 64
+```
+
+# Evaluation
+The evaluation is done in two steps.
+First, the samples are generated for each video. Second, evaluation script is run.
+The sampling procedure supports multi-gpu multi-node parallization.
+We provide a multi-gpu command which can easily be applied on a multi-node setup by replacing `--master_addr` to your main machine and `--node_rank` for every worker's id (also see an `sbatch` script in `./evaluation/sbatch_sample.sh` if you have a SLURM cluster at your disposal):
+```bash
+# Sample
+python -m torch.distributed.launch \
+    --nproc_per_node=3 \
+    --nnodes=1 \
+    --node_rank=0 \
+    --master_addr=localhost \
+    --master_port=62374 \
+    --use_env \
+        evaluation/generate_samples.py \
+        sampler.config_sampler=evaluation/configs/sampler.yaml \
+        sampler.model_logdir=$EXPERIMENT_PATH \
+        sampler.splits=$SPLITS \
+        sampler.samples_per_video=$SAMPLES_PER_VIDEO \
+        sampler.batch_size=$SAMPLER_BATCHSIZE \
+        sampler.top_k=$TOP_K \
+        data.params.spec_dir_path=$SPEC_DIR_PATH \
+        data.params.rgb_feats_dir_path=$RGB_FEATS_DIR_PATH \
+        data.params.flow_feats_dir_path=$FLOW_FEATS_DIR_PATH \
+        sampler.now=$NOW
+# Evaluate
+python -m torch.distributed.launch \
+    --nproc_per_node=3 \
+    --nnodes=1 \
+    --node_rank=0 \
+    --master_addr=localhost \
+    --master_port=62374 \
+    --use_env \
+    evaluate.py \
+        config=./evaluation/configs/eval_melception_${DATASET,,}.yaml \
+        input2.path_to_exp=$EXPERIMENT_PATH \
+        patch.specs_dir=$SPEC_DIR_PATH \
+        patch.spec_dir_path=$SPEC_DIR_PATH \
+        patch.rgb_feats_dir_path=$RGB_FEATS_DIR_PATH \
+        patch.flow_feats_dir_path=$FLOW_FEATS_DIR_PATH \
+        input1.params.root=$EXPERIMENT_PATH/samples_$NOW/$SAMPLES_FOLDER
+```
+The variables for the **VAS** dataset:
+```bash
+EXPERIMENT_PATH="./logs/<folder-name-of-vas-transformer-or-codebook>"
+SPEC_DIR_PATH="./data/vas/features/*/melspec_10s_22050hz/"
+RGB_FEATS_DIR_PATH="./data/vas/features/*/feature_rgb_bninception_dim1024_21.5fps/"
+FLOW_FEATS_DIR_PATH="./data/vas/features/*/feature_flow_bninception_dim1024_21.5fps/"
+SAMPLES_FOLDER="VAS_validation"
+SPLITS="\"[validation, ]\""
+SAMPLER_BATCHSIZE=4
+SAMPLES_PER_VIDEO=10
+TOP_K=64 # use TOP_K=512 when evaluating a VAS transformer trained with a VGGSound codebook
+NOW=`date +"%Y-%m-%dT%H-%M-%S"`
+```
+The variables for the **VGGSound** dataset:
+```bash
+EXPERIMENT_PATH="./logs/<folder-name-of-vggsound-transformer-or-codebook>"
+SPEC_DIR_PATH="./data/vggsound/melspec_10s_22050hz/"
+RGB_FEATS_DIR_PATH="./data/vggsound/feature_rgb_bninception_dim1024_21.5fps/"
+FLOW_FEATS_DIR_PATH="./data/vggsound/feature_flow_bninception_dim1024_21.5fps/"
+SAMPLES_FOLDER="VGGSound_test"
+SPLITS="\"[test, ]\""
+SAMPLER_BATCHSIZE=32
+SAMPLES_PER_VIDEO=1
+TOP_K=512
+NOW=`date +"%Y-%m-%dT%H-%M-%S" the`
+```
+
+# Sampling Tool
+For interactive sampling, we rely on the [Streamlit](https://streamlit.io/) library.
+To start the streamlit server locally, run
+```bash
+# mind the trailing `--`
+streamlit run --server.port 5555 ./sample_visualization.py --
+# go to `localhost:5555` in your browser
+```
+
+A Google Colab demo is coming soon
+
+# The Neural Audio Codec Demo
+A recent [ArXiv submission](https://arxiv.org/abs/2107.03312) show-cased a VQVAE architecture with adversarial loss,
+called SoundStream, on lossy compression of a waveform with the state-of-the-art results on the 3 kbps
+bitrate which works on music and speech datasets. Since our approach includes sampling from a pre-trained
+codebook, we can employ our Spectrogram VQGAN pre-trained on an open-domain dataset as a neural audio codec without a change.
+
+A Google Colab demo is coming soon
+
+# Citation
+Our paper was accepted as an oral presentation for the BMVC 2021.
+Please, use this bibtex if you would like to cite our work
+```
+@InProceedings{SpecVQGAN_Iashin_2021,
+  title={Taming Visually Guided Sound Generation},
+  author={Iashin, Vladimir and Rahtu, Esa},
+  booktitle={British Machine Vision Conference (BMVC)},
+  year={2021}
+}
+```
+
+# Acknowledgments
+Funding for this research was provided by the Academy of Finland projects 327910 & 324346. The authors acknowledge CSC — IT Center for Science, Finland, for computational resources for our experimentation.
+
+We also acknowledge the following codebases:
+- The code base is built upon an amazing [taming-transformers](https://github.com/CompVis/taming-transformers) repo.
+Check it out if you are into high-res image generation.
+- The implementation of some evaluation metrics is partially borrowed and adapted from [torch-fidelity](https://github.com/toshas/torch-fidelity).
+- The feature extraction pipeline relies on the baseline implementation [RegNet](https://github.com/PeihaoChen/regnet).
+- MelGAN training scripts are built upon the [official implementation for text-to-speech MelGAN](https://github.com/descriptinc/melgan-neurips).
diff --git a/SpecVQGAN_Demo.ipynb b/SpecVQGAN_Demo.ipynb
new file mode 100644
index 0000000..89a6d45
--- /dev/null
+++ b/SpecVQGAN_Demo.ipynb
@@ -0,0 +1,551 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Taming Visually Guided Sound Generation 🖼️ 👉 🔉\n",
+    "This notebook will guide you through the visually-guided sound generation.\n",
+    "We will condition the generation on a set of visual frames extracted from \n",
+    "an arbitrary video.\n",
+    "\n",
+    "[Project Page](https://v-iashin.github.io/SpecVQGAN) • [Code](https://github.com/v-iashin/SpecVQGAN)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    import google.colab\n",
+    "    IN_COLAB = True\n",
+    "except:\n",
+    "    IN_COLAB = False\n",
+    "\n",
+    "if IN_COLAB:\n",
+    "    # Cloning the repo from GitHub\n",
+    "    !git clone https: // github.com/v-iashin/SpecVQGAN\n",
+    "    print('Some packages are not installed. Installing...')\n",
+    "    # Installing the environment\n",
+    "    !pip uninstall torchtext - y  # otherwise fails on PytorchLightning import\n",
+    "    !pip install pytorch-lightning == 1.2.10 omegaconf == 2.0.6 streamlit == 0.80 matplotlib == 3.4.1 albumentations == 0.5.2\n",
+    "    # We need to restart Colab Runtime because we installed new packages\n",
+    "    !for i in {1..20} do echo \"Packages have been installed. Please rerun the cell.\" done\n",
+    "    import os\n",
+    "    os.kill(os.getpid(), 9)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Imports and Device Selection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import time\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import IPython.display as display_audio\n",
+    "import soundfile\n",
+    "import torch\n",
+    "from IPython import display\n",
+    "from matplotlib import pyplot as plt\n",
+    "from torch.utils.data.dataloader import default_collate\n",
+    "from torchvision.utils import make_grid\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "from feature_extraction.demo_utils import (ExtractResNet50,\n",
+    "                                           extract_melspectrogram, load_model,\n",
+    "                                           show_grid, trim_video)\n",
+    "from sample_visualization import (all_attention_to_st, get_class_preditions,\n",
+    "                                  last_attention_to_st, spec_to_audio_to_st,\n",
+    "                                  tensor_to_plt)\n",
+    "from specvqgan.data.vggsound import CropImage\n",
+    "\n",
+    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Select a Model\n",
+    "The model will be automatically downloaded given the `Model Name`. \n",
+    "Use the following reference to select one:\n",
+    "\n",
+    "|                               Model Name |                  Info | FID ↓ | Avg. MKL ↓ | Sample Time️ ↓ |\n",
+    "| ---------------------------------------: | --------------------: | ----: | ---------: | ------------: |\n",
+    "| 2021-06-20T16-35-20_vggsound_transformer |              No Feats |  13.5 |        9.7 |           7.7 |\n",
+    "| 2021-07-30T21-03-22_vggsound_transformer |    1 ResNet50 Feature |  11.5 |        7.3 |           7.7 |\n",
+    "| 2021-07-30T21-34-25_vggsound_transformer |   5 ResNet50 Features |  11.3 |        7.0 |           7.9 |\n",
+    "| 2021-07-30T21-34-41_vggsound_transformer | 212 ResNet50 Features |  10.5 |        6.9 |          11.8 |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using: 2021-07-30T21-34-25_vggsound_transformer (5 ResNet50 Features)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2021-09-05 15:35:01.927 WARNING root: \n",
+      "  \u001b[33m\u001b[1mWarning:\u001b[0m to view this Streamlit app on a browser, run it with the following\n",
+      "  command:\n",
+      "\n",
+      "    streamlit run ipykernel_launcher [ARGUMENTS]\n",
+      "2021-09-05 15:35:05.667 INFO    specvqgan.modules.transformer.mingpt: number of parameters: 3.046851e+08\n",
+      "2021-09-05 15:35:09.801 INFO    main.specvqgan.modules.losses.vggishish.transforms: Assuming that the input stats are calculated using preprocessed spectrograms (log)\n",
+      "2021-09-05 15:35:09.802 INFO    main.specvqgan.modules.losses.vggishish.transforms: Trying to load train stats for Standard Normalization of inputs\n"
+     ]
+    }
+   ],
+   "source": [
+    "model_name = '2021-07-30T21-34-25_vggsound_transformer'\n",
+    "log_dir = './logs'\n",
+    "config, sampler, melgan, melception = load_model(model_name, log_dir, device)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Select a Video\n",
+    "We extract visual features and display the corresponding frames.\n",
+    "\n",
+    "**Note**: the selected video is trimmed to 10 seconds.\n",
+    "By default, we use the first 10 seconds: adjust `start_sec` if you'd like to \n",
+    "start from another time.\n",
+    "If the video is shorter than 10 sec it will be tiled until 10 sec."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Video Duration: 10.024\n",
+      "Trimmed the input video ./data/vggsound/video/-Qowmc0P9ic_34000_44000.mp4 and saved the output @ ./tmp/-Qowmc0P9ic_34000_44000_trim_to_10s.mp4\n",
+      "Raw Extracted Representation: (215, 2048)\n",
+      "Post-processed Representation: (5, 2048)\n",
+      "Rendering the Plot with Frames Used in Conditioning\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x1800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Select a video\n",
+    "video_path = './data/vggsound/video/-Qowmc0P9ic_34000_44000.mp4'\n",
+    "\n",
+    "# Trim the video\n",
+    "start_sec = 0  # to start with 01:35 use 95 seconds\n",
+    "video_path = trim_video(video_path, start_sec, trim_duration=10)\n",
+    "\n",
+    "# Extract Features\n",
+    "extraction_fps = 21.5\n",
+    "feature_extractor = ExtractResNet50(extraction_fps, config.data.params, device)\n",
+    "visual_features, resampled_frames = feature_extractor(video_path)\n",
+    "\n",
+    "# Show the selected frames to extract features for\n",
+    "if not config.data.params.replace_feats_with_random:\n",
+    "    fig = show_grid(make_grid(resampled_frames))\n",
+    "    fig.show()\n",
+    "\n",
+    "# Prepare Input\n",
+    "batch = default_collate([visual_features])\n",
+    "batch['feature'] = batch['feature'].to(device)\n",
+    "c = sampler.get_input(sampler.cond_stage_key, batch)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Codebook Reconstruction of the Input Spectrogram\n",
+    "\n",
+    "If the video has an audio, it will extract mel-spectrogram from the \n",
+    "audio track using the same pre-processing pipeline as in our experiments.\n",
+    "This is also useful if you plan to prime the sampling with a half of \n",
+    "the ground truth codes.\n",
+    "\n",
+    "This cell can be ignored if the video doesn't have any audio."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "using melspec_10s_22050hz\n",
+      "Original Spectrogram:\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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IvjB5AA0jZv0ZOw7OOvK8GGQul1OtVjti1pJsrXgWWDHPS6Bhftrttq0h68kck4UBDGRVgClZoSdZjAEQxS4kHYGrt1f/h3UG8MgMkJKYU2wBuYgAzLz7OieOi6qBVBRFkdkIYIrv+eAEoMKIkb4Afuw2n8+rXC5rvV7bHPiMslarHRGxQqFgwQ/gohZKCcJnjcvl0ogA6wpGkHkSEHkm5p6aTqvVMvnV+y9Avd1uVavVtFqtjPlTcgBQsTHG78ewXC4lPRKOer1+5JvgVRAEJlHX63WTC30AZU0gnKVSSY1GQ9lsVtPp1IIYz+EVMPwV3yBTr9VqRyUbgL/ZbNpnl8ul+W0QBCaxYot8Hqwk48X2kVUhDYyFdQVvWVsyda8isW7gFrbu/Wmz2RhGIVN7kgdmELyp20LIkW0Jgvi5V8SeBvqvu94a+AAc/p//4rAYAxIY4IMhkWJjcNVq9SiS44BeesEAcSiyl2w2q2KxaEyBGg4ThTPAMpCFfPbJMyBXsEDcC4mNSfeBiAyOoFssFi1D436e/RBAWTxkOuaQgOuzAc/YJB3NMd+FMQFczBUMnawAqQuDAZCr1eph4X+VDdBEQmbrv7dcLms+n5theUAkuGEPkA6CBI7lCQCs2tcSyPyoZfAd3gFYS+Qav5aMhXmDOTPOfD6vUqlkz4HkCkACGEhKMMjlcmn2AAgivxAs/JozLmyZ9YB0+NqJdCBGSFhkH94+CXgEhmKxeCSzMrYoijSdTlUoFI6kcsbqAcQ3i3hmjUpDEECW81kV9/XM2j87857L5ey5MpmMyuWygTm+0mg0jnzX1+lRf/yzk/Ei4RUKBZsHSJifawI1fsZ9wQ6am3a7naktyKReosQvDCx/9Rz4MNgk6QjDCA6dTsd+12eKrVbLmqLAmNVqZYGQy2f14DBk3Psdduj7IQh6BJf1em2kE9sBn1CtkiQxBcDbssck7B/MJVBC2giyjBnMoh7IvPueBeYbVW2z2RjeoN5g+8QLSkw+wSFbpeT0tuutgY9Aw8MyQAATgEI6CILACtQYA0CIEUuP6ffTDNE7Dz//uo5R6kjlctnuR8aHAz8FK4Icz8QY+X7GtN/vjZUBxAACC82F43oApMZAtsHzAxY4qvRY8PXyLgEQZ/TPDTgAOsw1P+P3ff3HZ0+73c46zbyUIcnkL4wOMKIeA0gSSHBkHM83oTAfPAOOTyDm4mc4CGToqWRDJosNkcU8VRRYAz7rM0gvy8FgqQX5OhT1VsbC2LkvQXM2mxk58LK/l9GxMxQSxsf4WXvkXl+39ZIW6+HXlTkhy8NfCbBkLgAS2ScE0dd9M5mMARNAg22hBOBj2AKghF0xL5PJxAgNc7Ddbs0nvN/wWS4Ix2azUaFQ0GAwUKlUsqzL24Rfd29D1OyQlFlbQJGMmTUgk8THCHhgkVehvATra8g09LH2Xsqbz+c2LnwVqRg5k2BDHY+5xqbImllX5hX7ZrzYAevE+KXHumi5XFa1Wj1Sa2imw06xM9/r4Amd75XAz1gPlCeCKCoYts89wefNZmP3R2qmPjudTrVarSzQEmB5ZvALYsc8MIa3XW8NfBg3UoZn7RivD46wF18M5mEJfmQ+SZKoWCyajs2/h2FomjcFfdheHD9uUwBUnhb5CSoYOo7A/yN7MUlsb8BACG6bzUadTscWhfodwIHxYmSAEMwaBor8QgCuVqvK5XIWXFlsnJLv851ZZCFeBvJNCjwf7KdYLGqxWBjYYhy+Cwtykc/nNZvNDOj5Hl83JGg9Db5+LX3zBw7omyI8OeJ7PCCnaWrt3TwjagJAT8cpwQgQJpPh3vl83lqnsUUcjKCEExF8ngZq7AlH8pkD84qsCgADDBALANmDFWvOeuFnBFA/z0iVECrm0XdfhmFoDQyssx8PpMtLa15lAfy4H/PusyVvX9SJIRR09hIEKpXKUUchtuQ7S6kzoxJ4dYDMm7kgSLHe2CyqBgGEjKFUKlkXtQd+n7lDcrLZrBF4ghPYgo342qHfvkUzH9t9yGSeYhHZqiTL6vAPn71TguDfCKhRFJlU6+eTBg8fbPm7Tx7I6Hy9FukVogVZJ1FBaYNcJ0li/sV/ST6YI7DQd+Gyrl6d82STYMdnWF8wiMSG9SObJdD6Bhv+H2Lhy0Nfd731p6T+OCaLhXHAzr2kyUQQLAlc3rEAegCFh8UpqYEARAROHtIDCs4OK2ZyYGU4Dc5CZsXCY4iADGOEsUmPDS75fN72L/rap5cWPCMlMPtMhPEyX9RzYJ6APPOEcTJu3zmLw7Iu3Jvf85kDwXQymajRaNjcY5CsISDCWL1k5LNkwJWOWG8HrM9TmQRAYZ59s4p3asDJkwZvyJ5kkEnD3nFushsyWF9XRuLh70EQmMxJFkfGg13CwnkO/t2DvJeWsCcv9fJZ39jim0mwX+mxS3A4HJriwtxB/vgO/zMadlgHLxsDZjyzbwgCIAl6vuGB9ZB0FEiRqfEN1pomLDI4iCWEMIoiW3/Ajnnj2SHddNxiH5PJxPwP4AUzfJMPdk0glnSEL4yB74Pc0cWIv0G+wRVfokFN8Q1KnrhBCL3Cwhzye2R7EDGCo6+DdzodTadTI948iydknlBCDnzm7TNIxgGB8tk8tWXssVarWVDGliER/Beb8o1LXi5lPVHcwBivdpGx+zKOJLMtMALyDsagOvBz1tjf4+uutwY+mjdgW37iPHvCcAAqHB2mhGNRkPR7pChYMxkMmuD5tM6BE/jgA1ABjuz/Q7rx0g7gjiMCCt6wc7mcZrOZVquVTarXlan/+DSe2gdGw9iZM2qPft8JjIznx/j4PjJhntnXCbkHAd9LSQQUSVZz4XlxRM+c2NhLmzKAwx5B1shvEeH+HiR8LREG6DfXMlewRv9s/IxgwFpgaxAnLwkDmMydr1/5jIuxsHeI9WV+IAnYAHULnMh30yZJYrYGCYFg+MzHgzKsGtUCVQSG7SW1xWKhbDar2Wxmvudrb9SJPLkhuAPEKBaAAZmVdGhWqVarGo1G5oPcj/Fj4/v94ZADZFiCGWA2nU7N7/ErQG88HltWwjwwvwQ/7kdQYD4k2bPf398fybeMAyD3tean8l4cHzprS6WSAStjxbd9ScKXaVgjfBAfqlarJhuCDfzhsxBV7JA9svgBQRtf8vbNHOBLfJZ7Y1/UvSB4zAl4u1gsbOsGAQb/Za3xSTJ35tATEl+H9eQNXPVKCXhLbPCE1CuF+IvP8mazmcUSX1/n2XzGzt8hn/gMmOnH+k3XWwNfFEWmX/tMA50Yo/fBAYOjiEyQ4mE9Q9hsNtblSQfW0/ZyDP1pcPFO62UsSXY6gGdFGDeLz/czWQRYggtdVr5zjrGx6IwdMJIedXF+jwXyWZ3PaskKwzA82ufoC70wNLpsqUMBonzGAxhA7RsUkEAwbsgJcpVn5Ug7XPv9oWUb2YU2f5oGmGPp8SQd5pV/o4juwYbPeSejXvJ1NTFJlhl6xg4pgu1Sw/M1ULIT7JWfEfQ9MMNQsS3mioMcvKyP7fFZ5CRYMz+jvgup8ADDdg1+lxoi0q6v57B2ZAWsH35D8NvtdkcAzvjm87nJv8wnc/KUxftn8pIdRM7XdziIAvJBrQ67AKDIKgF7/JHMmiDFHmFfw8KfmTdAkkwfW+12u/bvzAv4Q/MDa889fKcpz+sPUcA+PFknIPpxSbL6IrbEMzKn/hn8/HuVhnFjC/gBdgku+Voa4/X7FCHxXp5G7iQJwU5oAgNXfSIjHU6TknSE008JLHYhyZQ2nhsfrtVqlsU/xWriBdk03++za0jpeDw2vIDUMu63XW8NfF6Si6LDfqnRaKT7+3tbDAbrW3N9zQqHAIx8loNhYVCTyeSoUCnJmDoZF4CJgXtWjfPwbywQnyFF9oblDRAnBIB8YIU9Yqiz2czSejKsIAg0Go1sUXAez26KxaKl675gTZAAFFarlbFE71RIrRgCTogzBEGgdrttbJygKsnYO3vufH0UJ+K/3M8bEIw4lzsclzQajY5kOoIr8w/z4jmRzBkzBo8DAdqerHig4DmxBS9veBDxNWifIdBhhr2RdZARAbpe9nx6+doJwAtI+3tju9Lj3rY4jnV5ealqtWq2y6kcko662djnyhFZPFOtVjM/AtC8BMYc+qDuAd3Lsr5Gjo8ALD6wQaImk4kBGicQEVBns5mBFDYcx7GdsIE/8N08j5dsWV/WFBLqJWTfKc2cUgvDfmlgieNYi8XiSA0hU4Sos26egFErJAjhB9ybOWeTNsGV70HVwO84/o81qtVqtt3Ay3P4uicDrJP0WCoAm6TH7TZegZFkahjNQmSHktRqtexZ6XjG7p+qOf6z4I5XLNL0UJ8Ex1CMCLoEK19a8WPhs9jnU/LLGlK3ZT5Ya++3kBGPHd90vTXwVatVSz19TYzMz6fBnh1TsPSO/LQwyaKxi5+H93UZX0fxIAI7o7GGfydTI2gAwL6QSoPLdDq1QDqfz482UwPkLD4Tzd+91ODBD+fA+DBiH6Bevnxpi+yZMcYPsPhUHUBHRuJYnvl8bswOEGG8zDWZo/S4FcXXFwnGgIckM1xAykutngTlcjmTQsmekWtYF5wFCaTf7x9tH/Fg5turcXxPTMhwybAhEP60F99MAmBLj1kimYYPAE+PgvKfZY7I2llfZFO+jwyTdebeXgHY7x/rll6e9i3ovlnGS8y+JsK80gXopVie1Wfv1C1hzjSVoTLgL9gN/sVeOB/k/fi4j691Mh9gAOeoenKCDwdBYJIqAB7HsR2jRxbP2CFxZDdkE/V63TJongv/8iQTLEKt4Yg11ou58lkpGORro7VazWR134uAnfBM+GaxWLQTWqRDAMMe8X+enRODqC3yhyCDX1C79HbGGAhe+DFkhPWn/sfzgFXYKMQB++QZkR2ZRwKab97yxJfx0kTEuHxmTqOY92O+h0wcX8D2wGDmheMK8SmSnLddbw18fn8WkgsLzmTzsDBtb8g8HMbZbDbt2CBYdbVatXZd9ljBdvl/QIfvBhx955bvcvSMHkfI5XJ2PqDPMvkcrAH5CBDwm+UJOgQJX7sEbLzk4BsvCD7dbtfOxoOVEpSn06ktHjKpfw7P3D2bkh43erNuODOBh+CDzMNz+jVDMsPpGLsnGpVK5eg0GJwJEEAlAKCeNviQIcO8YWg4IWvBH8/kuHydEefz4EMrO12mdNxRa6PZIAxDTafTI6dlPGT3fn8e8yw9Hqbgi+nUm3F4Pu/rFmTsZGk4saSjbt9ms2mEp9VqSdKRPArRo5sW0umDBSdYrFYrO7mk0+lYxggA+WYpgAaS5BsZXrx4cZR1kKkC7ARH1gAbwdeYA4AY0MR2pEPQpg6NTzHH2CZrgX9C/Fhj/t2TNu8vUXQ4PpHGskKhYBIfvow9+YMEmI9ms2lBB3JEExBYA5h3Oh2zYWqF1B2n06nNLeMCv5AHs9msKpWKba3iWf0RgWTXECeCtm+WYv589zNZOjgtyfZkQ7SYT9QM4gH4S5c8n8cvIDleacCfeV5favKb0X1NkNjDZ8BoSCylJKRXr3y87Xp7z6ceTxjgQWDb3oBhKywYASGXy9lBwtJjtsbiMlnUvPh/5DB0aRyCB0Oy8xIWi8oZkhi/Z1Y4JU7v5Sw+46UE36xBfY3aEL+DsTEvfA6GAotjrNPp9Eg+xZB9il4oFI6aa5gzMjiOOfP7l6hBEiz5XemxGMx6Id+kafq12yoAKxodvHQL4FCvxDn9XPgW8SR5bBbZ7/caj8cGOqwHmaOvU2Efk8nEQAU5RpLVMnAmLx2TpcBMIWZeevbNBFy+lsjbAer1uqkZfKf0uFXDS3VPJUUyZC8FkwXBsL0qgf2xRtg0vuBJCff3NT06bOm6JCPEHgD1p4d/A0LYGQCEzfHsyP1syWGuZ7OZARJjoanIk55SqWQYwJywNl5RAfR6vZ6KxeLR0VfMG9shvE1g+wRt1ASIJXOVzR7eNsL3kql7VcsrCJAg3yjD3EDwsHvmmwYxngkS6k+VYT3wszQ9vEmERg+es91uW20d/4M08lx0v2KLSJ0QF+yFY/g4yMJLz+AgmW8QPB54wTg5LYb5wZZ8Zuo7mX1W7I8R5LuTJDF7AmOwN0gcfuZrrJ6QevuF/BA3vul6609ZzMlkYiDN4Eip6V4kU/GZHoOO49hOskfv3m63ZkSwEGoFZIy1Ws0A0dfXSJ9hArB2DAPQhPH6vVF8jvocTgLbJlvz0gxyF8boZTIcbTKZWAYFi8EhCYYAaj6fN4kJ4CFDY15wZM77Yz38qRt0CWIA3iE5KYe3D8D+CNIEHhgVspMnEWSCPIM/lPlplxUdaN6JuRcF+FKppIuLC7MTf3qFl1apJ3Q6HbMHX1NBCiHIeMIC++YtEdgq3cQEWoiTPwmEedtsDoduz2YzG0sUReb04/HYnBzgj+NYnU7n6NBtgozP6svl8tFxVE9rOF8XWJGjPUgiMfrmJwhOqVRSr9fTdDq1rAkS5zMmmDLAhG2hxmDPHIzMPPsajz8lg7XGFr0y4DPJarVq/oy94kdpmlpdfjgcGhiTrfu+g9VqZcrKfn9ozKCm6c8KBWSflin8cVr+KDHmHOLCf1Gr5vO5nTvMHLBtg2dk7allsTbc39s2/huG4VHghFg8PDwc1d1IHMBfyDPfxRxiJ35dIS8Q9acKDEcYQhg5pJ7gMh6PTbXg+X25icApPR73xtjopEZp8XV4SJwnCKwDCZDPZlkfSBH4RoLjCe3XXW8NfP50B5gowEQ0JgXGiWFm3mlhYrARshRONcD5fFHUsy+CEQ8Hs/aFbiK/T+0JjIyP92wxabvdzpxL0lGmwDg5uYIFnEwmBoYsGuwVQ4Yc0LzA6RO0NpdKJc1mM2N94/H4qDmEYEht1e+twegrlYrNyXA4tIVmDr6O/c7nc5OGKOBPJhMLWGT3BAaMnSBDUAEACeSAL6CNRO5B23d3IZWQkRNoAQ6CD01N+/3hmCfAGENHikJx8M0IntwEQWDvOATgsIHVamXEAukOpu+zGjJoZFSCSRzHJg8BNr7rr1Qq2eHovoMNAIfJEhxQSHxDFeOk2cnX1Lz8g5Td7XZtDTgOjoz0/PzcSCrZsicMyE/+KDAIECoDB42PRiObVzCCt3KwhpAabJZxQoYIll5mxq6Z/3w+r1qtdhQkWSe6in326ueIN2EQGPEP/7v8IeDiu4Ctb6DCp2hyIcgRSFk75hwSyIZ31oGAhT/gZ8jmBCUIJvPDXEIad7vDaUx0Z3of9L0GPuPHViG+XnWaz+cql8uGZWAPZAYfYh0hOARbSdaPIMl8hX/HjiHmlHEYEzVRgp2fN+YJEiU9ni7ma+ueDH7T9Wu7OgEJjIWghJES7AhaAJkkAzdqG0guPChM128sJ+OBgRPBn46Hh8MweXCvK2O40+lUaZra63Bg/j6oAEqwDRaBoAlrxxj8PBAA6vW6ad4YAkZLbcm/hob71ev1o1M6mG90/NFoZACGo/uCNSfQ73Y7VSoVNRoNu5/0+KYKfyq7GUD28VBmzq38ugzJMzOfQTMXGD2ZF+vi92wSrDebjZEeAiTZr2/EKRaL1q7sN+QS8LABHIN6sC/2Mwbp8WQVAI/n83VJScbsqalhy9TvkJb8MWOwW9aEQO0lZxgvwYo18uPi96mP8iJOnNt3MzJ2H8Amk4n53Gq10nQ6tfVA/nzx4oUBP1KfJ6DITz4rpV5JRkCmh6+s14e3CIzHY3sPHWNBTeDwBOwa3+D+njRi35BR/Bs74c0jyHeQYDoUyci8Hfh6Lnjia1H+1Clq7tPp9KhzmT9kZex1xWe9QgBpIVu/vb3VbDZTt9u1d4+i3DAX4/HYAgqYCK7wzkJKFJlMxjaYg3nMVxRFarVa2mw2FqCp1flyD+QT2Rg1gmacKIoMPyeTib0zD5tljvgcdoJKRoD1OA0RgcjxX/wfEgEpAh+8AsccS4/d5hBiFIpvHfg8k9putzbJvtsLQPTBDONh062v943HY2PkvmkBACDCAwK+tsf3+PP8VqvVka4NsDC22WxmwY1T5Qli3Jfsxt+f+hngSqcVgIus4ptYuJckS8GRNnkfVaPRsKDB3q0wfDyeyhd4mTvYKsHaZ2e+jpHJHDo9OZnFA2IYhib9FYtFzWYz64BFbuMUeLIpSdYkAuPCqWBlGCHrBCuTHlknAOvrFswd64Xj4xiQGhwVR280GgaMfmsKjSUEaepLABTSpz8EudvtWos+0jTEhGYR6s3MXbfbVRRFtmeNrlmeAcLGfWkISpLDKR7VatX2wPpCPbUPX0sm0/M1KgIfRJSD3/E1nL/T6Wi9Xqter1swCMPQjgujQSJNUw0GA5tfAjhZODUeCAVZAf7KtgZAjiaaxWKhbrdrTS/z+dxqgZA1CIsHdE9my+Wynj17JkkG4KgwqCi+tg8msd/U103BFuyf+5PRYksQQOR4TnkhUKKceGWJLJLv8woIQYLmPQI+wEy2m6aPByxASqjXIvtCMDKZjM7Ozo4CnSdUnigRPCAp2+3WXumTJIlhAdk+OETTD0SEwy3ASd9wFMexvcllOp0aaSIDQwlBfQOPIcG+Dg9ezGazo5ohQQ//YhwEZ8jOYrHQYDD4n0mdkmzyWGA6MKlbEXmZAIDf11yI8L4DjoUioC0WCyuU8pAsIIvn97nBIqlfEQQ98/YNFgQMJhb2iGGSeTC5ZHIYBFIpWRdBjcXCaWFmyIeSjBEhOzI/BFZ/0DGB1Wv4vlV9MBgcdXERMJA72R/EzyaTiQEnzpjPPx40jWEBqL6A7mskzN14PLY1xBH4XYgKwWe1Wtmc+TnFWNmMjy3Azvk7gEqBnJrnZDKx7BHZlDMRgyAwYCI7ZWx+87yvc0kyUPINI4wVAGI+CfqAYKVSsYyHPY4Ef+a7Xq9rMBhYsCBg4i84s38pMuzW136CILDtLIARzT0+gPs65Wg0MtD3pQkCGLLmeDxWv99Xo9HQeDy2oHl/f2/jRbXAj7bb7dE+Lp4FCZlsl7FRf6UGRyaH//jtHrPZTLe3t6YW8PvUovr9vq0/vo7vIYk9PDwcyZJsk6CG7Zu4sH/fSMMRaWBVp9NRo9Gwuj5EEIzkPXsESebH1+24wCkyRwIDz7lcLrVYLPTw8GBEDHIIiYBgMX4IBNl7LpfTBx98oH6/r9evX5tMi6KD/0P0wXFq1WSelCp4cz0b99lK4Bt8IHAESGRZ31DnG7LwFU8q6JXYbrdH+7jp6qdUZoHsV4Gc/97f33/7wEdNiMVAhiSjo2OR7AHtmaI4ABpFkUajkWUzPDwLBIiQwTAJ3jD5LGkxIHd2dnZUXKZZgc8RIIMgsC0EsBFf6+AVPDTE0BGHpBXHsRqNxtF7vwgYsFCvcxOwkT9ms5mazabu7u5MbiUr5FkHg4HJacPh0Fil71ACLHEs39QDiAD2gCKgSaYyGAyOTvvwnW0YFXPtiQ/72FarlYE/34lhQ0r8uuFM3I9s1Ge1ZDeAELId8wy5IBNinn1DDEEE2+R3GCdzQJDxJM6TOeoTURRZDZisqVqt2n05gYUaSa/XO+qqhWU/PDxoOBxahoKz88dvy2DeyWKeNoAgj2M3sGMCj6+lzudzq2uRxZTLZSMvrIkHaS+vAtoQFuZ4MpmYn5NZDIdDqw9nMhl1u131er2jYIxfElRQTfhuNuqz/w2iKx1kYl9fk2RE1a81pIY/yKpnZ2cmJ0MuaFjx8+67IAuFgsnCvsHt4eHBao7S48b1bDar29tbW3ffTAY+UQahMcfL9cwJtV3GgT0z3jRNrVfA913M53M7ig4SLB1KJbPZTOfn57bflKwS//f1YjAdIk+Qymaz6vf7tr0LW4Zg0gHNfHLCDxggPXa44gN8bz5/OFje4xA4iqoBLvjtDxwBCc7e3d0dJQrfdL018HHCAF1ABAzAebfbWZC4u7vTZrNRs9k0AyETw0i9YWKQRG0mlt+hoQGjAkxharARmC9SF4YB+2DxyZB80ZP6IbWAzWZjewkxOO5HU4A/HBdplL1aOHkulzMtnHqED7q+k9WfvkGQrlQq1r6MYfgGjrOzMzuZhSyDsSJPUQ/CKMkci8WiyZdeqoTtMqd8DsJBAHr6IlMMGptotVpqt9sm8fo9aGSayHo+mOGoMD0CvK9HErgqlYo1rwBWfMZ3tlFboZ6IlMz3ck8yDUCZWhRZ8XQ6NafFFrGjXq9ngYPGkjg+bMLO5XJmg9RYcVDYPrbv15n3uFUqFdvgi43g5F7e8zVofsYcv3z50hSO/X5vtTFkS0hbNnvYp4dcid+SNRCYCTL+u7B3CAAZEFIqQYa3i+PnvrZM0KV+utls1G63zU75fuRhpGueE3LGUVysFccrFgoFa0wiGA2HQ2sgQ3UhiIxGI7M7amlhGNreOwK+lwU9iQCvyH55ZyK+igxOXZLL18eQJsn0eOWTz8KwbW9LKGeQo91up7OzMzWbTV1fXx/VTqlPM34yY7Af/0e1Wq/X1oCFP5JA9Pt9rddrs1nf3EhDJKSQJjCSnv1+b2UI+gaow/uGMJIZMJTGPcgtdnd2dva20Pb2wMfWgeFwqFarpUajYY5P6o7OTNAZDocmq5EN7HY7NZtNMyLfmcWiokPjKLCEKIo0m82OCpb+8wQaX0skqyRAksbzRue7uzsD1LOzM6VpqtFoZM4Du6d1nuYR39BC0PPNMsvlUq1Wy45o415BEBj4nZ+fW7BEOyeQAfp+7gFl5pgMlWDE7wBiGBuAT0DxsiHzRR0EQwW0vDQ9Ho8tgJBt8H2sJw5A6zNMHtZIZsf8cto8mRvBgLFjE6y9l2YIGtPp9KjWgv1st1sjHfxbt9vVZvO4N6hWq9n+QOyZOSJwMl4I2XA4VJIcXruDnO0PcgAIYPBexiFAzGYz25M1Ho8tw/VkbL/fazQameSN/IXcTp2ObA7Sl8vlTCWgHJDP5y3T8p2hvV7PgqWX1b/66isDWu/T4/HYwAXfxoYAXjLEzebwOq/FYqHPP/9c9XrdyB1ZWbfbNQLCRfcrdkPWR/MIgQbgJLijNkgHNeT+/l5RFKndbltzHdtPxuOxkYAPP/zQ1KvJZKK7uzuzY/bg8Vkk4ULh8MaMRqNxVH/EVufzuW5ubkx69wH+5ubG6l+tVsvm3Nfe6G9Aema9vJzvs1GIAKSBfgPpcCwZmeZ2u9WXX36p//iP/1Aul9Pz58+NkC2XSyNU/o0zHruQ73kWpHX8hsyPOjfYRi8I2So2Bz6BByhlvuwCvmNnvtEFtQnyynYiiIhXwb5V4FssFmq1WtaGT1E+n89rNBpZ4AOQAOLhcKhut3vExGFeZFUYFQXr7XarwWBwlMKzsEmS2PcBMEweMuFmszEmQmY0Go0swNHNRsbmNwYz6SwsDQ58DyywWCxaLRKj8ucXovuT/QEY6/Xa6lIEag96BAEWC4Mn2/CygK/vwYLpapJk9Tyf/SBZQxjYm5XJZOw0HaTRJEmsfsD6cCB1kiS6uro6agSIosiaPAiYzJl0kFkIyoA146ARgowGtswzPt3SkCSJOp2OBW265sgC2BsE0SGj9k1aECq+A3mdzjOkTuyY4IotEFBarZa++OKLo4wQh4cM3d3dHWWHnU7HQBVyRx2Jeg3S0G63MzLGNgWcnvVmOwFjQvaDLLGmBHcy8qurK/s9tj2wvqgi+/3hLFDWnnodWwQAKWQxGiRoZKGWV6/XjbETsGgQgsjSbEMQI2CwfeTpvHIvDjqmHkbzmCQrSTCGYrGo29tb65Cm7ue7NCFH2WzWDmMGh8AKD7jIfNgvWSRSMGtG8Or3+7q7u9M777yjy8tLawhBwvTnoyKhExTG4/F/I51IgOCPJ83gmj90oVqt6t133zXsg7BTR6PpZTAYmJqw2WxUr9fVaDQ0m83M3lg/AqvvrCX4so+UnyOP8nOa8qjhg2GUYXxjEkoPhJJ1D4LAOt2Hw6ElKP+jwEeQoNgOmA6HQ5N2YOfS8aZmSSb5UBthkpBZABg6CdnvxcG8ZILcEwel3rbZbDSZTCw9ZiJwGN8k45sFkEGYHL6H32VBCE7SY0cXoH57eyvp8cxEFoOUnTnjszBusjAy4tFoZGOj1sZnKJ4D7k/lMRYZEF8sFup0OsaIMBLWijnt9/v2/7QoA4Q8I7U4nsM3HvkAx7YD2BbdegRPxukzROosADXOzxxTH5AOZGI0GpmWT/2Sn1OrwKaYB9ZvMploPB5b8PMt2tTYqDngfHSdIiXBgCEIuVxONzc31v5NA1CpVNJqtbIOPP4fydMfSwfo+GYhZCIyNbor+/2+2u222RXPWiwWLdgDeGTngPF0OrUAh9SHLUAG8BufbaNo+FoQdSuCCz7faDQMBLEN5ioMQ1OKfKMR30HA9lsCyF6RvLDDZrNpwZJaMjYFOaHEQoZQr9d1dnam1Wqlly9f6sWLFya1+TM1yZQhFawB3aO+Cxmpf7M5bOim6cmXV7Bt6naoT76eTHZN1gxZhuhRYyU7IttCqkWC5L5gLmNttVoaDAYqFouWAVKDZE0J+NSKJR3VoanrPTw82Pr474AYSbLXm3mc9lkYkjqNVwRCn9HTvOfJYbvdti0yyM3gJMoAGSa25+PQ111vDXyTycTS4G63awZGfc1vYPTsmYcNgsOBrr7QSJqMDMbnadOVHvfp+e0JaOSc/gKDoKZIxx9GEMexBT/2pyFPeVAB7GFvgAFGQYYwmUzUbDbNES4vL40l43S73eFsPpgjhWoYppdqPRNC8oR9YdS1Wk2SzLggCH6bBX+oCcLGkOae1ke5kAFhz2TN/IzPEVR9phxFkXXUsS7UZZMkMWf1oC49vjWeYFyr1ez5CYDs3eO1PnwONkn2yJh5Dmo1PA+OiRQ8Ho+1XC5tOwmyJYEuk8lYMw0BCMfHnlAmcPgkSaymCeMnULOt5L333jN1gOCDwydJYkSBzB8C1ul09OzZs6MmKToyWX+auyABtIwT5LkfGTB1KogrvkJdjE5kfCIMQ8vuCD4EK+7NWMg6fX2Leer3+0cY8PLlS/V6PVMiWNc4jq0DFZ+TZHIn2ejFxYXZ+Hg8Nr+GAHD8XpIkuru7083NjdXCy+WyWq2WZYvT6dQkWHCHbBFSQSMetVhkfeyiVqvp7OxMd3d3ttUA26lWqyoWiwbo2DilGYgtwczXVCEjEEFsmcwOEkddEPzie6XHV8ghWaO4QVZpWsrlcibpUqZJkuSoBLDf73V+fm5KBfboM3G2y9zf3xvJo/yEcoGKxJrRRQr+kPHVajVLru7v7y3IUT/PZDLWvQn+EC9+3fVrMz4mhE4eFhuGAEj7Djq0YUlHtRsmOUkO+4/6/b4kGaMCsJB06KqEVVI7ofgM+8ZJ2WfnO85gJxRtOXaK4ioSFjo6OjnZKJNKrQzngb1ghGRAdPoVCgWTrQhW0mOHZhzHJqf4e+BUngkBOF4GZH796SR0VRWLRXU6Hd3c3NiYcDIyBQgKsjBs3B/3Qw2A9aWwTLEfRktjiHc6WDNNMVwYJ1mW32LBemF3yFmAH8d98ft0B1NzQRoiAJJtIKWRmVGrgSUSTJh7GP1ud9h7xQbeer1uGfW7776rdrtt600pAJZ6dXVlTh3Hsc7OzozUoGrU63XLgMlOfDZLhzKgA/ARyFENIEFeLUBBQcbzqgI26UkbtoacRhCFCHCQABmNrzPShMAcdjqdI4kVMAfQ0vTxKCzp8aALL2NSX8TPOOIL5QEZLJfLHR0WwBjwYTqYCYTU0JgDGtOQSsnMwQUIFRJgsVg0Qg6Bfyp5+gM3ODTD+zQBDsyEdPA7lFIgZmSfkCcSBggTmOlr/Pg4Y0FOJbOmW5L/p5REjY4tN7vdzqR4bIJ9kr4G7k+0StPUauHYU7/ftyYgr14x5+fn5+ZzECIyt06no9vbWwvKzWbT5hy8Q+UhofCd8P/XgY/0n0IxToqxII/xUASbZrNphkDWheF5TZ0OJBid9HhCPU0uABMdPAAaBX86sjz7JCCizdO5BrPyRsI+nVqtpvl8rm63a8/hJ5DP+lZ5GCjjIvOgIwwQxnm8USAP4GBkFj57pAbgGze4F3U27kv2A1GQdLRJFtBLksSYE/uqcAaAgS5UurSo7fjgD1hAAnieSqViZ4QS8KilUN+iBsU9AVxAjeAwGo0sE+a5CoWC6vW6OSIdsH6/GDIdHb2wcwKel+0BbNYCFkp2Smu59LhFhVrdw8ODPv74Y6vR/uhHP7Kuwevra8t0stmsLi4uzCcYO9+NxASrz2az1rzTbrftdwB7mlMIPDB/JP5er6flcmlbZ1BXCoWCarWa3n//fWuvZ88hMh612jRNLZuCJLFOZI3MGYEROY6sFgnUN52RFVDnB3jpoMUuC4WCGo2G2dB+v7e9m7TY8yZ0L0OiBBGEIDMAOdszSqWSEXrsYjAYHMnMKFaUSOgroEEG/AOPWD8ISi6Xs60hEC/8AhIOhjFH+Ci2Lsm2wjB+iCF1eC4/zwRM33XcbDbV7Xb1/Plzq81DhOg4RnamkUqSEQUUOd9HkKap7u7ujvyvVCpZZzfn3YL7ZKioPiQVEG6kcL5nNBqp1+sd1Yqxc3pGCIioLuDutw58pPxMou8QHI1Gtp/CgxsPf3d3Z+ktAFetVjUej01CYKE4rYLsA3DfbDa6vr62I3sAIJidryniqAQhpDFqCLA0agO0VjcaDQsKPnv14DedTo9auiVZcCZw4Ez+vwROHAXHQNLkWcbjsQUvsiQYDcGfoICz4xw0o8D0AG6cV3osdvuMmkwOp+SoNSQRSbZ1gfPwfOMKtQuIBERhMBgcPbtn3vydNZdk6gBrRdcqAZesC8fysi4NFMVi0ezNd1bSFAHjl3R06LVvyiGYU39CFuJZyVpg719++aVub28tgPsmlk8//dS2myDt8nP2c+ITSD7YK+C63++tE5rnwWc4lADAJZgyXsgZY5Z01DUIuIRhaHK8VxLiONbt7a35gQc1X7fzddxOp2O1c+yd7Krdbtv4n3Yf8xySzFbAGoAS24XoMFYvIe92u6NzTX0ZpdPpWMaOxIwvcWg+WQ4ZCs1sXtYEDxkzygakHv/D7rBlsBFVhkBL0yAyJYoT/kmDF4kBAI/aRkICBiCJQpx4WXSz2bQa32w20/39vXq9no1tsVjY/mXflEbywDMzH7vdTjc3Nyb9gofUYSuVit68eWNxAxzirToEO68K4Lf4I7YtHZRHlB+IO9kk/+a3hKDgve16a+ADfDabjW3e9h2Wq9XK6gY0ebBg3W5X6/Xa5B8CEtINEZ6MjkBEMEEP9nUxGLgkAz0mkpZbtF4cB0f2xXPe+gBjk2SLjtQGy8YRyCxgLKTXyJ282Jb9Z0gQnqnDmiAIOCvBQzrWqtkbB6Cxf6lcLqvf71smxPiRVimyU2RGSoaJ4xyMCwnD1wBh+DBNsgbqFDBdarE4RBRFJuGShRLkOZQW48XA6YYFzJCYOSEHG/SZGc4K6wS4RqOR1QcZG3uM/HgzmYx1sUG4sAu2UJBJEhBGo5EFZzIPso00PRw5Rcs8GRhzSKMLb+XAQVErqPUgtcJgqXNB/DxRmc/nRqby+cMbP7Al3hsZBIEajYb6/b5lQ14q5NnxQ9QOZFdA3jcnsUnekwYCEg1Z0uGUoZubG5NnUWKeP39uCgv74vA9giOqAWeDspWC2k+z2bTgDgn0XZGl0uF9dxyHhh2SBb169cqaeai18/1gDpvCZ7OZtfM3m03V63XVajXV63VdXFxYXfv6+lq5XM7kdXCOE6ZQUySZ7N1oNExKZy4pF9CYdHV1pUKhoOvra5MLW63W0fnHfB6yApGjKWoymVi3Jrax2+1s+xlkm5d0QyZYfxSmy8tLy9DwvfF4rHa7bWWISqViByUwXsisX2t/IAZYTbMchDOfz2swGNhWkFKppPv7eyNmkEvq7yRkkvTRRx99u8AHyHl5DkMlkgNqMF82/yJr+YOAqe+RrcH2CYi5XM40cACZYOI3TsJQYQzIr7APv2A4Kx1iBErGDJt+ykIBHYyCzZVIq9S8mJ96vW7PwpFPYRhaYwtZKk0qzWbTuiuROHwRmG0izI3f/sF5lZIMrIvFomn2ZC44MVkTXYd0ksZxrIeHB5MP/D6iIAhsTxQBHiZFDc2vIQCP4wLAADcSKZ29vjnF7/ekHhdFkW2F4Ow/sprd7tDKDyv1TS2+Xst2gE6no/Pzc9uw7DeMUwdhrjk0QZId8AyI0ODlN47jG9i3JPv3drttwWO/31tbeKPR0HK5NAaMRAOxAmQKhYKePXtmNuPlN57BEyMyP+ow5+fnlpFwcgk26bsqvdzGfdk367M0pEO6dvEPpECUB6RMjxXYC1nGeDzWbDYz/GA89/f3hhGQaKR53/BB8w6ZDvIioI/9bbdbIz2QTOQxMinsmwySbk8k6NFoZMSWvgBq1Pgh+/2QM3kjB9sKIMK3t7fWqci87XY7C7zYJMECn6R7FMLEy3oJCtgzRL1Wq5kcSYcxWSw9EqwlpAZ8f9obQbCk2/b8/NzqlEmSWP+HP0wim83a9i18ulAo2LYu7k2yA1n0kqrHMWJNuVy2hjqvwkBsuAdKx7cKfGdnZ0cMgn1fcXzYpkBHGvtlcDpYD/fwHYQMlqYEmD9GgAxGgZbgxiRg4OypgQVjmHQacXFiCBmK79okOOJIHtBhLml6fPg1TgNokAkhUfFdjNnXBXAYDF3SURs2zw9Qkhnw7zR8wIyYm0wmo9FoZEGZzAwpmuCeJImBK2BA/ZS6DXUHXzNttVpHryIh2Pj2Z+lRgsb5aHRgnmmy8RkOEhG24cHA1zK5N00ppVLJ9tlRC6XYzT1hsL5TFImUlmi+m/oJjVTszSOTxqkovBMIkeGRbtjETG2cenitVjNp0HcEMgcAH2ST7KXb7dpePxpoAO9KpaLz83Ot12uTtiA5QRCo1WqZQhMEgdW70zS1RjDW1Z+RCEHyMi8Ei+YPJEYABuKJDzw8PJgt0xTCGgH6kAL8AqUDsMf/nta+AVOvbPhtFBACABhZE5ITBIHJ1Pg9gRufQkojYBLwqAmifrHx3W8rAsiRMm9ubhSGodk4R9tx1i/+hA+xFQlFi1quJ+nUi3lmf+A2+3Q90aDZJAgCnZ+fW2Cnach3d3M+MWsOKQTX/CvRIObgIzYDbtLV7WufkGgycxqlqN3ix2Ab9yDAsRWLPhDmBQLlCfe3CnwPDw+Wbp+dnVnt6u7u7uhh0eaZWBwMx/Ztpq1Wy9rcgyCwrkSyGWQUJpEmFr6PYINMJj1KgujQvlXW17ree+89W1SkPaQngMQHH591+X9DIk3T1Db1A/QAG6dXMAdIY5eXl5Yx1Go1269EFyjZJl2KyJWwvPPzc2NqtAb7vTUAOfVLugxhqOVy2c5QJPtBxkGyg7XhPDBljn7ytQyahnzTE6SBzkOyJpyn0+no9evXkmRbLvz8IYcjvfnN/vwOki/ZPAwUGYhslMyFjAaSQ5AgmJBlIMeyKXY8HluGjzzY7XbtlJHxeKzb21ttt1s1Gg3V63W99957mk6ntp8PoIQseQLlN/n6ueCdaHSzIbtzghKSLLUVakq+Aevu7s46+SBevBKHWvr9/b0RFTrjANwvvvjCiCENFr4hCYJJ1oavEpAvLi6UyWT05ZdfHsDmV0D57NkzUwBQVlBpnj9/bkGK4A35RhqjHMIWJbJ8xgFBg7BeXFyoXC7r9evXRrjb7ba173tMAwOazaaazaY18WADNFch5ZGpEMDxJeyUQIQcjzqCnI2iAsZ4ya/ValnPAQGC9cWvyFSRO30i4ZvdmE+yTenxgPZms2nzVywe3pzgz91kywxzhw1ADn09H3kS6RxytN1ujZzxlh8kYbbU+KTDy/JpmtprnFBSUECiKDpqboQ8/I9qfLVazc5a9FIIurYkeysAreYwQCYVJgdo0KDi2RwNCz6tlR4zMuRAJrler5uTEhABJjJB3yFGV5c/8gcjRFZEZkQmgxmTOpNpkakxfoCKAjMdpL6jkmK1lyGRcAlMOCxAzn3JZtgCAslgWwbsBxbWbrdNDvOZBHOCLEG2TYax2+2sg7Db7ZqMyDpQr4LZA0qetcPgkP663e7RkU80CgAg5XL56LQFMlaAjHkjMwvD0DayTqdTk5POz8+t4YN5TJLkaMMzUhNrCYiT8Z6fn9tWHAIf6+nBCLI0GAxsLyMHjJMtcaILGQrfi1TlN9pC1vhd6fHk+81mY/bMc9F2T+MHgQr79ITTM2x8GLunWYoOUGSw1erwRg183bfLJ8nh1Huf2W23W2uMApAJpIATp/MDzNIhq8NG2RqTzR72GlarVd3e3lq9E4mW5hPmIEkSa9JAGuSEEyTQZ8+e6fz8XHEc69WrVzYP/t6bzcZ+hy0I2HSz2TQCibyG7/gOU/ANxSRJEsuUeT5qvdTEeXaICvVnGptQLSBOkG5ILpkVKo6vkVIXp5EGRYfSE/fEVlCtCNDYmiTzX+acEsRm83j0Ij7tT8ZBdWJ+nqpp4IR/S4SvIdPvwFg5cIKaMniGPaPo3N7emt9/q8CHTEUWQWSnkwxHx9AJFrAN6fh1JN1u1zR0sgDACq0XkABwieQ0yvjX/VBUBrgJIjRXAFQ0y8BIySb9VgtOgWFBKKIThDmazMuy6/XamBGGHYah1Q4wKpymVqtZcKPxhQwL9u+7AwEX6fHt4BAFZEYAn+daLpcGNL4T0u/RabVaR/uNkBT9XiTkPRyVBgrugfzE+pFxIcUNh0OTE309gAYoOhpxXJ/t8kZz9mAR2H23FkaPkzBnMO5SqWSdmVdXV1ZEhzn7/T4+IwTcsHHs0tcM7u7u/ls3ar1et5oTdsJ+zu12a5kDp1BQR/JyaxAE6vf7R01kAB91V8ZEzQOZllofTBrAgqlDlLAbgi370KifoyD4Jg+IJ37DW0Tq9br6/b7JcF42j6JIb968sXMUAbUwDPXee+/ZQdxI3mysRkqmLuVldOR5pFyaJ7Al7590RQ8GA3311VdmR7lcTu+//76pHnSZQ3YBatbNy3uoD0jHlHTw6f1+b4cDSDK7oAuZwARB8SUZAj8NQ9TdkFY9+WbOWBOCBVkS/vD69Wt7Brp3kYeR8CE1kDu20fDmej6fz+ft4GoILphMZrjb7Y7q4GTEqFn7/d66L1kn7INmLcio3wKCLRLwpMcDzt+8eWOYkCSJzs7O7B5vu94a+H72s59Z2vr8+XM1Gg3rvgNYkQ0BNxaNhSKtZ9B0oZFasxi+OO3ZKvU8HobGEVggUgCGg5REwKIov16vdXl5aQcE07wBCyNIjEYjK7DTENPr9Y5qGewVpGEEXRsQpOCPI0qPrdo4CQCEHLDf7+3UF6QJsh722TBPBDlOJuEkA5yQIrx/GSfgitwEgMC2aHog+PB2AuRKGBx1ErIOH3ABFn4O2NHMgFNAqJgj1tpnlL5e5X/ORl/sh8yYgj7PUiqV9OzZMw2HQ9t4Dpl5+lysx3w+N5Da7XZqtVp6eHgw+0NOJ+tHKkVaYW4l2RiQrH0NmY5NbINOVNgw0g+1cL8dIps9nCMJScMOmE9+Tns6n4WdQwwBQ4DEdzZSSjg7O9NXX31lMhaBj4PWfU0XUgQgoVwwH5Isa8BvsCXmy3clQ0JYYzIqCGOn07HMn7klMOx2O52fn5stU3JgQ/9oNNL3vvc9609A+vVzhfRKgxeYEkWRZRS73U4PDw9HfkrjhZfW/ekwkmyvZTab1evXrw2DyOawAeprNPn4kgd10u12ezQeSAeZLuQVLPYNIZAoMjofBPk3iA9/73a7kg5KX7vdtqMbweIoitTr9SzD3+/3+uUvf2ljo8ZeKBSseYx1Yu2wK2qbudzh0JCLiwvr+yApuLq6MtvCBi4vL404fKvA1+l0FMexAQ3ARk2F1LNQeDw8GiPxjR10Z5LGI/lIj1smYNawR1pckf3Qz2EzTI7PDqlN+cBH1kXbbrVaNTmCGtVyuVS73baCNkAF26Ghh/EAEGR3sBMyh2azaa3vgLIHPiRh6oP+5AG6R/2JKTiCB3WAjuAAgWg0Gvb2A74bB0TiIVNC8vDZHvPmC93o9mTj1Bp8lxnAnKapGT61U4I6oMZxSnSrIgfyu757FFLBv/tDi+nc3GwObwRATfBbYMhkIHDUZ7AN7zAAMkDlu9lwWvYTPTw8qFAoGGNlrXg+X4tCsvVOzeuLAHgIAEGJ8dP+TjbAtga/oRq75F50VfpORda20+lY0wKBBWlvOp1avYn5/Y3f+I2jRqB8Pm+yMIBPNuMPG4BQsM4QF+YATEAFkY7fC0kmMRgMrBEIv0e14N7MFcQE0oIETTbH99BBu16vrWZK9k0mxHexLYtmPprpkPPIuPEzgJxaIWskyTCHrVhIrEi1dDy3223rgORUGp6fe1GL3u/3ev78uWEzwapSqajdbmswGFhggJDg88j2NBNB4iAlYDKNWewJhPiBDWCLLytx8AHNX8jtJC35/OGlttgGtuKbAMls/Wlg1WpVw+HQ8IBD6Gk88n72rQMfr9hBZoFNEIgkHWnP1Dt8jQaQIatBysjlcqaz+8zQS6qk8v6VRDBA/440DB9nwhgJanwWJ6E2uFqtrPPO74nCSAgu7EUCrBq/euswRojTEnjZruAP5UZSBbQJfv5NxwQSL8X6TdUERjJCv48HJoTBUBdkjxASJjICoMxcA15I2zgSGRjSNJl8vV7XcDi0Z0LPZ6vBeDy27wHMCSLsr+KiVkTNhbUvFB5fPUMdqtvtqtVqGdP3c8Y+IRydjkW6+jhFAnvAvslevWRKdkDDFQSOMWKnnU7HyBIAm8lkrMvYHwGHpANB4c0bAClHzQGS+BggSxMUtky9gzcBIJ+hChCEW62WNa/wO2Sb+C4kJEkSew8eqgPkE5vE7+fzuakUBCvmh88Axp70Uc+ndR5ZGT8me/Lde2EYmmpEfR3fwE/BKGRA1of9amShNOk9JSkoJSgsnLKCvTMuCKjfSgOuEFBpvCFLh7gyXvAHaRjg5m3rnrTik/g5vsT9mR+vPEH46Pb05NerJE/raswj9/U9G2SbZMcEUpp0kIJ9VkmXND4JBuA/ZG+My2MqSg9HAzIGSFQmkzlqhMKmfWL1TdevfQM7mQcG8+bNG3NkmDoyJ80kvqvI79NhHwhnaVJj8O3KMBYCI7o1iwYgsCC+XRbGwQZyshMmg5qgb/lGQuVEBd7FR1bnDZvCPGyZ+gLBjYBL5kvzCMZE8GHc2WzWskwCKvo4jBMmzz04B5FuSi+B+cCIYZONwzwBcoIaTkNmTxCmbsTa0/FFJg/7osEGzX+xWOji4sLmHEcHZFgHgIR6DBIYzkDRGlkagAiCx3NcCSQEZ9QI5EVqZbwhhEweR4N0+RpZPp+3QEMG47dWwLp94xMSP89MHYR6GABQKpVMuiLzoykDZyaYQ76wDeyRhg0aOQj4g8HA5vjFixdHR5D5QxSy2ax1mwIQKBdkxmTY2+3WmDvZ4WazsUzCbxfBpvkeQArC5zuO8UNsjn+H3HhFgHVhjjmthPUDLyAudGli3/gBIA1xZ5sAAI9sLT2WH1gHAjUZIWBPBp0kiWVLzIMkO/QClYiGHZQm34zFv5FdZTIZq8WTYSFPYoscXcbeU99lSic2SUsulzvaKzgYDGy9WRuf7RFUyTbBM28LzJ9v3EPm9U1A2DjkETLkSzBeZYAIQMYlHa0lxMUnMiQO2N7/qKuTkySQVzB4jIsOQQIUoIvDMPEYfhzH9jJYQLVerx+1ctOMwqTDWAB5ztgDUGEsZIJRFOnm5sYAG6aVzx/OfoQhsM+IIBPHhw2xdLQxmT7wUahmEakzETwxMFJ0wNg3gbCv0DcB0bGGMZCFwNRI99mUzAI/BU3GwAnpSF04CpkjRsX+KiRMD+oEVzKEZrN5FKiRS5jDMDy0+Xv26TNw1mazObzf6/r62pyZPUMcep7P5+1kGmqbrAcEBUBYLpeq1Wpmi3d3dyahYYutVkvb7dbqKjgbtd00TY35d7td636bTqdHTJO6bzabtTNeuYIgsDoOXXZpmlqTgJeGWStAhKBPtsyaENABK+kgcbGW3tmRKH1t2CsnPlNBwr+7u7NA57f4SI/HS9HqzmfJ+uieRLHwdU6CArVS/IcgNhwODRw51xYQ5F6ZzOPbMnytlJrPO++8o81mY9+P0sI8kb1x5KI/nN6XYNi7CZHi86PR6L9JcJLs9UN0gUIqaBTBzsFF5lKS2aQ/HQhSRiBk6w41PggtgQmcBFcpDyAn+np/HMf2WizWHDv0ihBEB79HPRuPx9asxneSsfGd/g0okD38D5KDesDfqaHiCwR0byckT5B+sjt8glcucdwj9336jsJvFfh+9KMf2ekXAPM777xjoOiZ1dMGBECb7KpcPryxOEkOZw1SJCUo+gyGCybmZbJ8Pm/7OGj1Zy8gWSUSoN9ygAxxdnZmzk7zB11vgAfg5s+vIwthKwGGCntn3AQcXkLK0T7ozuzxu729tTQ+DEOT/jBKmDJGhxzJM9GK7PdEAswERRpxJB11Vp2fn9s5fr4GReGZwEk2BclBsqlWq7q6urK9lzgE67Xf79Vuty3bR/rLZg+n7MOCCXRhGFrHHnXFH/zgB2YXOA8BJJvNqtfrHYEkgeDy8lK9Xk+1Wk1v3rwxmQRb63Q6lims12uTYdi8TGPPbrc7eiMAR1VtNofDr6+urszmJdk8ozQUi0VNJhMDeRpNKpWKut3uEdtljxgZN47PnJIlkolks1m9evXKfsaLnwlunI9ZKpXs1WI0HVFbpg7n9275d0MiYbfbbQsiPiu6urqyzkkC/mKxMKLMXPAWFbCA4Nbtdo+2zURRZEe+kTUwFogrJ8NARpvNpmUUADvrQD0fAIR4QpI5+YQ3xLBlgb8DxtgzgYctLvgUvsYbOrBPshfsCuIGtnBeq/8u1p1mKlr96Ugn6EImyc4gPnEc2x495Edq0mAjisLZ2ZkdqM9JUOADHbqcwMW7J8nUwCNka+R8iEOapnrx4oXFAVQuxoHPSo/nv1J+AMv4dy/PgoEXFxfWlIQvse77/f6oA/ubruDXpYSn63SdrtN1uk7X/09X+P96AKfrdJ2u03W6Ttf/5nUKfKfrdJ2u03W6vlPXKfCdrtN1uk7X6fpOXafAd7pO1+k6XafrO3WdAt/pOl2n63Sdru/UdQp8p+t0na7Tdbq+U9f/AdzLV4LMLzbTAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reconstructed Spectrogram:\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Extract Spectrogram\n",
+    "audio_fps = 22050\n",
+    "spectrogram = extract_melspectrogram(video_path, audio_fps)\n",
+    "spectrogram = {'input': spectrogram}\n",
+    "# [80, 860] -> [80, 848]\n",
+    "random_crop = False\n",
+    "crop_img_fn = CropImage([config.data.params.mel_num, config.data.params.spec_crop_len], random_crop)\n",
+    "spectrogram = crop_img_fn(spectrogram)\n",
+    "\n",
+    "# Prepare input\n",
+    "batch = default_collate([spectrogram])\n",
+    "batch['image'] = batch['input'].to(device)\n",
+    "x = sampler.get_input(sampler.first_stage_key, batch)\n",
+    "\n",
+    "# Encode and Decode the Spectrogram\n",
+    "with torch.no_grad():\n",
+    "    quant_z, z_indices = sampler.encode_to_z(x)\n",
+    "    xrec = sampler.first_stage_model.decode(quant_z)\n",
+    "\n",
+    "print('Original Spectrogram:')\n",
+    "display.display(tensor_to_plt(x, flip_dims=(2,)))\n",
+    "print('Reconstructed Spectrogram:')\n",
+    "display.display(tensor_to_plt(xrec, flip_dims=(2,)))\n",
+    "plt.close()\n",
+    "plt.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sample a New Sound Given a Condition\n",
+    "\n",
+    "Feel free to adjust the parameters:\n",
+    "- `W_scale (int > 0)`: the output sound duration will be `W_scale * 9.8 seconds`\n",
+    "- `mode (str ['half', 'full'])`: the sampling mode:\n",
+    "    - `full`: the sampling starts with conditioning-only\n",
+    "    - `half`: the sampling will be primed with the first half of the quantized spectrogram representation \n",
+    "- `temperature (float != 0)`: softmax temperature. $T$ in $\\frac{\\exp(x_i/T)} {\\Sigma_j \\exp(x_j/T)}$\n",
+    "- `top_x (int [0, codebook_size])`: the distribution of the next token is cliped to Top-`top_x` codes of the codebook\n",
+    "- `update_every (int >= 0)`: how often to print progress (slows down the sampling speed)\n",
+    "- `full_att_mat (bool)`: \n",
+    "    - `True`: shows attention at each step at once\n",
+    "    - `False`: shows attention at the current step only"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sampling Time: 7.22 seconds\n",
+      "Sampling Time (with vocoder): 7.25 seconds\n",
+      "Generated: 9.85 seconds\n",
+      "Spectrogram Classifier (K=10):\n",
+      "\tplaying cymbal: 0.55628\n",
+      "\tplaying drum kit: 0.18596\n",
+      "\tplaying bass drum: 0.10818\n",
+      "\tplaying snare drum: 0.08010\n",
+      "\tplaying timbales: 0.03887\n",
+      "\tplaying guiro: 0.01512\n",
+      "\tplaying bongo: 0.00624\n",
+      "\tplaying congas: 0.00357\n",
+      "\tplaying double bass: 0.00205\n",
+      "\tplaying djembe: 0.00163\n",
+      "\n",
+      "The sample has been saved @ ./logs/-Qowmc0P9ic_34000_44000_trim_to_10s.wav\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "                <audio  controls=\"controls\" >\n",
+       "                    <source 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\" type=\"audio/x-wav\" />\n",
+       "                    Your browser does not support the audio element.\n",
+       "                </audio>\n",
+       "              "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.Audio object>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Define Sampling Parameters\n",
+    "W_scale = 1\n",
+    "mode = 'full'\n",
+    "temperature = 1.0\n",
+    "top_x = sampler.first_stage_model.quantize.n_e // 2\n",
+    "update_every = 0  # use > 0 value, e.g. 15, to see the progress of generation (slows down the sampling speed)\n",
+    "full_att_mat = True\n",
+    "\n",
+    "# Start sampling\n",
+    "with torch.no_grad():\n",
+    "    start_t = time.time()\n",
+    "\n",
+    "    quant_c, c_indices = sampler.encode_to_c(c)\n",
+    "    # crec = sampler.cond_stage_model.decode(quant_c)\n",
+    "\n",
+    "    patch_size_i = 5\n",
+    "    patch_size_j = 53\n",
+    "\n",
+    "    B, D, hr_h, hr_w = sampling_shape = (1, 256, 5, 53*W_scale)\n",
+    "\n",
+    "    if mode == 'full':\n",
+    "        start_step = 0\n",
+    "    else:\n",
+    "        start_step = (patch_size_j // 2) * patch_size_i\n",
+    "\n",
+    "    z_pred_indices = torch.zeros((B, hr_h*hr_w)).long().to(device)\n",
+    "    z_pred_indices[:, :start_step] = z_indices[:, :start_step]\n",
+    "\n",
+    "    pbar = tqdm(range(start_step, hr_w * hr_h), desc='Sampling Codebook Indices')\n",
+    "    for step in pbar:\n",
+    "        i = step % hr_h\n",
+    "        j = step // hr_h\n",
+    "\n",
+    "        i_start = min(max(0, i - (patch_size_i // 2)), hr_h - patch_size_i)\n",
+    "        j_start = min(max(0, j - (patch_size_j // 2)), hr_w - patch_size_j)\n",
+    "        i_end = i_start + patch_size_i\n",
+    "        j_end = j_start + patch_size_j\n",
+    "\n",
+    "        local_i = i - i_start\n",
+    "        local_j = j - j_start\n",
+    "\n",
+    "        patch_2d_shape = (B, D, patch_size_i, patch_size_j)\n",
+    "\n",
+    "        pbar.set_postfix(\n",
+    "            Step=f'({i},{j}) | Local: ({local_i},{local_j}) | Crop: ({i_start}:{i_end},{j_start}:{j_end})'\n",
+    "        )\n",
+    "\n",
+    "        patch = z_pred_indices \\\n",
+    "            .reshape(B, hr_w, hr_h) \\\n",
+    "            .permute(0, 2, 1)[:, i_start:i_end, j_start:j_end].permute(0, 2, 1) \\\n",
+    "            .reshape(B, patch_size_i * patch_size_j)\n",
+    "\n",
+    "        # assuming we don't crop the conditioning and just use the whole c, if not desired uncomment the above\n",
+    "        cpatch = c_indices\n",
+    "        logits, _, attention = sampler.transformer(patch[:, :-1], cpatch)\n",
+    "        # remove conditioning\n",
+    "        logits = logits[:, -patch_size_j*patch_size_i:, :]\n",
+    "\n",
+    "        local_pos_in_flat = local_j * patch_size_i + local_i\n",
+    "        logits = logits[:, local_pos_in_flat, :]\n",
+    "\n",
+    "        logits = logits / temperature\n",
+    "        logits = sampler.top_k_logits(logits, top_x)\n",
+    "\n",
+    "        # apply softmax to convert to probabilities\n",
+    "        probs = torch.nn.functional.softmax(logits, dim=-1)\n",
+    "\n",
+    "        # sample from the distribution\n",
+    "        ix = torch.multinomial(probs, num_samples=1)\n",
+    "        z_pred_indices[:, j * hr_h + i] = ix\n",
+    "\n",
+    "        if update_every > 0 and step % update_every == 0:\n",
+    "            z_pred_img = sampler.decode_to_img(z_pred_indices, sampling_shape)\n",
+    "            # fliping the spectrogram just for illustration purposes (low freqs to bottom, high - top)\n",
+    "            z_pred_img_st = tensor_to_plt(z_pred_img, flip_dims=(2,))\n",
+    "            display.clear_output(wait=True)\n",
+    "            display.display(z_pred_img_st)\n",
+    "\n",
+    "            if full_att_mat:\n",
+    "                att_plot = all_attention_to_st(attention, placeholders=None, scale_by_prior=True)\n",
+    "                display.display(att_plot)\n",
+    "                plt.close()\n",
+    "            else:\n",
+    "                quant_z_shape = sampling_shape\n",
+    "                c_length = cpatch.shape[-1]\n",
+    "                quant_c_shape = quant_c.shape\n",
+    "                c_att_plot, z_att_plot = last_attention_to_st(\n",
+    "                    attention, local_pos_in_flat, c_length, sampler.first_stage_permuter,\n",
+    "                    sampler.cond_stage_permuter, quant_c_shape, patch_2d_shape,\n",
+    "                    placeholders=None, flip_c_dims=None, flip_z_dims=(2,))\n",
+    "                display.display(c_att_plot)\n",
+    "                display.display(z_att_plot)\n",
+    "                plt.close()\n",
+    "                plt.close()\n",
+    "            plt.close()\n",
+    "\n",
+    "    # quant_z_shape = sampling_shape\n",
+    "    z_pred_img = sampler.decode_to_img(z_pred_indices, sampling_shape)\n",
+    "\n",
+    "    # showing the final image\n",
+    "    z_pred_img_st = tensor_to_plt(z_pred_img, flip_dims=(2,))\n",
+    "    display.clear_output(wait=True)\n",
+    "    display.display(z_pred_img_st)\n",
+    "\n",
+    "    if full_att_mat:\n",
+    "        att_plot = all_attention_to_st(attention, placeholders=None, scale_by_prior=True)\n",
+    "        display.display(att_plot)\n",
+    "        plt.close()\n",
+    "    else:\n",
+    "        quant_z_shape = sampling_shape\n",
+    "        c_length = cpatch.shape[-1]\n",
+    "        quant_c_shape = quant_c.shape\n",
+    "        c_att_plot, z_att_plot = last_attention_to_st(\n",
+    "            attention, local_pos_in_flat, c_length, sampler.first_stage_permuter,\n",
+    "            sampler.cond_stage_permuter, quant_c_shape, patch_2d_shape,\n",
+    "            placeholders=None, flip_c_dims=None, flip_z_dims=(2,)\n",
+    "        )\n",
+    "        display.display(c_att_plot)\n",
+    "        display.display(z_att_plot)\n",
+    "        plt.close()\n",
+    "        plt.close()\n",
+    "    plt.close()\n",
+    "\n",
+    "    print(f'Sampling Time: {time.time() - start_t:3.2f} seconds')\n",
+    "    waves = spec_to_audio_to_st(z_pred_img, config.data.params.spec_dir_path,\n",
+    "                                config.data.params.sample_rate, show_griffin_lim=False,\n",
+    "                                vocoder=melgan, show_in_st=False)\n",
+    "    print(f'Sampling Time (with vocoder): {time.time() - start_t:3.2f} seconds')\n",
+    "    print(f'Generated: {len(waves[\"vocoder\"]) / config.data.params.sample_rate:.2f} seconds')\n",
+    "\n",
+    "    # Melception opinion on the class distribution of the generated sample\n",
+    "    topk_preds = get_class_preditions(z_pred_img, melception)\n",
+    "    print(topk_preds)\n",
+    "\n",
+    "save_path = os.path.join(log_dir, Path(video_path).stem + '.wav')\n",
+    "soundfile.write(save_path, waves['vocoder'], config.data.params.sample_rate, 'PCM_24')\n",
+    "print(f'The sample has been saved @ {save_path}')\n",
+    "display_audio.Audio(save_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "4303ab8d4aa722fbfe5ef930090d796d2891667ab68dcd386422f219ade7139d"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.8.8 64-bit ('specvqgan': conda)",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/conda_env.yml b/conda_env.yml
new file mode 100644
index 0000000..c2eccc3
--- /dev/null
+++ b/conda_env.yml
@@ -0,0 +1,246 @@
+name: specvqgan
+channels:
+  - pytorch
+  - conda-forge
+  - defaults
+dependencies:
+  - _libgcc_mutex=0.1=conda_forge
+  - _openmp_mutex=4.5=1_llvm
+  - abseil-cpp=20210324.0=h9c3ff4c_0
+  - absl-py=0.12.0=pyhd8ed1ab_0
+  - aiohttp=3.7.4=py38h497a2fe_0
+  - altair=4.1.0=py_1
+  - appdirs=1.4.4=pyh9f0ad1d_0
+  - argh=0.26.2=pyh9f0ad1d_1002
+  - argon2-cffi=20.1.0=py38h497a2fe_2
+  - arrow-cpp=4.0.0=py38hd6878d3_0_cpu
+  - astor=0.8.1=pyh9f0ad1d_0
+  - async-timeout=3.0.1=py_1000
+  - async_generator=1.10=py_0
+  - attrs=20.3.0=pyhd3deb0d_0
+  - audioread=2.1.9=py38h578d9bd_0
+  - autopep8=1.5.6=pyhd3eb1b0_0
+  - aws-c-cal=0.4.5=h76129ab_8
+  - aws-c-common=0.5.2=h7f98852_0
+  - aws-c-event-stream=0.2.7=h6bac3ce_1
+  - aws-c-io=0.9.1=ha5b09cb_1
+  - aws-checksums=0.1.11=h99e32c3_3
+  - aws-sdk-cpp=1.8.151=hceb1b1e_1
+  - backcall=0.2.0=pyh9f0ad1d_0
+  - backports=1.0=py_2
+  - backports.functools_lru_cache=1.6.4=pyhd8ed1ab_0
+  - base58=2.1.0=pyhd8ed1ab_0
+  - blas=1.0=mkl
+  - bleach=3.3.0=pyh44b312d_0
+  - blinker=1.4=py_1
+  - boto3=1.17.59=pyhd8ed1ab_0
+  - botocore=1.20.59=pyhd8ed1ab_1
+  - brotli=1.0.9=h9c3ff4c_4
+  - brotlipy=0.7.0=py38h497a2fe_1001
+  - bzip2=1.0.8=h7f98852_4
+  - c-ares=1.17.1=h7f98852_1
+  - ca-certificates=2021.5.30=ha878542_0
+  - cachetools=4.2.2=pyhd8ed1ab_0
+  - certifi=2021.5.30=py38h578d9bd_0
+  - cffi=1.14.5=py38ha65f79e_0
+  - chardet=4.0.0=py38h578d9bd_1
+  - click=7.1.2=pyh9f0ad1d_0
+  - cryptography=3.4.7=py38ha5dfef3_0
+  - cudatoolkit=11.1.1=h6406543_8
+  - cycler=0.10.0=py_2
+  - defusedxml=0.7.1=pyhd8ed1ab_0
+  - entrypoints=0.3=pyhd8ed1ab_1003
+  - ffmpeg=4.3.1=hca11adc_2
+  - flake8=3.9.0=pyhd3eb1b0_0
+  - freetype=2.10.4=h0708190_1
+  - fsspec=2021.4.0=pyhd8ed1ab_0
+  - future=0.18.2=py38h578d9bd_3
+  - gettext=0.19.8.1=h0b5b191_1005
+  - gflags=2.2.2=he1b5a44_1004
+  - gitdb=4.0.7=pyhd8ed1ab_0
+  - gitpython=3.1.15=pyhd8ed1ab_0
+  - glog=0.4.0=h49b9bf7_3
+  - gmp=6.2.1=h58526e2_0
+  - gnutls=3.6.13=h85f3911_1
+  - google-auth=1.28.0=pyh44b312d_0
+  - google-auth-oauthlib=0.4.1=py_2
+  - grpc-cpp=1.37.0=h36de60a_1
+  - grpcio=1.37.0=py38hdd6454d_0
+  - idna=2.10=pyh9f0ad1d_0
+  - imageio=2.9.0=py_0
+  - imageio-ffmpeg=0.4.3=pyhd8ed1ab_0
+  - importlib-metadata=4.0.1=py38h578d9bd_0
+  - ipykernel=5.5.5=py38hd0cf306_0
+  - ipython=7.22.0=py38hd0cf306_0
+  - ipython_genutils=0.2.0=py_1
+  - ipywidgets=7.6.3=pyhd3deb0d_0
+  - jedi=0.18.0=py38h578d9bd_2
+  - jinja2=2.11.3=pyh44b312d_0
+  - jmespath=0.10.0=pyh9f0ad1d_0
+  - joblib=1.0.1=pyhd8ed1ab_0
+  - jpeg=9b=h024ee3a_2
+  - jsonschema=3.2.0=pyhd8ed1ab_3
+  - jupyter_client=6.1.12=pyhd8ed1ab_0
+  - jupyter_core=4.7.1=py38h578d9bd_0
+  - jupyterlab_pygments=0.1.2=pyh9f0ad1d_0
+  - jupyterlab_widgets=1.0.0=pyhd8ed1ab_1
+  - kiwisolver=1.3.1=py38h1fd1430_1
+  - krb5=1.17.2=h926e7f8_0
+  - lame=3.100=h7f98852_1001
+  - lcms2=2.12=h3be6417_0
+  - ld_impl_linux-64=2.35.1=hea4e1c9_2
+  - libcurl=7.76.1=hc4aaa36_1
+  - libedit=3.1.20191231=he28a2e2_2
+  - libev=4.33=h516909a_1
+  - libevent=2.1.10=hcdb4288_3
+  - libffi=3.3=h58526e2_2
+  - libflac=1.3.3=h9c3ff4c_1
+  - libgcc-ng=9.3.0=h2828fa1_19
+  - libgfortran-ng=7.5.0=h14aa051_19
+  - libgfortran4=7.5.0=h14aa051_19
+  - libiconv=1.16=h516909a_0
+  - libllvm10=10.0.1=he513fc3_3
+  - libnghttp2=1.43.0=h812cca2_0
+  - libogg=1.3.4=h7f98852_1
+  - libopus=1.3.1=h7f98852_1
+  - libpng=1.6.37=h21135ba_2
+  - libprotobuf=3.15.8=h780b84a_0
+  - librosa=0.8.0=pyh9f0ad1d_0
+  - libsndfile=1.0.31=h9c3ff4c_1
+  - libsodium=1.0.18=h36c2ea0_1
+  - libssh2=1.9.0=ha56f1ee_6
+  - libstdcxx-ng=9.3.0=h6de172a_19
+  - libthrift=0.14.1=he6d91bd_1
+  - libtiff=4.1.0=h2733197_1
+  - libutf8proc=2.6.1=h7f98852_0
+  - libuv=1.41.0=h7f98852_0
+  - libvorbis=1.3.7=h9c3ff4c_0
+  - llvm-openmp=11.1.0=h4bd325d_1
+  - llvmlite=0.36.0=py38h4630a5e_0
+  - lz4-c=1.9.3=h9c3ff4c_0
+  - markdown=3.3.4=pyhd8ed1ab_0
+  - markupsafe=1.1.1=py38h497a2fe_3
+  - matplotlib-base=3.4.1=py38hcc49a3a_0
+  - mccabe=0.6.1=py38_1
+  - mistune=0.8.4=py38h497a2fe_1003
+  - mkl=2020.4=h726a3e6_304
+  - mkl-service=2.3.0=py38h1e0a361_2
+  - mkl_fft=1.3.0=py38h5c078b8_1
+  - mkl_random=1.2.0=py38hc5bc63f_1
+  - multidict=5.1.0=py38h497a2fe_1
+  - nbclient=0.5.3=pyhd8ed1ab_0
+  - nbconvert=6.0.7=py38h578d9bd_3
+  - nbformat=5.1.3=pyhd8ed1ab_0
+  - ncurses=6.2=h58526e2_4
+  - nest-asyncio=1.5.1=pyhd8ed1ab_0
+  - nettle=3.6=he412f7d_0
+  - ninja=1.10.2=h4bd325d_0
+  - notebook=6.3.0=pyha770c72_1
+  - numba=0.53.1=py38h0e12cce_0
+  - numpy=1.19.2=py38h54aff64_0
+  - numpy-base=1.19.2=py38hfa32c7d_0
+  - oauthlib=3.0.1=py_0
+  - olefile=0.46=pyh9f0ad1d_1
+  - omegaconf=2.0.6=py38h578d9bd_0
+  - openh264=2.1.1=h780b84a_0
+  - openssl=1.1.1k=h7f98852_0
+  - orc=1.6.7=heec2584_1
+  - packaging=20.9=pyh44b312d_0
+  - pandas=1.2.4=py38h1abd341_0
+  - pandoc=2.12=h7f98852_0
+  - pandocfilters=1.4.2=py_1
+  - parquet-cpp=1.5.1=2
+  - parso=0.8.2=pyhd8ed1ab_0
+  - pexpect=4.8.0=pyh9f0ad1d_2
+  - pickleshare=0.7.5=py_1003
+  - pillow=8.2.0=py38he98fc37_0
+  - pip=21.1=pyhd8ed1ab_0
+  - pooch=1.3.0=pyhd8ed1ab_0
+  - prometheus_client=0.10.1=pyhd8ed1ab_0
+  - prompt-toolkit=3.0.18=pyha770c72_0
+  - protobuf=3.15.8=py38h709712a_0
+  - ptyprocess=0.7.0=pyhd3deb0d_0
+  - pyarrow=4.0.0=py38hc9229eb_0_cpu
+  - pyasn1=0.4.8=py_0
+  - pyasn1-modules=0.2.7=py_0
+  - pycodestyle=2.6.0=pyhd3eb1b0_0
+  - pycparser=2.20=pyh9f0ad1d_2
+  - pydeck=0.5.0=pyh9f0ad1d_0
+  - pyflakes=2.2.0=pyhd3eb1b0_0
+  - pygments=2.8.1=pyhd8ed1ab_0
+  - pyjwt=2.0.1=pyhd8ed1ab_1
+  - pyopenssl=20.0.1=pyhd8ed1ab_0
+  - pyparsing=2.4.7=pyh9f0ad1d_0
+  - pyrsistent=0.17.3=py38h497a2fe_2
+  - pysocks=1.7.1=py38h578d9bd_3
+  - pysoundfile=0.10.3.post1=pyhd3deb0d_0
+  - python=3.8.8=hffdb5ce_0_cpython
+  - python-dateutil=2.8.1=py_0
+  - python_abi=3.8=1_cp38
+  - pytorch=1.8.1=py3.8_cuda11.1_cudnn8.0.5_0
+  - pytorch-lightning=1.2.10=pyhd8ed1ab_0
+  - pytz=2021.1=pyhd8ed1ab_0
+  - pyyaml=5.4.1=py38h497a2fe_0
+  - pyzmq=22.0.3=py38h2035c66_1
+  - re2=2021.04.01=h9c3ff4c_0
+  - readline=8.1=h46c0cb4_0
+  - requests=2.25.1=pyhd3deb0d_0
+  - requests-oauthlib=1.3.0=pyh9f0ad1d_0
+  - resampy=0.2.2=py_0
+  - rsa=4.7.2=pyh44b312d_0
+  - s2n=1.0.0=h9b69904_0
+  - s3transfer=0.4.2=pyhd8ed1ab_0
+  - scikit-learn=0.24.1=py38ha9443f7_0
+  - scipy=1.6.2=py38h91f5cce_0
+  - send2trash=1.5.0=py_0
+  - setuptools=49.6.0=py38h578d9bd_3
+  - six=1.15.0=pyh9f0ad1d_0
+  - smmap=3.0.5=pyh44b312d_0
+  - snappy=1.1.8=he1b5a44_3
+  - sqlite=3.35.5=h74cdb3f_0
+  - streamlit=0.80.0=pyhd8ed1ab_0
+  - tensorboard=2.4.1=pyhd8ed1ab_0
+  - tensorboard-plugin-wit=1.8.0=pyh44b312d_0
+  - terminado=0.9.4=py38h578d9bd_0
+  - testpath=0.4.4=py_0
+  - threadpoolctl=2.1.0=pyh5ca1d4c_0
+  - tk=8.6.10=h21135ba_1
+  - toml=0.10.2=pyhd8ed1ab_0
+  - toolz=0.11.1=py_0
+  - torchaudio=0.8.1=py38
+  - torchmetrics=0.3.1=pyhd8ed1ab_0
+  - torchvision=0.9.1=py38_cu111
+  - tornado=6.1=py38h497a2fe_1
+  - tqdm=4.60.0=pyhd8ed1ab_0
+  - traitlets=5.0.5=py_0
+  - typing-extensions=3.7.4.3=0
+  - typing_extensions=3.7.4.3=py_0
+  - tzlocal=2.1=pyh9f0ad1d_0
+  - urllib3=1.26.4=pyhd8ed1ab_0
+  - validators=0.18.2=pyhd3deb0d_0
+  - watchdog=2.0.3=py38h578d9bd_0
+  - wcwidth=0.2.5=pyh9f0ad1d_2
+  - webencodings=0.5.1=py_1
+  - werkzeug=1.0.1=pyh9f0ad1d_0
+  - wheel=0.36.2=pyhd3deb0d_0
+  - widgetsnbextension=3.5.1=py38h578d9bd_4
+  - x264=1!161.3030=h7f98852_1
+  - xz=5.2.5=h516909a_1
+  - yaml=0.2.5=h516909a_0
+  - yarl=1.6.3=py38h497a2fe_1
+  - zeromq=4.3.4=h9c3ff4c_0
+  - zipp=3.4.1=pyhd8ed1ab_0
+  - zlib=1.2.11=h516909a_1010
+  - zstd=1.4.9=ha95c52a_0
+  - pip:
+    - albumentations==0.5.2
+    - decorator==4.4.2
+    - imgaug==0.4.0
+    - networkx==2.5.1
+    - opencv-python==4.1.2.30
+    - opencv-python-headless==4.5.1.48
+    - pywavelets==1.1.1
+    - scikit-image==0.18.1
+    - shapely==1.7.1
+    - test-tube==0.7.5
+    - tifffile==2021.4.8
diff --git a/configs/vas_codebook.yaml b/configs/vas_codebook.yaml
new file mode 100644
index 0000000..a4f7472
--- /dev/null
+++ b/configs/vas_codebook.yaml
@@ -0,0 +1,66 @@
+model:
+  base_learning_rate: 1e-6
+  target: specvqgan.models.vqgan.VQModel
+  params:
+    embed_dim: 256
+    n_embed: 128
+    ddconfig:
+      double_z: False
+      z_channels: 256
+      resolution: 848
+      in_channels: 1
+      out_ch: 1
+      ch: 128
+      ch_mult: [ 1,1,2,2,4]  # num_down = len(ch_mult)-1
+      num_res_blocks: 2
+      attn_resolutions: [53]
+      dropout: 0.0
+
+    lossconfig:
+      target: specvqgan.modules.losses.vqperceptual.VQLPAPSWithDiscriminator
+      params:
+        disc_conditional: False
+        disc_in_channels: 1
+        disc_start: 2001
+        disc_weight: 0.8
+        codebook_weight: 1.0
+        min_adapt_weight: 1.0
+        max_adapt_weight: 1.0
+        perceptual_weight: 1.0
+
+lightning:
+  callbacks:
+    image_logger:
+      target: train.ImageLogger
+      params:
+        for_specs: True
+        vocoder_cfg:
+          target: train.VocoderMelGan
+          params:
+            ckpt_vocoder: './vocoder/logs/vggsound/'
+  trainer:
+    sync_batchnorm: true
+
+data:
+  target: train.SpectrogramDataModuleFromConfig
+  params:
+    batch_size: 3
+    num_workers: 8
+
+    spec_dir_path: './data/vas/features/*/melspec_10s_22050hz'
+    sample_rate: 22050
+    mel_num: 80
+    spec_len: 860
+    spec_crop_len: 848
+    random_crop: False
+
+    train:
+      target: specvqgan.data.vas.VASSpecsTrain
+      params:
+        specs_dataset_cfg:
+          split_path: './data/vas_train.txt'
+    validation:
+      target: specvqgan.data.vas.VASSpecsValidation
+      params:
+        specs_dataset_cfg:
+          split_path: './data/vas_valid.txt'
diff --git a/configs/vas_transformer.yaml b/configs/vas_transformer.yaml
new file mode 100644
index 0000000..bf3747a
--- /dev/null
+++ b/configs/vas_transformer.yaml
@@ -0,0 +1,115 @@
+model:
+  base_learning_rate: 1e-6
+  target: specvqgan.models.cond_transformer.Net2NetTransformer
+  params:
+    cond_stage_key: feature
+    transformer_config:
+      target: specvqgan.modules.transformer.mingpt.GPTFeats
+      params:
+        feat_embedding_config:
+          target: torch.nn.Conv1d
+          params:
+            in_channels: 2048  # feat_depth
+            out_channels: 1024  # n_embd
+            kernel_size: 1
+            padding: 0
+        GPT_config:
+          vocab_size: 128
+          block_size: 477  # 53*5 + 212
+          n_layer: 24
+          n_head: 16
+          n_embd: 1024
+    first_stage_permuter_config:
+      target: specvqgan.modules.transformer.permuter.ColumnMajor
+      params:
+        H: 5  # mel_num, num of feats in specs / down_factor
+        W: 53  # cropped spec length / down_factor
+    first_stage_config:
+      target: specvqgan.models.vqgan.VQModel
+      params:
+        ckpt_path: 'Replace the first_stage ckpt path' # e.g. '2021-06-06T19-42-53_vas_specs_vqgan' or '2021-05-19T22-16-54_vggsound_specs_vqgan'
+        embed_dim: 256
+        n_embed: 128
+        ddconfig:
+          double_z: false
+          z_channels: 256
+          resolution: 848
+          in_channels: 1
+          out_ch: 1
+          ch: 128
+          ch_mult: [1, 1, 2, 2, 4]
+          num_res_blocks: 2
+          attn_resolutions: [53]
+          dropout: 0.0
+        lossconfig:
+          target: specvqgan.modules.losses.DummyLoss
+    # no permuter for the cond stage as the raw features is already a sequence
+    cond_stage_config:
+      target: specvqgan.modules.misc.raw_feats.RawFeatsStage
+
+lightning:
+  modelcheckpoint:
+    target: pytorch_lightning.callbacks.ModelCheckpoint
+    params:
+      monitor: val/loss
+      mode: min
+      save_last:
+  callbacks:
+    image_logger:
+      target: train.ImageLogger
+      params:
+        for_specs: True
+        vocoder_cfg:
+          target: train.VocoderMelGan
+          params:
+            ckpt_vocoder: './vocoder/logs/vggsound/'
+    early_stop_callback:
+      target: pytorch_lightning.callbacks.EarlyStopping
+      params:
+        monitor: val/loss
+        mode: min
+        min_delta: 0.00
+        patience: 2
+        verbose: True
+
+
+
+data:
+  target: train.ConditionedSpectrogramDataModuleFromConfig
+  params:
+    batch_size: 2
+    num_workers: 8
+
+    spec_dir_path: './data/vas/features/*/melspec_10s_22050hz'
+    sample_rate: 22050
+    mel_num: 80
+    spec_len: 860
+    spec_crop_len: 848
+    random_crop: False
+
+    rgb_feats_dir_path: './data/vas/features/*/feature_rgb_bninception_dim1024_21.5fps' # BNInception features
+    flow_feats_dir_path: './data/vas/features/*/feature_flow_bninception_dim1024_21.5fps' # BNInception features
+    # rgb_feats_dir_path: './data/vas/features/*/feature_resnet50_dim2048_21.5fps' # ResNet50 features
+    feat_depth: 2048
+    feat_len: 215
+    feat_crop_len: 212
+    feat_sampler_cfg:
+      target: specvqgan.data.vas.ResampleFrames
+      params:
+        feat_sample_size: 212  # will resample `feat_crop_len` evenly to match `feat_sample_size`
+        # times_to_repeat_after_resample: 5  # will duplicate each of `feat_sample_size` by this number
+
+    train:
+      target: specvqgan.data.vas.VASSpecsCondOnFeatsTrain
+      params:
+        specs_dataset_cfg:
+          split_path: './data/vas_train.txt'
+        condition_dataset_cfg:
+          split_path: './data/vas_train.txt'
+    validation:
+      target: specvqgan.data.vas.VASSpecsCondOnFeatsValidation
+      params:
+        specs_dataset_cfg:
+          split_path: './data/vas_valid.txt'
+        condition_dataset_cfg:
+          split_path: './data/vas_valid.txt'
diff --git a/configs/vggsound_codebook.yaml b/configs/vggsound_codebook.yaml
new file mode 100644
index 0000000..6d08a8c
--- /dev/null
+++ b/configs/vggsound_codebook.yaml
@@ -0,0 +1,68 @@
+model:
+  base_learning_rate: 4.5e-6
+  target: specvqgan.models.vqgan.VQModel
+  params:
+    embed_dim: 256
+    n_embed: 1024
+    ddconfig:
+      double_z: False
+      z_channels: 256
+      resolution: 848
+      in_channels: 1
+      out_ch: 1
+      ch: 128
+      ch_mult: [1, 1, 2, 2, 4]  # num_down = len(ch_mult)-1
+      num_res_blocks: 2
+      attn_resolutions: [53]
+      dropout: 0.0
+
+    lossconfig:
+      target: specvqgan.modules.losses.vqperceptual.VQLPAPSWithDiscriminator
+      params:
+        disc_conditional: False
+        disc_in_channels: 1
+        disc_start: 30001
+        disc_weight: 0.8
+        codebook_weight: 1.0
+        min_adapt_weight: 1.0
+        max_adapt_weight: 1.0
+        perceptual_weight: 1.0
+
+lightning:
+  callbacks:
+    image_logger:
+      target: train.ImageLogger
+      params:
+        for_specs: True
+        vocoder_cfg:
+          target: train.VocoderMelGan
+          params:
+            ckpt_vocoder: './vocoder/logs/vggsound/'
+  trainer:
+    sync_batchnorm: true
+
+data:
+  target: train.SpectrogramDataModuleFromConfig
+  params:
+    batch_size: 3
+    num_workers: 8
+
+    spec_dir_path: './data/vggsound/melspec_10s_22050hz'
+    sample_rate: 22050
+    mel_num: 80
+    spec_len: 860
+    spec_crop_len: 848
+    random_crop: False
+
+    train:
+      target: specvqgan.data.vggsound.VGGSoundSpecsTrain
+      params:
+        specs_dataset_cfg:
+    validation:
+      target: specvqgan.data.vggsound.VGGSoundSpecsValidation
+      params:
+        specs_dataset_cfg:
+    test:
+      target: specvqgan.data.vggsound.VGGSoundSpecsTest
+      params:
+        specs_dataset_cfg:
diff --git a/configs/vggsound_transformer.yaml b/configs/vggsound_transformer.yaml
new file mode 100644
index 0000000..90bc26b
--- /dev/null
+++ b/configs/vggsound_transformer.yaml
@@ -0,0 +1,118 @@
+model:
+  base_learning_rate: 5e-6
+  target: specvqgan.models.cond_transformer.Net2NetTransformer
+  params:
+    cond_stage_key: feature
+    transformer_config:
+      target: specvqgan.modules.transformer.mingpt.GPTFeats
+      params:
+        feat_embedding_config:
+          target: torch.nn.Conv1d # conv is used for convenience of applying the same FC at each position (kernel_size=1, padding=0) – donot change these params
+          params:
+            in_channels: 2048  # feat_depth
+            out_channels: 1024  # n_embd
+            kernel_size: 1
+            padding: 0
+        GPT_config:
+          vocab_size: 1024
+          block_size: 477  # 53*5 + how many frames (1)
+          n_layer: 24
+          n_head: 16
+          n_embd: 1024
+    first_stage_permuter_config:
+      target: specvqgan.modules.transformer.permuter.ColumnMajor
+      params:
+        H: 5  # mel_num, num of feats in specs / down_factor
+        W: 53  # cropped spec length / down_factor
+    first_stage_config:
+      target: specvqgan.models.vqgan.VQModel
+      params:
+        ckpt_path: 'Replace the first_stage ckpt path' # e.g. '2021-05-19T22-16-54_vggsound_codebook'
+        embed_dim: 256
+        n_embed: 1024
+        ddconfig:
+          double_z: false
+          z_channels: 256
+          resolution: 848
+          in_channels: 1
+          out_ch: 1
+          ch: 128
+          ch_mult: [1, 1, 2, 2, 4]
+          num_res_blocks: 2
+          attn_resolutions: [53]
+          dropout: 0.0
+        lossconfig:
+          target: specvqgan.modules.losses.DummyLoss
+    # no permuter for the cond stage as the raw features is already a sequence
+    cond_stage_config:
+      target: specvqgan.modules.misc.raw_feats.RawFeatsStage
+
+lightning:
+  modelcheckpoint:
+    target: pytorch_lightning.callbacks.ModelCheckpoint
+    params:
+      monitor: val/loss
+      mode: min
+      save_last:
+  callbacks:
+    image_logger:
+      target: train.ImageLogger
+      params:
+        for_specs: True
+        vocoder_cfg:
+          target: train.VocoderMelGan
+          params:
+            ckpt_vocoder: './vocoder/logs/vggsound/'
+    early_stop_callback:
+      target: pytorch_lightning.callbacks.EarlyStopping
+      params:
+        monitor: val/loss
+        mode: min
+        min_delta: 0.00
+        patience: 2
+        verbose: True
+
+
+data:
+  target: train.ConditionedSpectrogramDataModuleFromConfig
+  params:
+    batch_size: 2
+    num_workers: 8
+
+    spec_dir_path: './data/vggsound/melspec_10s_22050hz'
+    sample_rate: 22050
+    mel_num: 80
+    spec_len: 860
+    spec_crop_len: 848
+    random_crop: False
+
+    rgb_feats_dir_path: './data/vggsound/feature_rgb_bninception_dim1024_21.5fps' # BNInception features
+    flow_feats_dir_path: './data/vggsound/feature_flow_bninception_dim1024_21.5fps' # BNInception features
+    # rgb_feats_dir_path: './data/vggsound/feature_resnet50_dim2048_21.5fps' # ResNet50 features
+    feat_depth: 2048
+    feat_len: 215
+    feat_crop_len: 212
+    feat_sampler_cfg:
+      target: specvqgan.data.vggsound.ResampleFrames
+      params:
+        feat_sample_size: 212  # will resample `feat_crop_len` evenly to match `feat_sample_size`
+        # times_to_repeat_after_resample: 5  # will duplicate each of `feat_sample_size` by this number
+
+    train:
+      target: specvqgan.data.vggsound.VGGSoundSpecsCondOnFeatsTrain
+      params:
+        specs_dataset_cfg:
+        condition_dataset_cfg:
+          split_path: './data/vggsound_train.txt'
+    validation:
+      target: specvqgan.data.vggsound.VGGSoundSpecsCondOnFeatsValidation
+      params:
+        specs_dataset_cfg:
+        condition_dataset_cfg:
+          split_path: './data/vggsound_valid.txt'
+    test:
+      target: specvqgan.data.vggsound.VGGSoundSpecsCondOnFeatsTest
+      params:
+        specs_dataset_cfg:
+        condition_dataset_cfg:
+          split_path: './data/vggsound_test.txt'
diff --git a/data/available_clips.txt b/data/available_clips.txt
new file mode 100644
index 0000000..3636a36
--- /dev/null
+++ b/data/available_clips.txt
@@ -0,0 +1,190055 @@
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+--56QUhyDQM_185000_195000
+--5OkAjCI7g_40000_50000
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+--9O4XZOge4_30000_40000
+--Aa4M484QM_0_10000
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+--EquRwM9IQ_8000_18000
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diff --git a/data/download_vas_features.sh b/data/download_vas_features.sh
new file mode 100644
index 0000000..38c7377
--- /dev/null
+++ b/data/download_vas_features.sh
@@ -0,0 +1,51 @@
+#!/bin/bash
+
+if (! command -v tar &> /dev/null) | (! command -v curl &> /dev/null) | (! command -v md5sum &> /dev/null)
+then
+    echo "tar or curl or md5sum commands not found"
+    echo ""
+    echo "Please, install it first."
+    echo "If you cannot/dontwantto install these, you may download the features manually."
+    echo "You may find the links and correct paths in the repo README."
+    echo "Make sure to check the md5 sums after manual download."
+    echo "Extraction commands can be checked in this file."
+    exit
+fi
+
+download_check_expand_rmtar () {
+    # $1: $BASE_LINK
+    # $2: $FNAME
+    # $3: $WHERE_TO
+    # $4: $MD5SUM_GT
+    echo "Downloading" $2
+    curl $1/$2 --create-dirs -o $3/$2
+    echo "Checking tar md5sum"
+    cat $4 | grep $2 | md5sum --check
+    echo "Expanding tar"
+    tar xf $3/$2 -C $3
+    echo "Removing tar"
+    rm $3/$2
+    echo ""
+}
+
+WHERE_TO="./downloaded_features"
+BASE_LINK="https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/vas"
+MD5SUM_GT="./md5sum_vas.md5"
+
+strings=("dog" "fireworks" "drum" "baby" "gun" "sneeze" "cough" "hammer")
+for class in "${strings[@]}"; do
+    # spectrograms
+    FNAME="${class}_melspec_10s_22050hz.tar"
+    download_check_expand_rmtar $BASE_LINK $FNAME $WHERE_TO $MD5SUM_GT
+
+    # BN Inception Features
+    FNAME="${class}_feature_rgb_bninception_dim1024_21.5fps.tar"
+    download_check_expand_rmtar $BASE_LINK $FNAME $WHERE_TO $MD5SUM_GT
+
+    FNAME="${class}_feature_flow_bninception_dim1024_21.5fps.tar"
+    download_check_expand_rmtar $BASE_LINK $FNAME $WHERE_TO $MD5SUM_GT
+
+    # ResNet50 Features
+    # FNAME="${class}_feature_resnet50_dim2048_21.5fps.tar"
+    # download_check_expand_rmtar $BASE_LINK $FNAME $WHERE_TO $MD5SUM_GT
+done
diff --git a/data/download_vggsound_features.sh b/data/download_vggsound_features.sh
new file mode 100644
index 0000000..dfa264b
--- /dev/null
+++ b/data/download_vggsound_features.sh
@@ -0,0 +1,52 @@
+#!/bin/bash
+
+if (! command -v tar &> /dev/null) | (! command -v curl &> /dev/null) | (! command -v md5sum &> /dev/null)
+then
+    echo "tar or curl or md5sum commands not found"
+    echo ""
+    echo "Please, install it first."
+    echo "If you cannot/dontwantto install these, you may download the features manually."
+    echo "You may find the links and correct paths in the repo README."
+    echo "Make sure to check the md5 sums after manual download."
+    echo "Extraction commands can be checked in this file."
+    exit
+fi
+
+download_check_expand_rmtar () {
+    # $1: $BASE_LINK
+    # $2: $FNAME
+    # $3: $WHERE_TO
+    # $4: $MD5SUM_GT
+    echo "Downloading" $2
+    curl $1/$2 --create-dirs -o $3/$2
+    echo "Checking tar md5sum"
+    cat $4 | grep $2 | md5sum --check
+    echo "Expanding tar"
+    tar xf $3/$2 -C $3
+    echo "Removing tar"
+    rm $3/$2
+    echo ""
+}
+
+WHERE_TO="./downloaded_features"
+BASE_LINK="https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/vggsound"
+MD5SUM_GT="./md5sum_vggsound.md5"
+
+for i in {01..64}; do
+    echo "Starting" $i "/ 64 part"
+
+    # spectrograms
+    FNAME="melspec_10s_22050hz_$i.tar"
+    download_check_expand_rmtar $BASE_LINK $FNAME $WHERE_TO $MD5SUM_GT
+
+    # BN Inception Features
+    FNAME="feature_rgb_bninception_dim1024_21.5fps_$i.tar"
+    download_check_expand_rmtar $BASE_LINK $FNAME $WHERE_TO $MD5SUM_GT
+
+    FNAME="feature_flow_bninception_dim1024_21.5fps_$i.tar"
+    download_check_expand_rmtar $BASE_LINK $FNAME $WHERE_TO $MD5SUM_GT
+
+    # ResNet50 Features
+    # FNAME="feature_resnet50_dim2048_21.5fps_$i.tar"
+    # download_check_expand_rmtar $BASE_LINK $FNAME $WHERE_TO $MD5SUM_GT
+done
diff --git a/data/md5sum_vas.md5 b/data/md5sum_vas.md5
new file mode 100644
index 0000000..8f4bc1d
--- /dev/null
+++ b/data/md5sum_vas.md5
@@ -0,0 +1,32 @@
+c2ca29dc204b26f7f5874ce69f341082  ./downloaded_features/baby_feature_flow_bninception_dim1024_21.5fps.tar
+9aafcb00c963acf3af9dbbd323982e36  ./downloaded_features/baby_feature_rgb_bninception_dim1024_21.5fps.tar
+16744bae9f6ffecc23ec4a2d843ccf5d  ./downloaded_features/baby_feature_resnet50_dim2048_21.5fps.tar
+637c44028e14b9b2a402677391d15a7c  ./downloaded_features/baby_melspec_10s_22050hz.tar
+ad1043a90d5dcb4a4756c4a5b1bc50b6  ./downloaded_features/cough_feature_flow_bninception_dim1024_21.5fps.tar
+21ee13e9bf9320263124b1f8803b419b  ./downloaded_features/cough_feature_rgb_bninception_dim1024_21.5fps.tar
+5c3847f6686cae37645b1985317f2522  ./downloaded_features/cough_feature_resnet50_dim2048_21.5fps.tar
+a28a6d5c4870903e69c1862e56305c65  ./downloaded_features/cough_melspec_10s_22050hz.tar
+246fd75ca37a60e6fa77cbf9dd65c7d3  ./downloaded_features/dog_feature_flow_bninception_dim1024_21.5fps.tar
+adfbbf3c1735ba7bed5d56321b669834  ./downloaded_features/dog_feature_rgb_bninception_dim1024_21.5fps.tar
+5523026f873e5e495724be8f63819698  ./downloaded_features/dog_feature_resnet50_dim2048_21.5fps.tar
+b512318d5815539f018f4884747102f6  ./downloaded_features/dog_melspec_10s_22050hz.tar
+e1f5a289180a8852c12fef4051a2d4bd  ./downloaded_features/drum_feature_flow_bninception_dim1024_21.5fps.tar
+13a3378bfe154515f028bb1f45877e57  ./downloaded_features/drum_feature_rgb_bninception_dim1024_21.5fps.tar
+ec9e05362e82f2f980c4444626bad810  ./downloaded_features/drum_feature_resnet50_dim2048_21.5fps.tar
+7ad30685d7d0e9ae412fa031be9e08ce  ./downloaded_features/drum_melspec_10s_22050hz.tar
+8ac8a7141af2992f66e42ecdc121ebac  ./downloaded_features/fireworks_feature_flow_bninception_dim1024_21.5fps.tar
+8608b0eaf2f2731ddc18a0890d0d04ae  ./downloaded_features/fireworks_feature_rgb_bninception_dim1024_21.5fps.tar
+e45f1b05a954bd30b8086e47c2b29ba3  ./downloaded_features/fireworks_feature_resnet50_dim2048_21.5fps.tar
+f641ab60ef4db208562b524cdb05669a  ./downloaded_features/fireworks_melspec_10s_22050hz.tar
+1820ed55415b32aac78ddb585fbe3bce  ./downloaded_features/gun_feature_flow_bninception_dim1024_21.5fps.tar
+9863433bee12387863659b8b7f4d26c9  ./downloaded_features/gun_feature_rgb_bninception_dim1024_21.5fps.tar
+313b3ecb1d865c5d608990b9f36fc8bf  ./downloaded_features/gun_feature_resnet50_dim2048_21.5fps.tar
+8489604751cf42cb77033773769303b3  ./downloaded_features/gun_melspec_10s_22050hz.tar
+3a711830af0ea7d242bc5334d2e01393  ./downloaded_features/hammer_feature_flow_bninception_dim1024_21.5fps.tar
+c4c71ead5fd3ddb9eeaafa0ab6a991c4  ./downloaded_features/hammer_feature_rgb_bninception_dim1024_21.5fps.tar
+c78943a3203a7087b03ad5bf203aa1e3  ./downloaded_features/hammer_feature_resnet50_dim2048_21.5fps.tar
+04cecbbba97f5ce462469f06bc36607c  ./downloaded_features/hammer_melspec_10s_22050hz.tar
+d33007af5bb626438c372c2e0fcf4644  ./downloaded_features/sneeze_feature_flow_bninception_dim1024_21.5fps.tar
+e4c91d575883b62c0ef7781e9906daa6  ./downloaded_features/sneeze_feature_rgb_bninception_dim1024_21.5fps.tar
+bbd56e8927033f846b87a4fd3902cabc  ./downloaded_features/sneeze_feature_resnet50_dim2048_21.5fps.tar
+6db969f5ad69f667470d73e917d76ea6  ./downloaded_features/sneeze_melspec_10s_22050hz.tar
diff --git a/data/md5sum_vggsound.md5 b/data/md5sum_vggsound.md5
new file mode 100644
index 0000000..415d6e2
--- /dev/null
+++ b/data/md5sum_vggsound.md5
@@ -0,0 +1,256 @@
+9c5f1be4637cae16f307480de6778777  ./downloaded_features/feature_flow_bninception_dim1024_21.5fps_01.tar
+7da521897dc8b9d35d66cb9a8d7e9700  ./downloaded_features/feature_flow_bninception_dim1024_21.5fps_02.tar
+4ab2e881274e509d3b692afc41571aab  ./downloaded_features/feature_flow_bninception_dim1024_21.5fps_03.tar
+2bd5233ae5cfded4a6af26783d39848e  ./downloaded_features/feature_flow_bninception_dim1024_21.5fps_04.tar
+914e10e113603c45a323d684e95f6b99  ./downloaded_features/feature_flow_bninception_dim1024_21.5fps_05.tar
+021f9bcf231727579f4ff875711ecf87  ./downloaded_features/feature_flow_bninception_dim1024_21.5fps_06.tar
+b6b370da6cc2d5163875dd64b187fac9  ./downloaded_features/feature_flow_bninception_dim1024_21.5fps_07.tar
+8bd2913e0d332c90df3c2b9d94560c18  ./downloaded_features/feature_flow_bninception_dim1024_21.5fps_08.tar
+809523f98e122d994c11afef971441d7  ./downloaded_features/feature_flow_bninception_dim1024_21.5fps_09.tar
+17f2e99494f5c5d63850ea7286543d04  ./downloaded_features/feature_flow_bninception_dim1024_21.5fps_10.tar
+8b475816e47a79bf32f749af2c4a1cb2  ./downloaded_features/feature_flow_bninception_dim1024_21.5fps_11.tar
+4b90e7d5d6536620dbe902c723e6e882  ./downloaded_features/feature_flow_bninception_dim1024_21.5fps_12.tar
+4ebd26490df852fc0b75be018897ca3c  ./downloaded_features/feature_flow_bninception_dim1024_21.5fps_13.tar
+8e6fb49c652195d79ed9a60a586f9dd7  ./downloaded_features/feature_flow_bninception_dim1024_21.5fps_14.tar
+34ccac81f26d82794c32fa89491cf315  ./downloaded_features/feature_flow_bninception_dim1024_21.5fps_15.tar
+0940ed5678f673b7cc0b15c7406e422c  ./downloaded_features/feature_flow_bninception_dim1024_21.5fps_16.tar
+78e7b6ff02203da359aa11c3627981ab  ./downloaded_features/feature_flow_bninception_dim1024_21.5fps_17.tar
+a1162e85ae090bb2ff24dc7be1639cf1  ./downloaded_features/feature_flow_bninception_dim1024_21.5fps_18.tar
+ba9e74a0754d0eba69cf4b38f6feb7b9  ./downloaded_features/feature_flow_bninception_dim1024_21.5fps_19.tar
+a21695134fa68eba385ece55ef0f4d47  ./downloaded_features/feature_flow_bninception_dim1024_21.5fps_20.tar
+3cc049c353a7c27d095b0476d5fcf4e8  ./downloaded_features/feature_flow_bninception_dim1024_21.5fps_21.tar
+00d918275409e699d508c2cd17ad5bbb  ./downloaded_features/feature_flow_bninception_dim1024_21.5fps_22.tar
+1132cd528cb3fd0d8b400ed47f33e896  ./downloaded_features/feature_flow_bninception_dim1024_21.5fps_23.tar
+e10e7015b556baa1c3b1eafc5fa3302f  ./downloaded_features/feature_flow_bninception_dim1024_21.5fps_24.tar
+c826c8d7b24f22720484d4631fed475b  ./downloaded_features/feature_flow_bninception_dim1024_21.5fps_25.tar
+764afa4217578fd021eacd23696a3e4a  ./downloaded_features/feature_flow_bninception_dim1024_21.5fps_26.tar
+b1b8c276b950cc5eef3645aaf762dcf4  ./downloaded_features/feature_flow_bninception_dim1024_21.5fps_27.tar
+3e11488de8cfd81870c0cf72a81b2e62  ./downloaded_features/feature_flow_bninception_dim1024_21.5fps_28.tar
+595bed7697bbfadba0db6992cd2feb1b  ./downloaded_features/feature_flow_bninception_dim1024_21.5fps_29.tar
+9f72c96c9ce7b2b3829f43368b788a4f  ./downloaded_features/feature_flow_bninception_dim1024_21.5fps_30.tar
+fc5bda182f02034f2f9f379f36f787c6  ./downloaded_features/feature_flow_bninception_dim1024_21.5fps_31.tar
+f82f8fcddac79d5e44823a31b68a6ce4  ./downloaded_features/feature_flow_bninception_dim1024_21.5fps_32.tar
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diff --git a/data/neural_audio_codec/README.txt b/data/neural_audio_codec/README.txt
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+Examples and results of others are taken from SoundStream [2107.03312] project page:
+https://google-research.github.io/seanet/soundstream/examples/
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diff --git a/data/vas_valid.txt b/data/vas_valid.txt
new file mode 100644
index 0000000..6e3c29c
--- /dev/null
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diff --git a/data/vggsound.csv b/data/vggsound.csv
new file mode 100644
index 0000000..dbcbf5b
--- /dev/null
+++ b/data/vggsound.csv
@@ -0,0 +1,199176 @@
+---g-f_I2yQ,1,people marching,test
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+--CC5pH97q4,33,foghorn,train
+--CudrykwoE,67,cricket chirping,train
+--CudrykwoE,78,cricket chirping,train
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+--DTR0_mIGI,32,people battle cry,train
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+-1Vd89MCLJU,514,footsteps on snow,train
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+-1WK72M4xeg,220,driving motorcycle,train
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+-23CeprtibU,30,chainsawing trees,test
+-25e5qcELvw,11,printer printing,train
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+-26aVYRtEAc,30,female singing,train
+-26kjr5MxSI,0,chicken clucking,train
+-28N8VI2emM,76,underwater bubbling,train
+-28cdRYzR4w,65,child singing,train
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+-2Fbw2plADY,30,people whispering,train
+-2HXm-Rj3lg,283,people nose blowing,train
+-2JAK9CdWvY,435,slot machine,train
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+-2LHurwr5CQ,45,"subway, metro, underground",train
+-2LHurwr5CQ,95,"subway, metro, underground",train
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+-2LovF10IwM,179,ice cracking,train
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+-2X5YwLlICg,2,door slamming,train
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+-2poGMEmbbk,98,using sewing machines,train
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+-2qJQqXbtZs,42,vacuum cleaner cleaning floors,train
+-2qJQqXbtZs,128,vacuum cleaner cleaning floors,train
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+-32HIdBGdjo,78,"vehicle horn, car horn, honking",train
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+BaoD5mW9bnQ,678,wood thrush calling,train
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diff --git a/data/vggsound/melspec_10s_22050hz/-Qowmc0P9ic_34000_44000_mel.npy b/data/vggsound/melspec_10s_22050hz/-Qowmc0P9ic_34000_44000_mel.npy
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diff --git a/data/vggsound/video/-Qowmc0P9ic_34000_44000.mp4 b/data/vggsound/video/-Qowmc0P9ic_34000_44000.mp4
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diff --git a/data/vggsound_test.txt b/data/vggsound_test.txt
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diff --git a/data/vggsound_train.txt b/data/vggsound_train.txt
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index 0000000..a956df6
--- /dev/null
+++ b/data/vggsound_train.txt
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diff --git a/data/vggsound_valid.txt b/data/vggsound_valid.txt
new file mode 100644
index 0000000..8291426
--- /dev/null
+++ b/data/vggsound_valid.txt
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diff --git a/evaluate.py b/evaluate.py
new file mode 100644
index 0000000..1bd2aaf
--- /dev/null
+++ b/evaluate.py
@@ -0,0 +1,235 @@
+'''
+Adapted from `https://github.com/toshas/torch-fidelity`.
+Modified by Vladimir Iashin, 2021.
+'''
+import itertools
+import multiprocessing
+import os
+from pathlib import Path
+from specvqgan.util import get_ckpt_path
+
+import torch
+import torch.distributed as dist
+import torchvision
+from omegaconf import OmegaConf
+from omegaconf.dictconfig import DictConfig
+from omegaconf.listconfig import ListConfig
+from torch.utils.data import DataLoader
+from torch.utils.data.distributed import DistributedSampler
+from tqdm import tqdm
+
+from evaluation.metrics.fid import calculate_fid
+from evaluation.metrics.isc import calculate_isc
+from evaluation.metrics.kid import calculate_kid
+from evaluation.metrics.kl import calculate_kl
+from train import instantiate_from_config
+
+
+def patch_cfg_for_new_paths(nested_dict, patch):
+    if patch is None:
+        print('Nothing to patch')
+        return nested_dict
+    if not isinstance(nested_dict, (dict, DictConfig)):
+        # print('Ignoring the in patching', nested_dict, type(nested_dict))
+        return nested_dict
+    for key, value in nested_dict.items():
+        if isinstance(value, (dict, DictConfig)):
+            nested_dict[key] = patch_cfg_for_new_paths(value, patch)
+        elif isinstance(value, (list, ListConfig)):
+            for i, element in enumerate(value):
+                value[i] = patch_cfg_for_new_paths(element, patch)
+        else:
+            if key in patch:
+                print(f'Patched {key}: {nested_dict[key]} --> {patch[key]}')
+                nested_dict[key] = patch.get(key, nested_dict[key])
+    return nested_dict
+
+def get_dataset_class(dataset_cfg):
+    if 'target' in dataset_cfg:
+        dataset_class = instantiate_from_config(dataset_cfg)
+    elif 'path_to_exp' in dataset_cfg:
+        dataset_class = instantiate_from_config(dataset_cfg.exp_dataset)
+        dataset_class.prepare_data()
+        dataset_class.setup()
+        dataset_class = dataset_class.datasets[dataset_cfg.key]
+    else:
+        raise NotImplementedError
+
+    return dataset_class
+
+
+def get_featuresdict(feat_extractor, device, dataset_cfg, is_ddp, batch_size, save_cpu_ram):
+    input = get_dataset_class(dataset_cfg)
+    # for debugging
+    # input.specs_dataset.dataset = input.specs_dataset.dataset[:1000]
+    # input.feats_dataset.dataset = input.feats_dataset.dataset[:1000]
+    batch_size = min(batch_size, len(input))
+
+    if dataset_cfg.transform_dset_out_to_inception_in is not None:
+        transforms = [instantiate_from_config(c) for c in dataset_cfg.transform_dset_out_to_inception_in]
+    else:
+        transforms = [lambda x: x]
+    transforms = torchvision.transforms.Compose(transforms)
+
+    if is_ddp:
+        sampler = DistributedSampler(input, dist.get_world_size(), dist.get_rank(), shuffle=False)
+        num_workers = 0
+    else:
+        sampler = None
+        num_workers = 0 if save_cpu_ram else min(8, 2 * multiprocessing.cpu_count())
+
+    dataloader = DataLoader(
+        input,
+        batch_size,
+        sampler=sampler,
+        drop_last=False,
+        num_workers=num_workers,
+        pin_memory=device != 'cpu',
+    )
+
+    out = None
+    out_meta = None
+
+    for batch in tqdm(dataloader):
+        # saving batch meta so that we could merge predictions from both datasets when
+        # pair-wise KL is calculated
+        # comenting out target and label, because those are asigned by folder name, not original labels
+        metadict = {
+            # 'target': batch['target'].cpu().tolist(),
+            # 'label': batch['label'],
+            'file_path_': batch.get('file_path_', batch.get('file_path_specs_')),
+        }
+        batch = transforms(batch)
+        batch = batch.to(device)
+
+        with torch.no_grad():
+            features = feat_extractor(batch)
+
+        featuresdict = feat_extractor.convert_features_tuple_to_dict(features)
+        featuresdict = {k: [v.cpu()] for k, v in featuresdict.items()}
+
+        if out is None:
+            out = featuresdict
+        else:
+            out = {k: out[k] + featuresdict[k] for k in out.keys()}
+
+        if out_meta is None:
+            out_meta = metadict
+        else:
+            out_meta = {k: out_meta[k] + metadict[k] for k in out_meta.keys()}
+
+    out = {k: torch.cat(v, dim=0) for k, v in out.items()}
+
+    # we need to send the results from all ranks to one of them (gather). Otherwise, the metric is calculated
+    # only on the subset of data that one worker had
+    if is_ddp:
+        for k, v in out.items():
+            gather_out = [None for worker in range(dist.get_world_size())]
+            dist.all_gather_object(gather_out, v)
+            out[k] = torch.cat(gather_out)
+        for k, v in out_meta.items():
+            gather_out_meta = [None for worker in range(dist.get_world_size())]
+            dist.all_gather_object(gather_out_meta, v)
+            # just flattens the list
+            out_meta[k] = list(itertools.chain(*gather_out_meta))
+    # merging both dicts key-wise
+    out = {**out, **out_meta}
+    return out
+
+def main():
+    torch.manual_seed(0)
+    local_rank = os.environ.get('LOCAL_RANK')
+
+    cfg_cli = OmegaConf.from_cli()
+    cfg_eval = OmegaConf.load(cfg_cli.config)
+
+    # the latter arguments are prioritized
+    for dataset in ['input1', 'input2']:
+        cli_dataset_cfg = cfg_cli[dataset]
+        # first I check if the path_to_exp is specified in CLI args
+        if cli_dataset_cfg is not None:
+            if cli_dataset_cfg.path_to_exp is not None:
+                cfg_paths = Path(cli_dataset_cfg.path_to_exp).glob('configs/*-project.yaml')
+                cfgs_dataset = [OmegaConf.load(p) for p in sorted(list(cfg_paths))]
+                cfg_eval[dataset].exp_dataset = OmegaConf.merge(*cfgs_dataset).data
+            else:
+                assert cli_dataset_cfg.params.root is not None, 'path_to_exp or root should be specified'
+        # if not specified in CLI, I will check the default config
+        else:
+            eval_dataset_cfg = cfg_eval[dataset]
+            if eval_dataset_cfg.path_to_exp is not None:
+                cfg_paths = Path(eval_dataset_cfg.path_to_exp).glob('configs/*-project.yaml')
+                cfgs_dataset = [OmegaConf.load(p) for p in sorted(list(cfg_paths))]
+                cfg_eval[dataset].exp_dataset = OmegaConf.merge(*cfgs_dataset).data
+            else:
+                assert eval_dataset_cfg.params.root is not None, 'path_to_exp or root should be specified'
+    cfg = OmegaConf.merge(cfg_eval, cfg_cli)
+    cfg = patch_cfg_for_new_paths(cfg, cfg.patch)
+
+    assert cfg.have_isc or cfg.have_fid or cfg.have_kid, 'Select at least one metric'
+    assert (not cfg.have_fid) and (not cfg.have_kid) or cfg.input2 is not None, 'Two inputs are required'
+
+    if local_rank is not None:
+        is_ddp = True
+        local_rank = int(local_rank)
+        dist.init_process_group(cfg.get('dist_backend', 'nccl'), 'env://')
+        print(f'WORLDSIZE {dist.get_world_size()} – RANK {dist.get_rank()}')
+        if dist.get_rank() == 0:
+            print('MASTER:', os.environ['MASTER_ADDR'], ':', os.environ['MASTER_PORT'])
+            print(OmegaConf.to_yaml(cfg))
+    else:
+        is_ddp = False
+        local_rank = cfg.device[-1]  # extracting last elements from e.g. 'cuda:0'[-1]
+        print(OmegaConf.to_yaml(cfg))
+
+    device = torch.device(f'cuda:{local_rank}')
+    torch.cuda.set_device(device)
+
+    # downloading the checkpoint for melception
+    get_ckpt_path('melception', 'evaluation/logs/21-05-10T09-28-40')
+
+    feat_extractor = instantiate_from_config(cfg.feature_extractor)
+    feat_extractor.eval()
+    feat_extractor.to(device)
+
+    out = {}
+
+    print('Extracting features from input_1')
+    featuresdict_1 = get_featuresdict(feat_extractor, device, cfg.input1, is_ddp, **cfg.extraction_cfg)
+
+    featuresdict_2 = None
+    if cfg.input2 not in ['None', None, 'null', 'none']:
+        print('Extracting features from input_2')
+        featuresdict_2 = get_featuresdict(feat_extractor, device, cfg.input2, is_ddp, **cfg.extraction_cfg)
+
+    # pickle.dump(featuresdict_1, open('feats1.pkl', 'wb'))
+    # pickle.dump(featuresdict_2, open('feats2.pkl', 'wb'))
+
+    if cfg.have_kl:
+        metric_kl = calculate_kl(featuresdict_1, featuresdict_2, **cfg.kl_cfg)
+        out.update(metric_kl)
+    if cfg.have_isc:
+        metric_isc = calculate_isc(featuresdict_1, **cfg.isc_cfg)
+        out.update(metric_isc)
+    if cfg.have_fid:
+        metric_fid = calculate_fid(featuresdict_1, featuresdict_2, **cfg.fid_cfg)
+        out.update(metric_fid)
+    if cfg.have_kid:
+        metric_kid = calculate_kid(featuresdict_1, featuresdict_2, **cfg.kid_cfg)
+        out.update(metric_kid)
+
+    print('\n'.join((f'{k}: {v:.7f}' for k, v in out.items())))
+
+    # just pretty printing of the results, nothing more
+    print(
+        f'{cfg.input1.get("path_to_exp", Path(cfg.input1.params.root).parent.stem)}:',
+        f'KL: {out.get("kullback_leibler_divergence", float("nan")):8.5f};',
+        f'ISc: {out.get("inception_score_mean", float("nan")):8.5f} ({out.get("inception_score_std", float("nan")):5f});',
+        f'FID: {out.get("frechet_inception_distance", float("nan")):8.5f};',
+        f'KID: {out.get("kernel_inception_distance_mean", float("nan")):.5f}',
+        f'({out.get("kernel_inception_distance_std", float("nan")):.5f})'
+    )
+
+
+if __name__ == '__main__':
+    main()
diff --git a/evaluation/configs/eval_melception_vas.yaml b/evaluation/configs/eval_melception_vas.yaml
new file mode 100644
index 0000000..41c507f
--- /dev/null
+++ b/evaluation/configs/eval_melception_vas.yaml
@@ -0,0 +1,67 @@
+device: 'cuda:0'
+
+have_isc: True
+have_fid: True
+have_kid: True
+have_kl: True
+
+isc_cfg:
+  feat_layer_name: 'logits_unbiased'
+  splits: 10
+  samples_shuffle: True
+  rng_seed: 2020
+fid_cfg:
+  feat_layer_name: '2048'
+kid_cfg:
+  feat_layer_name: '2048'
+  subsets: 100
+  subset_size: 1000
+  degree: 3
+  gamma: None
+  coef0: 1
+  rng_seed: 2020
+kl_cfg:
+  feat_layer_name: 'logits'
+  dataset_name: 'vas'
+  classes: ['baby', 'cough', 'dog', 'drum', 'fireworks', 'gun', 'hammer', 'sneeze']
+
+extraction_cfg:
+  batch_size: 2
+  save_cpu_ram: False
+
+feature_extractor: # vggsound-inception-v3     features_list: ['logits_unbiased', '2048', 'logits']
+  target: evaluation.feature_extractors.melception.Melception
+  params:
+    num_classes: 309  # The number of classes in inception
+    features_list: ['logits_unbiased', '2048', 'logits']
+    feature_extractor_weights_path: ./evaluation/logs/21-05-10T09-28-40/melception-21-05-10T09-28-40.pt
+
+input1:
+  target: evaluation.datasets.fakes.FakesFolder
+  params:
+    root: 'Please specify the path to generated samples' # ./logs/2021-06-09T15-17-18_vas_resampleframes_transformer/samples_2021-06-14T10-43-53/VAS_validation
+    extensions: .npy
+  transform_dset_out_to_inception_in:
+    - target: evaluation.datasets.transforms.GetInputFromBatchByKey
+      params:
+        input_key: image
+    - target: specvqgan.modules.losses.vggishish.transforms.StandardNormalizeAudio
+      params:
+        specs_dir: ./data/vggsound/melspec_10s_22050hz # should be vggsound – the dataset inception was trained on.
+        cache_path: ./specvqgan/modules/losses/vggishish/data/
+    - target: evaluation.datasets.transforms.ToFloat32
+
+input2:
+  path_to_exp: ./logs/2021-06-09T15-17-18_vas_resampleframes_transformer
+  key: validation
+  batch_size: 1
+  transform_dset_out_to_inception_in:
+    - target: evaluation.datasets.transforms.FromMinusOneOneToZeroOne
+    - target: specvqgan.modules.losses.vggishish.transforms.StandardNormalizeAudio
+      params:
+        specs_dir: ./data/vggsound/melspec_10s_22050hz # should be vggsound – the dataset inception was trained on.
+        cache_path: ./specvqgan/modules/losses/vggishish/data/
+    - target: evaluation.datasets.transforms.GetInputFromBatchByKey
+      params:
+        input_key: image
+    - target: evaluation.datasets.transforms.ToFloat32
diff --git a/evaluation/configs/eval_melception_vggsound.yaml b/evaluation/configs/eval_melception_vggsound.yaml
new file mode 100644
index 0000000..f90e9ae
--- /dev/null
+++ b/evaluation/configs/eval_melception_vggsound.yaml
@@ -0,0 +1,78 @@
+device: 'cuda:0'
+
+have_isc: True
+have_fid: True
+have_kid: True
+have_kl: True
+
+isc_cfg:
+  feat_layer_name: 'logits_unbiased'
+  splits: 10
+  samples_shuffle: True
+  rng_seed: 2020
+fid_cfg:
+  feat_layer_name: '2048'
+kid_cfg:
+  feat_layer_name: '2048'
+  subsets: 100
+  subset_size: 1000
+  degree: 3
+  gamma: None
+  coef0: 1
+  rng_seed: 2020
+kl_cfg:
+  feat_layer_name: 'logits'
+  dataset_name: 'vggsound'
+
+extraction_cfg:
+  batch_size: 64
+  save_cpu_ram: False
+
+feature_extractor: # vggsound-inception-v3     features_list: ['logits_unbiased', '2048', 'logits']
+  target: evaluation.feature_extractors.melception.Melception
+  params:
+    num_classes: 309
+    features_list: ['logits_unbiased', '2048', 'logits']
+    feature_extractor_weights_path: ./evaluation/logs/21-05-10T09-28-40/melception-21-05-10T09-28-40.pt
+
+# input1:
+#   path_to_exp: ./logs/2021-04-28T07-07-03_vggsound_rawfeats_transformer
+#   key: validation
+#   transform_dset_out_to_inception_in:
+#     - target: evaluation.datasets.transforms.FromMinusOneOneToZeroOne # image
+#     - target: specvqgan.modules.losses.vggishish.transforms.StandardNormalizeAudio  # input
+#       params:
+#         specs_dir: ./data/vggsound/melspec_10s_22050hz
+#         cache_path: ./specvqgan/modules/losses/vggishish/data/
+#     - target: evaluation.datasets.transforms.GetInputFromBatchByKey # image
+#       params:
+#         input_key: image
+#     - target: evaluation.datasets.transforms.ToFloat32
+input1:
+  target: evaluation.datasets.fakes.FakesFolder
+  params:
+    root: 'Please specify the path to generated samples `input1.params.root=??`' # ./logs/2021-06-03T09-34-10_vggsound_resampleframes_transformer/samples_2021-06-11T20-55-54/VGGSound_test
+    extensions: .npy
+  transform_dset_out_to_inception_in:
+    - target: evaluation.datasets.transforms.GetInputFromBatchByKey
+      params:
+        input_key: image
+    - target: specvqgan.modules.losses.vggishish.transforms.StandardNormalizeAudio
+      params:
+        specs_dir: ./data/vggsound/melspec_10s_22050hz
+        cache_path: ./specvqgan/modules/losses/vggishish/data/
+    - target: evaluation.datasets.transforms.ToFloat32
+
+input2:
+  path_to_exp: ./logs/2021-06-03T09-34-10_vggsound_resampleframes_transformer
+  key: test
+  transform_dset_out_to_inception_in:
+    - target: evaluation.datasets.transforms.FromMinusOneOneToZeroOne
+    - target: specvqgan.modules.losses.vggishish.transforms.StandardNormalizeAudio
+      params:
+        specs_dir: ./data/vggsound/melspec_10s_22050hz
+        cache_path: ./specvqgan/modules/losses/vggishish/data/
+    - target: evaluation.datasets.transforms.GetInputFromBatchByKey
+      params:
+        input_key: image
+    - target: evaluation.datasets.transforms.ToFloat32
diff --git a/evaluation/configs/sampler.yaml b/evaluation/configs/sampler.yaml
new file mode 100644
index 0000000..03eb6d0
--- /dev/null
+++ b/evaluation/configs/sampler.yaml
@@ -0,0 +1,10 @@
+sampler:
+  splits: ['test']  # vggsound: ['train', 'validation', 'test'], vas: ['train', 'validation']
+  batch_size: 32
+  num_workers: 4
+  temperature: 1.0
+  sample_next_tok_from_pred_dist: True
+  top_k: 100
+  sampling_mode: 'nopix'  # nopix or half (given half of the encoded groundtruth tokens)
+  samples_per_video: 1
+  model_logdir: 'Please specify path to log directory e.g. ./logs/your_experiment'
diff --git a/evaluation/datasets/cifar.py b/evaluation/datasets/cifar.py
new file mode 100644
index 0000000..b8d2684
--- /dev/null
+++ b/evaluation/datasets/cifar.py
@@ -0,0 +1,42 @@
+import sys
+from contextlib import redirect_stdout
+
+import torch
+import torchvision
+from PIL import Image
+from torchvision.datasets import CIFAR10
+from train import instantiate_from_config
+
+
+class TransformPILtoRGBTensor:
+    def __call__(self, img):
+        assert type(img) is Image.Image, 'Input is not a PIL.Image'
+        assert isinstance(img, Image.Image), 'Input is not a PIL.Image'
+        width, height = img.size
+        img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())).view(height, width, 3)
+        img = img.permute(2, 0, 1)
+        return img
+
+class Cifar10_RGB(CIFAR10):
+    def __init__(self, transforms_cfg, cifar_cfg):
+        with redirect_stdout(sys.stderr):
+            super().__init__(**cifar_cfg)
+        self.transform = torchvision.transforms.Compose([instantiate_from_config(c) for c in transforms_cfg])
+
+    def __getitem__(self, index):
+        img, target = super().__getitem__(index)
+        return img
+
+# class ImagesPathDataset(Dataset):
+#     def __init__(self, files, transforms=None):
+#         self.files = files
+#         self.transforms = TransformPILtoRGBTensor() if transforms is None else transforms
+
+#     def __len__(self):
+#         return len(self.files)
+
+#     def __getitem__(self, i):
+#         path = self.files[i]
+#         img = Image.open(path).convert('RGB')
+#         img = self.transforms(img)
+#         return img
diff --git a/evaluation/datasets/fakes.py b/evaluation/datasets/fakes.py
new file mode 100644
index 0000000..ea9bc85
--- /dev/null
+++ b/evaluation/datasets/fakes.py
@@ -0,0 +1,93 @@
+import sys
+import librosa
+from typing import Any, Callable, Optional, Tuple
+
+import numpy as np
+from torchvision.datasets import DatasetFolder
+
+sys.path.insert(0, '.')  # nopep8
+from train import instantiate_from_config
+from feature_extraction.extract_mel_spectrogram import TRANSFORMS
+
+
+def read_wav(path, folder_name='melspec_10s_22050hz', length=22050*10):
+    wav, _ = librosa.load(path, sr=None)
+    # this cannot be a transform without creating a huge overhead with inserting audio_name in each
+    y = np.zeros(length)
+    if wav.shape[0] < length:
+        y[:len(wav)] = wav
+    else:
+        y = wav[:length]
+    if folder_name == 'melspec_10s_22050hz':
+        mel_spec = TRANSFORMS(y)
+    else:
+        raise NotImplementedError
+    return mel_spec
+
+
+class FakesFolder(DatasetFolder):
+
+    def __init__(self,
+                 root: str,
+                 loader: Callable = None,
+                 extensions: Optional[Tuple[str, ...]] = None,
+                 transform: Optional[Callable] = None,
+                 target_transform: Optional[Callable] = None,
+                 is_valid_file: Optional[Callable[[str], bool]] = None) -> None:
+        assert isinstance(extensions, str)
+
+        if loader is None:
+            loader = self.sample_from_path
+        if target_transform is None:
+            target_transform = self.target_transform
+
+        super().__init__(root, loader, extensions=extensions, transform=transform,
+                         target_transform=target_transform, is_valid_file=is_valid_file)
+        # self.idx_to_class = {v: k for k, v in self.class_to_idx.items()}
+
+    def __getitem__(self, index: int) -> Tuple[Any, Any]:
+        # super().__getitem__(index) returns (sample, target) – we need only sample
+        path, target = self.samples[index]
+        # comenting out target and label, because those are asigned by folder name, not original labels
+        return {
+            # 'target': target,
+            # 'label': self.idx_to_class[target],
+            'file_path_': path,
+            'image': super().__getitem__(index)[0],
+        }
+
+    @staticmethod
+    def sample_from_path(path):
+        if path.endswith('.wav'):
+            return read_wav(path)
+        else:
+            return np.load(path)
+
+    @staticmethod
+    def target_transform(target):
+        return None
+
+
+if __name__ == '__main__':
+    # input1:
+    # target: evaluation.datasets.fakes.FakesFolder
+    # params:
+    #     root: ./logs/2021-06-03T09-34-10_vggsound_resampleframes_transformer/samples_2021-06-11T20-55-54/VGGSound_test
+    #     extensions: .npy
+    #     transform:
+    #     - target: evaluation.datasets.transforms.GetInputFromBatchByKey
+    #       params:
+    #         input_key: image
+    #     - target: specvqgan.modules.losses.vggishish.transforms.StandardNormalizeAudio
+    #         params:
+    #         specs_dir: ./data/vggsound/melspec_10s_22050hz
+    #         cache_path: ./specvqgan/modules/losses/vggishish/data/
+    from omegaconf import OmegaConf
+
+    # cfg = OmegaConf.load('./evaluation/configs/eval_melception_vas.yaml')
+    # cfg.input1.params.root = './logs/2021-06-09T15-17-18_vas_resampleframes_transformer/samples_2021-06-14T10-43-53/VAS_validation'
+    cfg = OmegaConf.load('./evaluation/configs/eval_melception_vggsound.yaml')
+    cfg.input1.params.root = './logs/2021-06-03T09-34-10_vggsound_resampleframes_transformer/samples_2021-06-11T20-55-54/VGGSound_test'
+    data = instantiate_from_config(cfg.input1)
+    print(len(data))
+    print(data[0])
diff --git a/evaluation/datasets/transforms.py b/evaluation/datasets/transforms.py
new file mode 100644
index 0000000..6f35016
--- /dev/null
+++ b/evaluation/datasets/transforms.py
@@ -0,0 +1,35 @@
+import torch
+from specvqgan.modules.losses.vggishish.transforms import Crop
+
+class FromMinusOneOneToZeroOne(object):
+    '''Actually, it doesnot do [-1, 1] --> [0, 1] as promised. It would, if inputs would be in [-1, 1]
+    but reconstructed specs are not.'''
+
+    def __call__(self, item):
+        item['image'] = (item['image'] + 1) / 2
+        return item
+
+class CropNoDict(Crop):
+
+    def __init__(self, cropped_shape, random_crop=None):
+        super().__init__(cropped_shape=cropped_shape, random_crop=random_crop)
+
+    def __call__(self, x):
+        # albumentations expect an ndarray of size (H, W, ...) but we have tensor of size (B, H, W).
+        # we will assume that the batch-dim (B) is out "channel" dim and permute it to the end.
+        # Finally, we change the type back to Torch.Tensor.
+        x = self.preprocessor(image=x.permute(1, 2, 0).numpy())['image'].transpose(2, 0, 1)
+        return torch.from_numpy(x)
+
+class GetInputFromBatchByKey(object):
+
+    def __init__(self, input_key):
+        self.input_key = input_key
+
+    def __call__(self, item):
+        return item[self.input_key]
+
+class ToFloat32(object):
+
+    def __call__(self, item):
+        return item.float()
diff --git a/evaluation/feature_extractors/inception3.py b/evaluation/feature_extractors/inception3.py
new file mode 100644
index 0000000..ed0e1f1
--- /dev/null
+++ b/evaluation/feature_extractors/inception3.py
@@ -0,0 +1,490 @@
+'''
+Adapted from `https://github.com/pytorch/vision`.
+Modified by Vladimir Iashin, 2021.
+'''
+import math
+import sys
+from contextlib import redirect_stdout
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from omegaconf.listconfig import ListConfig
+from torch.hub import load_state_dict_from_url
+from torch.nn.modules.utils import _ntuple
+from torchvision.models.inception import BasicConv2d
+from torchvision.models.inception import Inception3 as TorchVisionInception3
+
+
+class FeatureExtractorInceptionV3(TorchVisionInception3):
+
+    def __init__(self, name, features_list, feature_extractor_weights_path=None, **kwargs):
+        """Build pretrained InceptionV3
+
+        Parameters
+        ----------
+        features_list: list
+            A list of feature names from the list of provided by this extractor,
+            which will be produced for each input
+        feature_extractor_weights_path: str
+            Path to the pretrained Inception model weights in PyTorch format.
+            Refer to inception_features.py:__main__ for making your own.
+            By default downloads the checkpoint from internet.
+        """
+        super().__init__(num_classes=1008, **kwargs)
+        self.input_image_size = 299
+        self.provided_feats = ('64', '192', '768', '2048', 'logits_unbiased', 'logits')
+        self.features_list = list(features_list)
+
+        assert type(name) is str, 'Feature extractor name must be a string'
+        assert type(features_list) in (list, tuple, ListConfig), 'Wrong features list type'
+        assert all((a in self.provided_feats for a in features_list)), 'Requested features arent on the list'
+        assert len(features_list) == len(set(features_list)), 'Duplicate features requested'
+
+        PT_INCEPTION_URL = \
+            'https://github.com/mseitzer/pytorch-fid/releases/download/fid_weights/pt_inception-2015-12-05-6726825d.pth'
+        self.Mixed_5b = InceptionA(192, pool_features=32)
+        self.Mixed_5c = InceptionA(256, pool_features=64)
+        self.Mixed_5d = InceptionA(288, pool_features=64)
+        self.Mixed_6b = InceptionC(768, channels_7x7=128)
+        self.Mixed_6c = InceptionC(768, channels_7x7=160)
+        self.Mixed_6d = InceptionC(768, channels_7x7=160)
+        self.Mixed_6e = InceptionC(768, channels_7x7=192)
+
+        self.Mixed_7b = InceptionE_1(1280)
+        self.Mixed_7c = InceptionE_2(2048)
+        self.AuxLogits = nn.Identity()
+
+        if feature_extractor_weights_path is None:
+            with redirect_stdout(sys.stderr):
+                state_dict = load_state_dict_from_url(PT_INCEPTION_URL, progress=True)
+        else:
+            state_dict = torch.load(feature_extractor_weights_path)
+        self.load_state_dict(state_dict)
+
+        for p in self.parameters():
+            p.requires_grad_(False)
+
+    def forward(self, x):
+        assert torch.is_tensor(x) and x.dtype == torch.uint8, 'Expecting x as torch.Tensor, dtype=torch.uint8'
+        features = {}
+        remaining_features = self.features_list.copy()
+
+        x = x.float()
+        # N x 3 x ? x ?
+
+        x = interpolate_bilinear_2d_like_tensorflow1x(
+            x,
+            size=(self.input_image_size, self.input_image_size),
+            align_corners=False,
+        )
+        # N x 3 x 299 x 299
+
+        # x = (x - 128) * torch.tensor(0.0078125, dtype=torch.float32, device=x.device)  # happening in graph
+        x = (x - 128) / 128  # but this gives bit-exact output _of this step_ too
+        # N x 3 x 299 x 299
+
+        x = self.Conv2d_1a_3x3(x)
+        # N x 32 x 149 x 149
+        x = self.Conv2d_2a_3x3(x)
+        # N x 32 x 147 x 147
+        x = self.Conv2d_2b_3x3(x)
+        # N x 64 x 147 x 147
+        x = self.maxpool1(x)
+        # N x 64 x 73 x 73
+
+        if '64' in remaining_features:
+            features['64'] = F.adaptive_avg_pool2d(x, output_size=(1, 1))
+            remaining_features.remove('64')
+            if len(remaining_features) == 0:
+                return tuple(features[a] for a in self.features_list)
+
+        x = self.Conv2d_3b_1x1(x)
+        # N x 80 x 73 x 73
+        x = self.Conv2d_4a_3x3(x)
+        # N x 192 x 71 x 71
+        x = self.maxpool2(x)
+        # N x 192 x 35 x 35
+
+        if '192' in remaining_features:
+            features['192'] = F.adaptive_avg_pool2d(x, output_size=(1, 1))
+            remaining_features.remove('192')
+            if len(remaining_features) == 0:
+                return tuple(features[a] for a in self.features_list)
+
+        x = self.Mixed_5b(x)
+        # N x 256 x 35 x 35
+        x = self.Mixed_5c(x)
+        # N x 288 x 35 x 35
+        x = self.Mixed_5d(x)
+        # N x 288 x 35 x 35
+        x = self.Mixed_6a(x)
+        # N x 768 x 17 x 17
+        x = self.Mixed_6b(x)
+        # N x 768 x 17 x 17
+        x = self.Mixed_6c(x)
+        # N x 768 x 17 x 17
+        x = self.Mixed_6d(x)
+        # N x 768 x 17 x 17
+        x = self.Mixed_6e(x)
+        # N x 768 x 17 x 17
+
+        if '768' in remaining_features:
+            features['768'] = F.adaptive_avg_pool2d(x, output_size=(1, 1))
+            remaining_features.remove('768')
+            if len(remaining_features) == 0:
+                return tuple(features[a] for a in self.features_list)
+
+        x = self.Mixed_7a(x)
+        # N x 1280 x 8 x 8
+        x = self.Mixed_7b(x)
+        # N x 2048 x 8 x 8
+        x = self.Mixed_7c(x)
+        # N x 2048 x 8 x 8
+        x = self.avgpool(x)
+        # N x 2048 x 1 x 1
+
+        x = torch.flatten(x, 1)
+        # N x 2048
+
+        if '2048' in remaining_features:
+            features['2048'] = x
+            remaining_features.remove('2048')
+            if len(remaining_features) == 0:
+                return tuple(features[a] for a in self.features_list)
+
+        if 'logits_unbiased' in remaining_features:
+            x = x.mm(self.fc.weight.T)
+            # N x 1008 (num_classes)
+            features['logits_unbiased'] = x
+            remaining_features.remove('logits_unbiased')
+            if len(remaining_features) == 0:
+                return tuple(features[a] for a in self.features_list)
+
+            x = x + self.fc.bias.unsqueeze(0)
+        else:
+            x = self.fc(x)
+            # N x 1008 (num_classes)
+
+        features['logits'] = x
+        return tuple(features[a] for a in self.features_list)
+
+    @staticmethod
+    def get_provided_features_list():
+        return
+
+    def get_requested_features_list(self):
+        return self.features_list
+
+    def get_name(self):
+        return self.name
+
+    def convert_features_tuple_to_dict(self, features):
+        """
+        The only compound return type of the forward function amenable to JIT tracing is tuple.
+        This function simply helps to recover the mapping.
+        """
+        message = 'Features must be the output of forward function'
+        assert type(features) is tuple and len(features) == len(self.features_list), message
+        return dict(((name, feature) for name, feature in zip(self.features_list, features)))
+
+
+class InceptionA(nn.Module):
+    """Block from torchvision patched to be compatible with TensorFlow implementation"""
+    def __init__(self, in_channels, pool_features):
+        super(InceptionA, self).__init__()
+        self.branch1x1 = BasicConv2d(in_channels, 64, kernel_size=1)
+
+        self.branch5x5_1 = BasicConv2d(in_channels, 48, kernel_size=1)
+        self.branch5x5_2 = BasicConv2d(48, 64, kernel_size=5, padding=2)
+
+        self.branch3x3dbl_1 = BasicConv2d(in_channels, 64, kernel_size=1)
+        self.branch3x3dbl_2 = BasicConv2d(64, 96, kernel_size=3, padding=1)
+        self.branch3x3dbl_3 = BasicConv2d(96, 96, kernel_size=3, padding=1)
+
+        self.branch_pool = BasicConv2d(in_channels, pool_features, kernel_size=1)
+
+    def forward(self, x):
+        branch1x1 = self.branch1x1(x)
+
+        branch5x5 = self.branch5x5_1(x)
+        branch5x5 = self.branch5x5_2(branch5x5)
+
+        branch3x3dbl = self.branch3x3dbl_1(x)
+        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
+        branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
+
+        # Patch: Tensorflow's average pool does not use the padded zero's in its average calculation
+        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1, count_include_pad=False)
+        branch_pool = self.branch_pool(branch_pool)
+
+        outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool]
+        return torch.cat(outputs, 1)
+
+
+class InceptionC(nn.Module):
+    """Block from torchvision patched to be compatible with TensorFlow implementation"""
+    def __init__(self, in_channels, channels_7x7):
+        super(InceptionC, self).__init__()
+        self.branch1x1 = BasicConv2d(in_channels, 192, kernel_size=1)
+
+        c7 = channels_7x7
+        self.branch7x7_1 = BasicConv2d(in_channels, c7, kernel_size=1)
+        self.branch7x7_2 = BasicConv2d(c7, c7, kernel_size=(1, 7), padding=(0, 3))
+        self.branch7x7_3 = BasicConv2d(c7, 192, kernel_size=(7, 1), padding=(3, 0))
+
+        self.branch7x7dbl_1 = BasicConv2d(in_channels, c7, kernel_size=1)
+        self.branch7x7dbl_2 = BasicConv2d(c7, c7, kernel_size=(7, 1), padding=(3, 0))
+        self.branch7x7dbl_3 = BasicConv2d(c7, c7, kernel_size=(1, 7), padding=(0, 3))
+        self.branch7x7dbl_4 = BasicConv2d(c7, c7, kernel_size=(7, 1), padding=(3, 0))
+        self.branch7x7dbl_5 = BasicConv2d(c7, 192, kernel_size=(1, 7), padding=(0, 3))
+
+        self.branch_pool = BasicConv2d(in_channels, 192, kernel_size=1)
+
+    def forward(self, x):
+        branch1x1 = self.branch1x1(x)
+
+        branch7x7 = self.branch7x7_1(x)
+        branch7x7 = self.branch7x7_2(branch7x7)
+        branch7x7 = self.branch7x7_3(branch7x7)
+
+        branch7x7dbl = self.branch7x7dbl_1(x)
+        branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
+        branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
+        branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
+        branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)
+
+        # Patch: Tensorflow's average pool does not use the padded zero's in its average calculation
+        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1, count_include_pad=False)
+        branch_pool = self.branch_pool(branch_pool)
+
+        outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]
+        return torch.cat(outputs, 1)
+
+
+class InceptionE_1(nn.Module):
+    """First InceptionE block from torchvision patched to be compatible with TensorFlow implementation"""
+    def __init__(self, in_channels):
+        super(InceptionE_1, self).__init__()
+        self.branch1x1 = BasicConv2d(in_channels, 320, kernel_size=1)
+
+        self.branch3x3_1 = BasicConv2d(in_channels, 384, kernel_size=1)
+        self.branch3x3_2a = BasicConv2d(384, 384, kernel_size=(1, 3), padding=(0, 1))
+        self.branch3x3_2b = BasicConv2d(384, 384, kernel_size=(3, 1), padding=(1, 0))
+
+        self.branch3x3dbl_1 = BasicConv2d(in_channels, 448, kernel_size=1)
+        self.branch3x3dbl_2 = BasicConv2d(448, 384, kernel_size=3, padding=1)
+        self.branch3x3dbl_3a = BasicConv2d(384, 384, kernel_size=(1, 3), padding=(0, 1))
+        self.branch3x3dbl_3b = BasicConv2d(384, 384, kernel_size=(3, 1), padding=(1, 0))
+
+        self.branch_pool = BasicConv2d(in_channels, 192, kernel_size=1)
+
+    def forward(self, x):
+        branch1x1 = self.branch1x1(x)
+
+        branch3x3 = self.branch3x3_1(x)
+        branch3x3 = [
+            self.branch3x3_2a(branch3x3),
+            self.branch3x3_2b(branch3x3),
+        ]
+        branch3x3 = torch.cat(branch3x3, 1)
+
+        branch3x3dbl = self.branch3x3dbl_1(x)
+        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
+        branch3x3dbl = [
+            self.branch3x3dbl_3a(branch3x3dbl),
+            self.branch3x3dbl_3b(branch3x3dbl),
+        ]
+        branch3x3dbl = torch.cat(branch3x3dbl, 1)
+
+        # Patch: Tensorflow's average pool does not use the padded zero's in its average calculation
+        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1, count_include_pad=False)
+        branch_pool = self.branch_pool(branch_pool)
+
+        outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
+        return torch.cat(outputs, 1)
+
+
+class InceptionE_2(nn.Module):
+    """Second InceptionE block from torchvision patched to be compatible with TensorFlow implementation"""
+    def __init__(self, in_channels):
+        super(InceptionE_2, self).__init__()
+        self.branch1x1 = BasicConv2d(in_channels, 320, kernel_size=1)
+
+        self.branch3x3_1 = BasicConv2d(in_channels, 384, kernel_size=1)
+        self.branch3x3_2a = BasicConv2d(384, 384, kernel_size=(1, 3), padding=(0, 1))
+        self.branch3x3_2b = BasicConv2d(384, 384, kernel_size=(3, 1), padding=(1, 0))
+
+        self.branch3x3dbl_1 = BasicConv2d(in_channels, 448, kernel_size=1)
+        self.branch3x3dbl_2 = BasicConv2d(448, 384, kernel_size=3, padding=1)
+        self.branch3x3dbl_3a = BasicConv2d(384, 384, kernel_size=(1, 3), padding=(0, 1))
+        self.branch3x3dbl_3b = BasicConv2d(384, 384, kernel_size=(3, 1), padding=(1, 0))
+
+        self.branch_pool = BasicConv2d(in_channels, 192, kernel_size=1)
+
+    def forward(self, x):
+        branch1x1 = self.branch1x1(x)
+
+        branch3x3 = self.branch3x3_1(x)
+        branch3x3 = [
+            self.branch3x3_2a(branch3x3),
+            self.branch3x3_2b(branch3x3),
+        ]
+        branch3x3 = torch.cat(branch3x3, 1)
+
+        branch3x3dbl = self.branch3x3dbl_1(x)
+        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
+        branch3x3dbl = [
+            self.branch3x3dbl_3a(branch3x3dbl),
+            self.branch3x3dbl_3b(branch3x3dbl),
+        ]
+        branch3x3dbl = torch.cat(branch3x3dbl, 1)
+
+        # Patch: TensorFlow Inception model uses max pooling instead of average
+        # pooling. This is likely an error in this specific Inception
+        # implementation, as other Inception models use average pooling here
+        # (which matches the description in the paper).
+        branch_pool = F.max_pool2d(x, kernel_size=3, stride=1, padding=1)
+        branch_pool = self.branch_pool(branch_pool)
+
+        outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
+        return torch.cat(outputs, 1)
+
+
+def interpolate_bilinear_2d_like_tensorflow1x(
+    input,
+    size=None,
+    scale_factor=None,
+    align_corners=None,
+    method='slow'
+):
+    r"""Down/up samples the input to either the given :attr:`size` or the given :attr:`scale_factor`
+
+    Epsilon-exact bilinear interpolation as it is implemented in TensorFlow 1.x:
+    https://github.com/tensorflow/tensorflow/blob/f66daa493e7383052b2b44def2933f61faf196e0/tensorflow/core/kernels/image_resizer_state.h#L41
+    https://github.com/tensorflow/tensorflow/blob/6795a8c3a3678fb805b6a8ba806af77ddfe61628/tensorflow/core/kernels/resize_bilinear_op.cc#L85
+    as per proposal:
+    https://github.com/pytorch/pytorch/issues/10604#issuecomment-465783319
+
+    Related materials:
+    https://hackernoon.com/how-tensorflows-tf-image-resize-stole-60-days-of-my-life-aba5eb093f35
+    https://jricheimer.github.io/tensorflow/2019/02/11/resize-confusion/
+    https://machinethink.net/blog/coreml-upsampling/
+
+    Currently only 2D spatial sampling is supported, i.e. expected inputs are 4-D in shape.
+
+    The input dimensions are interpreted in the form:
+    `mini-batch x channels x height x width`.
+
+    Args:
+        input (Tensor): the input tensor
+        size (Tuple[int, int]): output spatial size.
+        scale_factor (float or Tuple[float]): multiplier for spatial size. Has to match input size if it is a tuple.
+        align_corners (bool, optional): Same meaning as in TensorFlow 1.x.
+        method (str, optional):
+            'slow' (1e-4 L_inf error on GPU, bit-exact on CPU, with checkerboard 32x32->299x299), or
+            'fast' (1e-3 L_inf error on GPU and CPU, with checkerboard 32x32->299x299)
+    """
+    if method not in ('slow', 'fast'):
+        raise ValueError('how_exact can only be one of "slow", "fast"')
+
+    if input.dim() != 4:
+        raise ValueError('input must be a 4-D tensor')
+
+    if align_corners is None:
+        raise ValueError('align_corners is not specified (use this function for a complete determinism)')
+
+    def _check_size_scale_factor(dim):
+        if size is None and scale_factor is None:
+            raise ValueError('either size or scale_factor should be defined')
+        if size is not None and scale_factor is not None:
+            raise ValueError('only one of size or scale_factor should be defined')
+        if scale_factor is not None and isinstance(scale_factor, tuple) and len(scale_factor) != dim:
+            raise ValueError('scale_factor shape must match input shape. '
+                             'Input is {}D, scale_factor size is {}'.format(dim, len(scale_factor)))
+
+    is_tracing = torch._C._get_tracing_state()
+
+    def _output_size(dim):
+        _check_size_scale_factor(dim)
+        if size is not None:
+            if is_tracing:
+                return [torch.tensor(i) for i in size]
+            else:
+                return size
+        scale_factors = _ntuple(dim)(scale_factor)
+        # math.floor might return float in py2.7
+
+        # make scale_factor a tensor in tracing so constant doesn't get baked in
+        if is_tracing:
+            return [
+                (torch.floor((input.size(i + 2).float() *
+                 torch.tensor(scale_factors[i], dtype=torch.float32)).float()))
+                for i in range(dim)
+            ]
+        else:
+            return [int(math.floor(float(input.size(i + 2)) * scale_factors[i])) for i in range(dim)]
+
+    def tf_calculate_resize_scale(in_size, out_size):
+        if align_corners:
+            if is_tracing:
+                return (in_size - 1) / (out_size.float() - 1).clamp(min=1)
+            else:
+                return (in_size - 1) / max(1, out_size - 1)
+        else:
+            if is_tracing:
+                return in_size / out_size.float()
+            else:
+                return in_size / out_size
+
+    out_size = _output_size(2)
+    scale_x = tf_calculate_resize_scale(input.shape[3], out_size[1])
+    scale_y = tf_calculate_resize_scale(input.shape[2], out_size[0])
+
+    def resample_using_grid_sample():
+        grid_x = torch.arange(0, out_size[1], 1, dtype=input.dtype, device=input.device)
+        grid_x = grid_x * (2 * scale_x / (input.shape[3] - 1)) - 1
+
+        grid_y = torch.arange(0, out_size[0], 1, dtype=input.dtype, device=input.device)
+        grid_y = grid_y * (2 * scale_y / (input.shape[2] - 1)) - 1
+
+        grid_x = grid_x.view(1, out_size[1]).repeat(out_size[0], 1)
+        grid_y = grid_y.view(out_size[0], 1).repeat(1, out_size[1])
+
+        grid_xy = torch.cat((grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)), dim=2).unsqueeze(0)
+        grid_xy = grid_xy.repeat(input.shape[0], 1, 1, 1)
+
+        out = F.grid_sample(input, grid_xy, mode='bilinear', padding_mode='border', align_corners=True)
+        return out
+
+    def resample_manually():
+        grid_x = torch.arange(0, out_size[1], 1, dtype=input.dtype, device=input.device)
+        grid_x = grid_x * torch.tensor(scale_x, dtype=torch.float32)
+        grid_x_lo = grid_x.long()
+        grid_x_hi = (grid_x_lo + 1).clamp_max(input.shape[3] - 1)
+        grid_dx = grid_x - grid_x_lo.float()
+
+        grid_y = torch.arange(0, out_size[0], 1, dtype=input.dtype, device=input.device)
+        grid_y = grid_y * torch.tensor(scale_y, dtype=torch.float32)
+        grid_y_lo = grid_y.long()
+        grid_y_hi = (grid_y_lo + 1).clamp_max(input.shape[2] - 1)
+        grid_dy = grid_y - grid_y_lo.float()
+
+        # could be improved with index_select
+        in_00 = input[:, :, grid_y_lo, :][:, :, :, grid_x_lo]
+        in_01 = input[:, :, grid_y_lo, :][:, :, :, grid_x_hi]
+        in_10 = input[:, :, grid_y_hi, :][:, :, :, grid_x_lo]
+        in_11 = input[:, :, grid_y_hi, :][:, :, :, grid_x_hi]
+
+        in_0 = in_00 + (in_01 - in_00) * grid_dx.view(1, 1, 1, out_size[1])
+        in_1 = in_10 + (in_11 - in_10) * grid_dx.view(1, 1, 1, out_size[1])
+        out = in_0 + (in_1 - in_0) * grid_dy.view(1, 1, out_size[0], 1)
+
+        return out
+
+    if method == 'slow':
+        out = resample_manually()
+    else:
+        out = resample_using_grid_sample()
+
+    return out
diff --git a/evaluation/feature_extractors/melception.py b/evaluation/feature_extractors/melception.py
new file mode 100644
index 0000000..96b3024
--- /dev/null
+++ b/evaluation/feature_extractors/melception.py
@@ -0,0 +1,122 @@
+import torch
+import torch.nn.functional as F
+from torchvision.models.inception import BasicConv2d, Inception3
+
+class Melception(Inception3):
+
+    def __init__(self, num_classes, features_list, feature_extractor_weights_path, ** kwargs):
+        # inception = Melception(num_classes=309)
+        super().__init__(num_classes=num_classes, init_weights=True, **kwargs)
+        self.features_list = list(features_list)
+        # the same as https://github.com/pytorch/vision/blob/5339e63148/torchvision/models/inception.py#L95
+        # but for 1-channel input instead of RGB.
+        self.Conv2d_1a_3x3 = BasicConv2d(1, 32, kernel_size=3, stride=2)
+        # also the 'hight' of the mel spec is 80 (vs 299 in RGB) we remove all max pool from Inception
+        self.maxpool1 = torch.nn.Identity()
+        self.maxpool2 = torch.nn.Identity()
+
+        state_dict = torch.load(feature_extractor_weights_path, map_location='cpu')
+        self.load_state_dict(state_dict['model'])
+        for p in self.parameters():
+            p.requires_grad_(False)
+
+    def forward(self, x):
+        features = {}
+        remaining_features = self.features_list.copy()
+
+        # B x 1 x 80 x 848 <- N x M x T
+        x = x.unsqueeze(1)
+        # (B, 32, 39, 423) <-
+        x = self.Conv2d_1a_3x3(x)
+        # (B, 32, 37, 421) <-
+        x = self.Conv2d_2a_3x3(x)
+        # (B, 64, 37, 421) <-
+        x = self.Conv2d_2b_3x3(x)
+        # (B, 64, 37, 421) <-
+        x = self.maxpool1(x)
+
+        if '64' in remaining_features:
+            features['64'] = F.adaptive_avg_pool2d(x, output_size=(1, 1))
+            remaining_features.remove('64')
+            if len(remaining_features) == 0:
+                return tuple(features[a] for a in self.features_list)
+
+        # (B, 80, 37, 421) <-
+        x = self.Conv2d_3b_1x1(x)
+        # (B, 192, 35, 419) <-
+        x = self.Conv2d_4a_3x3(x)
+        # (B, 192, 35, 419) <-
+        x = self.maxpool2(x)
+
+        if '192' in remaining_features:
+            features['192'] = F.adaptive_avg_pool2d(x, output_size=(1, 1))
+            remaining_features.remove('192')
+            if len(remaining_features) == 0:
+                return tuple(features[a] for a in self.features_list)
+
+        # (B, 256, 35, 419) <-
+        x = self.Mixed_5b(x)
+        # (B, 288, 35, 419) <-
+        x = self.Mixed_5c(x)
+        # (B, 288, 35, 419) <-
+        x = self.Mixed_5d(x)
+        # (B, 288, 35, 419) <-
+        x = self.Mixed_6a(x)
+        # (B, 768, 17, 209) <-
+        x = self.Mixed_6b(x)
+        # (B, 768, 17, 209) <-
+        x = self.Mixed_6c(x)
+        # (B, 768, 17, 209) <-
+        x = self.Mixed_6d(x)
+        # (B, 768, 17, 209) <-
+        x = self.Mixed_6e(x)
+
+        if '768' in remaining_features:
+            features['768'] = F.adaptive_avg_pool2d(x, output_size=(1, 1))
+            remaining_features.remove('768')
+            if len(remaining_features) == 0:
+                return tuple(features[a] for a in self.features_list)
+
+        # (B, 1280, 8, 104) <-
+        x = self.Mixed_7a(x)
+        # (B, 2048, 8, 104) <-
+        x = self.Mixed_7b(x)
+        # (B, 2048, 8, 104) <-
+        x = self.Mixed_7c(x)
+        # (B, 2048, 1, 1) <-
+        x = self.avgpool(x)
+        # (B, 2048, 1, 1) <-
+        x = self.dropout(x)
+
+        # (B, 2048) <-
+        x = torch.flatten(x, 1)
+
+        if '2048' in remaining_features:
+            features['2048'] = x
+            remaining_features.remove('2048')
+            if len(remaining_features) == 0:
+                return tuple(features[a] for a in self.features_list)
+
+        if 'logits_unbiased' in remaining_features:
+            # (B, num_classes) <-
+            x = x.mm(self.fc.weight.T)
+            features['logits_unbiased'] = x
+            remaining_features.remove('logits_unbiased')
+            if len(remaining_features) == 0:
+                return tuple(features[a] for a in self.features_list)
+
+            x = x + self.fc.bias.unsqueeze(0)
+        else:
+            x = self.fc(x)
+
+        features['logits'] = x
+        return tuple(features[a] for a in self.features_list)
+
+    def convert_features_tuple_to_dict(self, features):
+        """
+        The only compound return type of the forward function amenable to JIT tracing is tuple.
+        This function simply helps to recover the mapping.
+        """
+        message = 'Features must be the output of forward function'
+        assert type(features) is tuple and len(features) == len(self.features_list), message
+        return dict(((name, feature) for name, feature in zip(self.features_list, features)))
diff --git a/evaluation/generate_samples.py b/evaluation/generate_samples.py
new file mode 100644
index 0000000..2ab895b
--- /dev/null
+++ b/evaluation/generate_samples.py
@@ -0,0 +1,301 @@
+import datetime
+import os
+from torch.utils.data import DataLoader
+import torch.distributed as dist
+from torch.utils.data.distributed import DistributedSampler
+import sys
+from tqdm import tqdm
+import soundfile
+
+sys.path.insert(0, '.')  # nopep8
+from pathlib import Path
+
+import torch
+import numpy as np
+from omegaconf import OmegaConf
+from train import instantiate_from_config
+from copy import deepcopy
+
+def save_config(cfg):
+    cfg_to_save = deepcopy(cfg)
+    # remove sensitive info
+    for key in ['config_sampler', 'device_ids', 'nodes', 'node_id', 'master_port', 'master_addr']:
+        if key in cfg_to_save.sampler:
+            cfg_to_save.sampler.pop(key)
+    # make a path, dir and save the config
+    save_dir = Path(cfg_to_save.sampler.model_logdir) / f'samples_{cfg.sampler.now}'
+    os.makedirs(save_dir, exist_ok=True)
+    OmegaConf.save(cfg_to_save, save_dir / 'full_sampling_cfg.yaml')
+
+def load_and_save_config():
+    # cli arguments and configs
+    config_cli = OmegaConf.from_cli()
+    config_sampler = OmegaConf.load(config_cli.sampler.config_sampler)
+    cfg_cli_sampler = OmegaConf.merge(config_sampler, config_cli)
+    model_logdir = Path(cfg_cli_sampler.sampler.model_logdir)
+    assert Path(model_logdir).exists(), f'Specified: {model_logdir}'
+    # load configs from the model specified in model_logdir
+    # there could be several *-project.yaml configs e.g. because the experiment was resumed
+    configs_model_base = Path(model_logdir).glob('configs/*-project.yaml')
+    configs_lightning_base = Path(model_logdir).glob('configs/*-lightning.yaml')
+    configs_model_base = [OmegaConf.load(p) for p in configs_model_base]
+    configs_lightning_base = [OmegaConf.load(p) for p in configs_lightning_base]
+    # the latter arguments are prioritized
+    cfg = OmegaConf.merge(*configs_model_base, *configs_lightning_base, cfg_cli_sampler)
+    # patch cfg. E.g. if the model is trained on another machine with different paths
+    for a in ['spec_dir_path', 'rgb_feats_dir_path', 'flow_feats_dir_path']:
+        # TODO: `config_cli.data` is a bit ambigiuous because we can pass some other fields besides paths in data
+        if config_cli.data is None:
+            if cfg.data.params[a] is not None:
+                if 'vggsound.VGGSound' in cfg.data.params.train.target:
+                    cfg.data.params[a] = os.path.join('./data/vggsound/', Path(cfg.data.params[a]).name)
+                elif 'vas.VAS' in cfg.data.params.train.target:
+                    cfg.data.params[a] = os.path.join('./data/vas/features/*', Path(cfg.data.params[a]).name)
+
+    # save the config
+    save_config(cfg)
+    return cfg
+
+def make_folder_for_samples(cfg):
+    assert Path(cfg.sampler.model_logdir).exists()
+    dataset_name = cfg.data.params.train.target.split('.')[-1]
+    if 'VGGSound' in dataset_name:
+        dataset_name = 'VGGSound'
+    elif 'VAS' in dataset_name:
+        dataset_name = 'VAS'
+    else:
+        raise NotImplementedError
+    # init a folder with samples
+    samples_split_dirs = {}
+    for split in cfg.sampler.splits:
+        samples_split_dirs[split] = Path(cfg.sampler.model_logdir) / f'samples_{cfg.sampler.now}' / f'{dataset_name}_{split}'
+        os.makedirs(samples_split_dirs[split], exist_ok=True)
+    return samples_split_dirs
+
+
+def load_model_and_dataloaders(cfg, device, is_ddp=False):
+    # find the checkpoint path
+    # e.g. checkpoints have names `epoch_000012.ckpt`
+    if (Path(cfg.sampler.model_logdir) / 'checkpoints/last.ckpt').exists():
+        ckpt_model = Path(cfg.sampler.model_logdir) / 'checkpoints/last.ckpt'
+    else:
+        ckpt_model = sorted(Path(cfg.sampler.model_logdir).glob('checkpoints/*.ckpt'))[-1]
+    # assert not (Path(cfg.sampler.model_logdir) / 'checkpoints/last.ckpt').exists()
+    print(f'Going to use the checkpoint from {ckpt_model}')
+
+    # get data
+    dsets = instantiate_from_config(cfg.data)
+    dsets.prepare_data()
+    dsets.setup()
+    dsets.datasets = {split: dset for split, dset in dsets.datasets.items() if split in cfg.sampler.splits}
+
+    # loading the vocoder
+    # ckpt_vocoder = cfg.lightning.callbacks.image_logger.params.vocoder_cfg.params.ckpt_vocoder
+    # if ckpt_vocoder:
+    #     vocoder = load_vocoder(ckpt_vocoder, eval_mode=True)['model'].to('cuda')
+    vocoder = None
+
+    # now load the specified checkpoint
+    if ckpt_model:
+        pl_sd = torch.load(ckpt_model, map_location='cpu')
+        global_step = pl_sd['global_step']
+    else:
+        pl_sd = {'state_dict': None}
+        global_step = None
+
+    if 'ckpt_path' in cfg.model.params:
+        cfg.model.params.ckpt_path = None
+    if 'downsample_cond_size' in cfg.model.params:
+        cfg.model.params.downsample_cond_size = -1
+        cfg.model.params['downsample_cond_factor'] = 0.5
+    try:
+        if 'ckpt_path' in cfg.model.params.first_stage_config.params:
+            cfg.model.params.first_stage_config.params.ckpt_path = None
+        if 'ckpt_path' in cfg.model.params.cond_stage_config.params:
+            cfg.model.params.cond_stage_config.params.ckpt_path = None
+    except Exception:
+        pass
+    model = instantiate_from_config(cfg.model)
+    if pl_sd['state_dict'] is not None:
+        missing, unexpected = model.load_state_dict(pl_sd['state_dict'], strict=False)
+        print(f'Missing fields: {missing}')
+        print(f'Unexpected fields: {unexpected}')
+    model.to(device)
+    model.eval()
+
+    assert not model.training, 'The model is in "training" mode'
+
+    if is_ddp:
+        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[device.index])
+
+    # defining data loaders in a dict
+    dloaders = {}
+    for split, dset in dsets.datasets.items():
+        if is_ddp:
+            sampler = DistributedSampler(dset, dist.get_world_size(), dist.get_rank(), shuffle=False)
+            num_workers = 0
+        else:
+            sampler = None
+            num_workers = cfg.sampler.num_workers
+        dloaders[split] = DataLoader(dset, cfg.sampler.batch_size, sampler=sampler, num_workers=num_workers,
+                                     pin_memory=True, drop_last=False)
+    return dloaders, model, vocoder, global_step
+
+def sample_spectrogram(cfg, model, batch):
+    if isinstance(model, torch.nn.parallel.DistributedDataParallel):
+        model = model.module
+
+    with torch.no_grad():
+        # if we use VQGAN (for ablation study only), sampling is pretty straight-forward
+        if 'VQModel' in model.__class__.__name__:
+            return model.log_images(batch)['reconstructions']
+
+        x = model.get_input(model.first_stage_key, batch).to(model.device)
+        # c = model.get_input(model.cond_stage_key, batch).to(model.device)
+        c = model.get_input(model.cond_stage_key, batch)
+        if isinstance(c, dict):
+            c = {k: v.to(model.device) for k, v in c.items()}
+        else:
+            c = c.to(model.device)
+
+        # applying pre-trained VQGAN encoder for input (x) and, optionally, conditioning
+        quant_z, z_indices = model.encode_to_z(x)
+        quant_c, c_indices = model.encode_to_c(c)
+
+        B, _, hr_h, hr_w = quant_z.shape
+
+        if cfg.sampler.sampling_mode == 'nopix':
+            start_step = 0
+        else:
+            start_step = (hr_w // 2) * hr_h
+
+        z_pred_indices = torch.zeros_like(z_indices)
+        z_pred_indices[:, :start_step] = z_indices[:, :start_step]
+
+        for step in range(start_step, hr_w * hr_h):
+            i = step % hr_h
+            j = step // hr_h
+
+            patch = z_pred_indices
+            cpatch = c_indices
+
+            if model.cond_stage_model.__class__.__name__ in ['RawFeatsStage', 'ClassOnlyStage', 'FeatsClassStage']:
+                logits, _, attention = model.transformer(patch[:, :-1], cpatch)
+            else:
+                patch = torch.cat((cpatch, patch), dim=1)
+                logits, _, attention = model.transformer(patch[:, :-1])
+
+            # remove conditioning
+            logits = logits[:, -hr_w*hr_h:, :]
+
+            local_pos_in_flat = j * hr_h + i
+            logits = logits[:, local_pos_in_flat, :]
+
+            logits = logits / cfg.sampler.temperature
+
+            if cfg.sampler.top_k is not None:
+                logits = model.top_k_logits(logits, cfg.sampler.top_k)
+
+            probs = torch.nn.functional.softmax(logits, dim=-1)
+
+            # sample from the distribution or take the most likely
+            if cfg.sampler.sample_next_tok_from_pred_dist:
+                ix = torch.multinomial(probs, num_samples=1)
+            else:
+                _, ix = torch.topk(probs, k=1, dim=-1)
+
+            # slicing in brackets means 'leave this dimension without squeezing'
+            z_pred_indices[:, [j * hr_h + i]] = ix
+            # print(z_pred_indices.reshape(B, hr_w, hr_h))
+
+        # applying pre-trained VQGAN decoder
+        z_pred_img = model.decode_to_img(z_pred_indices, quant_z.shape)
+
+        return z_pred_img
+
+
+def save_specs(cfg, specs, samples_split_dirs, model, batch, split, sample_id, vocoder):
+    if isinstance(model, torch.nn.parallel.DistributedDataParallel):
+        # model_ddp = model
+        model = model.module
+
+    if 'VQModel' in model.__class__.__name__:
+        specs_key = 'file_path_'
+    else:
+        cond_stage_model_name = model.cond_stage_model.__class__.__name__
+        transformer_model_name = model.transformer.__class__.__name__
+        if (cond_stage_model_name in ['VQModel1d', 'FeatClusterStage']
+            or transformer_model_name in ['GPTFeats', 'GPTFeatsClass']):
+            specs_key = 'file_path_specs_'
+        else:
+            specs_key = 'file_path_'
+
+    dataset_name = cfg.data.params.train.target.split('.')[-1]
+    if 'VGGSound' in dataset_name:
+        extract_vidname_fn = lambda x: Path(x).name.replace('_mel.npy', '')
+    elif 'VAS' in dataset_name:
+        extract_vidname_fn = lambda x: Path(x).stem
+    else:
+        raise NotImplementedError
+
+    # iterating inside a batch
+    for i, spec in enumerate(specs):
+        class_foldername = f'cls_{batch["target"][i]}'
+        vidname = extract_vidname_fn(batch[specs_key][i])
+        save_path = samples_split_dirs[split] / class_foldername
+        os.makedirs(save_path, exist_ok=True)
+        # spec = torch.clamp(spec, -1., 1.)
+        spec = spec.squeeze(0).cpu().numpy()
+        # audios are in [-1, 1], making them in [0, 1]
+        spec = (spec + 1) / 2
+        np.save(save_path / f'{vidname}_sample_{sample_id}.npy', spec)
+        if vocoder is not None:
+            wave_from_vocoder = vocoder(torch.from_numpy(spec).unsqueeze(0).to('cuda')).cpu().squeeze().detach().numpy()
+            soundfile.write(save_path / f'{vidname}_sample_{sample_id}.wav', wave_from_vocoder, 22050, 'PCM_24')
+
+
+def sample(gpu_id, cfg, samples_split_dirs, is_ddp):
+
+    device = torch.device(f'cuda:{gpu_id}')
+    torch.cuda.set_device(device)
+
+    dataloaders, model, vocoder, _ = load_model_and_dataloaders(cfg, device, is_ddp)
+
+    if isinstance(cfg.sampler.samples_per_video, int) and cfg.sampler.samples_per_video > 0:
+        samples_per_video = cfg.sampler.samples_per_video
+        print(f'USING {samples_per_video} samples per video')
+    else:
+        samples_per_video = 1
+
+    for split, dataloader in dataloaders.items():
+        for batch in tqdm(dataloader):
+            for sample_id in range(samples_per_video):
+                specs = sample_spectrogram(cfg, model, batch)
+                save_specs(cfg, specs, samples_split_dirs, model, batch, split, sample_id, vocoder)
+
+def main():
+    torch.manual_seed(0)
+    local_rank = os.environ.get('LOCAL_RANK')
+
+    cfg = load_and_save_config()
+    samples_split_dirs = make_folder_for_samples(cfg)
+
+    if local_rank is not None:
+        is_ddp = True
+        local_rank = int(local_rank)
+        # 300s is a timeout for other worker to check out when the first one reached the barrier
+        dist.init_process_group(cfg.sampler.get('dist_backend', 'nccl'), 'env://', datetime.timedelta(0, 300))
+        print(f'WORLDSIZE {dist.get_world_size()} – RANK {dist.get_rank()}')
+        if dist.get_rank() == 0:
+            print('MASTER:', os.environ['MASTER_ADDR'], ':', os.environ['MASTER_PORT'])
+            print(OmegaConf.to_yaml(cfg))
+    else:
+        is_ddp = False
+        print(OmegaConf.to_yaml(cfg))
+        local_rank = 0
+
+    sample(local_rank, cfg, samples_split_dirs, is_ddp)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/evaluation/metrics/fid.py b/evaluation/metrics/fid.py
new file mode 100644
index 0000000..09b8bac
--- /dev/null
+++ b/evaluation/metrics/fid.py
@@ -0,0 +1,61 @@
+import torch
+import numpy as np
+import scipy.linalg
+
+def calculate_fid(featuresdict_1, featuresdict_2, feat_layer_name):
+    eps = 1e-6
+
+    features_1 = featuresdict_1[feat_layer_name]
+    features_2 = featuresdict_2[feat_layer_name]
+
+    assert torch.is_tensor(features_1) and features_1.dim() == 2
+    assert torch.is_tensor(features_2) and features_2.dim() == 2
+
+    stat_1 = {
+        'mu': np.mean(features_1.numpy(), axis=0),
+        'sigma': np.cov(features_1.numpy(), rowvar=False),
+    }
+    stat_2 = {
+        'mu': np.mean(features_2.numpy(), axis=0),
+        'sigma': np.cov(features_2.numpy(), rowvar=False),
+    }
+
+    print('Computing Frechet Inception Distance')
+
+    mu1, sigma1 = stat_1['mu'], stat_1['sigma']
+    mu2, sigma2 = stat_2['mu'], stat_2['sigma']
+    assert mu1.shape == mu2.shape and mu1.dtype == mu2.dtype
+    assert sigma1.shape == sigma2.shape and sigma1.dtype == sigma2.dtype
+
+    mu1 = np.atleast_1d(mu1)
+    mu2 = np.atleast_1d(mu2)
+
+    sigma1 = np.atleast_2d(sigma1)
+    sigma2 = np.atleast_2d(sigma2)
+
+    assert mu1.shape == mu2.shape, 'Training and test mean vectors have different lengths'
+    assert sigma1.shape == sigma2.shape, 'Training and test covariances have different dimensions'
+
+    diff = mu1 - mu2
+
+    # Product might be almost singular
+    covmean, _ = scipy.linalg.sqrtm(sigma1.dot(sigma2), disp=False)
+    if not np.isfinite(covmean).all():
+        print(f'WARNING: fid calculation produces singular product; adding {eps} to diagonal of cov')
+        offset = np.eye(sigma1.shape[0]) * eps
+        covmean = scipy.linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))
+
+    # Numerical error might give slight imaginary component
+    if np.iscomplexobj(covmean):
+        if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
+            m = np.max(np.abs(covmean.imag))
+            assert False, 'Imaginary component {}'.format(m)
+        covmean = covmean.real
+
+    tr_covmean = np.trace(covmean)
+
+    fid = diff.dot(diff) + np.trace(sigma1) + np.trace(sigma2) - 2 * tr_covmean
+
+    return {
+        'frechet_inception_distance': float(fid),
+    }
diff --git a/evaluation/metrics/isc.py b/evaluation/metrics/isc.py
new file mode 100644
index 0000000..6dfcdd9
--- /dev/null
+++ b/evaluation/metrics/isc.py
@@ -0,0 +1,32 @@
+import torch
+import numpy as np
+
+
+def calculate_isc(featuresdict, feat_layer_name, rng_seed, samples_shuffle, splits):
+    print('Computing Inception Score')
+
+    features = featuresdict[feat_layer_name]
+
+    assert torch.is_tensor(features) and features.dim() == 2
+    N, C = features.shape
+    if samples_shuffle:
+        rng = np.random.RandomState(rng_seed)
+        features = features[rng.permutation(N), :]
+    features = features.double()
+
+    p = features.softmax(dim=1)
+    log_p = features.log_softmax(dim=1)
+
+    scores = []
+    for i in range(splits):
+        p_chunk = p[(i * N // splits): ((i + 1) * N // splits), :]
+        log_p_chunk = log_p[(i * N // splits): ((i + 1) * N // splits), :]
+        q_chunk = p_chunk.mean(dim=0, keepdim=True)
+        kl = p_chunk * (log_p_chunk - q_chunk.log())
+        kl = kl.sum(dim=1).mean().exp().item()
+        scores.append(kl)
+
+    return {
+        'inception_score_mean': float(np.mean(scores)),
+        'inception_score_std': float(np.std(scores)),
+    }
diff --git a/evaluation/metrics/kid.py b/evaluation/metrics/kid.py
new file mode 100644
index 0000000..3dd34a8
--- /dev/null
+++ b/evaluation/metrics/kid.py
@@ -0,0 +1,72 @@
+import torch
+import numpy as np
+from tqdm import tqdm
+
+
+def calculate_kid(featuresdict_1, featuresdict_2, subsets, subset_size, degree, gamma, coef0,
+                  rng_seed, feat_layer_name):
+    features_1 = featuresdict_1[feat_layer_name]
+    features_2 = featuresdict_2[feat_layer_name]
+
+    assert torch.is_tensor(features_1) and features_1.dim() == 2
+    assert torch.is_tensor(features_2) and features_2.dim() == 2
+    assert features_1.shape[1] == features_2.shape[1]
+    if subset_size > len(features_2):
+        print(f'WARNING: subset size ({subset_size}) is larger than feature length ({len(features_2)}). ',
+              'Using', len(features_2), 'for both datasets')
+        subset_size = len(features_2)
+    if subset_size > len(features_1):
+        print(f'WARNING: subset size ({subset_size}) is larger than feature length ({len(features_1)}). ',
+              'Using', len(features_1), 'for both datasets')
+        subset_size = len(features_1)
+
+    features_1 = features_1.cpu().numpy()
+    features_2 = features_2.cpu().numpy()
+
+    mmds = np.zeros(subsets)
+    rng = np.random.RandomState(rng_seed)
+
+    for i in tqdm(range(subsets), leave=False, unit='subsets', desc='Computing Kernel Inception Distance'):
+        f1 = features_1[rng.choice(len(features_1), subset_size, replace=False)]
+        f2 = features_2[rng.choice(len(features_2), subset_size, replace=False)]
+        o = polynomial_mmd(f1, f2, degree, gamma, coef0)
+        mmds[i] = o
+
+    return {
+        'kernel_inception_distance_mean': float(np.mean(mmds)),
+        'kernel_inception_distance_std': float(np.std(mmds)),
+    }
+
+def polynomial_kernel(X, Y, degree=3, gamma=None, coef0=1):
+    if gamma in [None, 'none', 'null', 'None']:
+        gamma = 1.0 / X.shape[1]
+    K = (np.matmul(X, Y.T) * gamma + coef0) ** degree
+    return K
+
+def polynomial_mmd(features_1, features_2, degree, gamma, coef0):
+    K_XX = polynomial_kernel(features_1, features_1, degree=degree, gamma=gamma, coef0=coef0)
+    K_YY = polynomial_kernel(features_2, features_2, degree=degree, gamma=gamma, coef0=coef0)
+    K_XY = polynomial_kernel(features_1, features_2, degree=degree, gamma=gamma, coef0=coef0)
+
+    # based on https://github.com/dougalsutherland/opt-mmd/blob/master/two_sample/mmd.py
+    # changed to not compute the full kernel matrix at once
+    m = K_XX.shape[0]
+    assert K_XX.shape == (m, m)
+    assert K_XY.shape == (m, m)
+    assert K_YY.shape == (m, m)
+
+    diag_X = np.diagonal(K_XX)
+    diag_Y = np.diagonal(K_YY)
+
+    Kt_XX_sums = K_XX.sum(axis=1) - diag_X
+    Kt_YY_sums = K_YY.sum(axis=1) - diag_Y
+    K_XY_sums_0 = K_XY.sum(axis=0)
+
+    Kt_XX_sum = Kt_XX_sums.sum()
+    Kt_YY_sum = Kt_YY_sums.sum()
+    K_XY_sum = K_XY_sums_0.sum()
+
+    mmd2 = (Kt_XX_sum + Kt_YY_sum) / (m * (m-1))
+    mmd2 -= 2 * K_XY_sum / (m * m)
+
+    return mmd2
diff --git a/evaluation/metrics/kl.py b/evaluation/metrics/kl.py
new file mode 100644
index 0000000..8c30de1
--- /dev/null
+++ b/evaluation/metrics/kl.py
@@ -0,0 +1,90 @@
+import torch
+from pathlib import Path
+
+def path_to_sharedkey(path, dataset_name, classes=None):
+    if dataset_name.lower() == 'vggsound':
+        # a generic oneliner which extracts the unique filename for the dataset.
+        # Works on both FakeFolder and VGGSound* datasets
+        sharedkey = Path(path).stem.replace('_mel', '').split('_sample_')[0]
+    elif dataset_name.lower() == 'vas':
+        # in the case of vas the procedure is a bit more tricky and involves relying on the premise that
+        # the folder names (.../VAS_validation/cls_0, .../cls_1 etc) are made after enumerating sorted list
+        # of classes.
+        classes = sorted(classes)
+        target_to_label = {f'cls_{i}': c for i, c in enumerate(classes)}
+        # replacing class folder with the name of the class to match the original dataset (cls_2 -> dog)
+        for folder_cls_name, label in target_to_label.items():
+            path = path.replace(folder_cls_name, label).replace('melspec_10s_22050hz/', '')
+        # merging video name with class name to make a unique shared key
+        sharedkey = Path(path).parent.stem + '_' + Path(path).stem.replace('_mel', '').split('_sample_')[0]
+    else:
+        raise NotImplementedError
+    return sharedkey
+
+def calculate_kl(featuresdict_1, featuresdict_2, feat_layer_name, dataset_name, classes=None):
+    test_input(featuresdict_1, featuresdict_2, feat_layer_name, dataset_name, classes)
+
+    print('KL: Assuming that `input2` is "pseudo" target and `input1` is prediction. KL(input2_i||input1_i)')
+
+    EPS = 1e-6
+
+    features_1 = featuresdict_1[feat_layer_name]
+    features_2 = featuresdict_2[feat_layer_name]
+
+    paths_1 = featuresdict_1['file_path_']
+    paths_2 = featuresdict_2['file_path_']
+
+    path_to_feats_1 = {p: f for p, f in zip(paths_1, features_1)}
+    path_to_feats_2 = {p: f for p, f in zip(paths_2, features_2)}
+
+    # in input1 (fakes) can have multiple samples per video, while input2 has only one real
+    sharedkey_to_feats_1 = {path_to_sharedkey(p, dataset_name, classes): [] for p in paths_1}
+    sharedkey_to_feats_2 = {path_to_sharedkey(p, dataset_name, classes): path_to_feats_2[p] for p in paths_2}
+
+    # TODO: assert all 1s in 2 and their size is the same
+
+    # grouping samples by vid name
+    for path, features in path_to_feats_1.items():
+        sharedkey_to_feats_1[path_to_sharedkey(path, dataset_name, classes)] += [features]
+
+    features_1 = []
+    features_2 = []
+    for sharedkey, feat_2 in sharedkey_to_feats_2.items():
+        features_1.extend(sharedkey_to_feats_1[sharedkey])
+        # just replicating the ground truth logits to compare with multiple samples in prediction
+        samples_num = len(sharedkey_to_feats_1[sharedkey])
+        features_2.extend([feat_2] * samples_num)
+
+    features_1 = torch.stack(features_1, dim=0)
+    features_2 = torch.stack(features_2, dim=0)
+
+    features_1 = features_1.softmax(dim=1)
+    features_2 = features_2.softmax(dim=1)
+
+    # KL(features_2_i || features_1_i)
+    kl = torch.nn.functional.kl_div((features_1 + EPS).log(), features_2, reduction='sum') / len(features_1)
+
+    return {'kullback_leibler_divergence': float(kl)}
+
+def test_input(featuresdict_1, featuresdict_2, feat_layer_name, dataset_name, classes):
+    assert feat_layer_name == 'logits', 'This KL div metric is implemented on logits.'
+    assert 'file_path_' in featuresdict_1 and 'file_path_' in featuresdict_2, 'File paths are missing'
+    assert len(featuresdict_1) >= len(featuresdict_2), 'There are more samples in input1, than in input2'
+    assert len(featuresdict_1) % len(featuresdict_2) == 0, 'Size of input1 is not a multiple of input1 size.'
+    if dataset_name == 'vas':
+        assert classes is not None, f'Specify classes if you are using vas dataset. Now `classes` – {classes}'
+        print('KL: when FakesFolder on VAS is used as a dataset, we assume the original labels were sorted',
+              'to produce the target_ids. E.g. `baby` -> `cls_0`; `cough` -> `cls_1`; `dog` -> `cls_2`.')
+
+
+if __name__ == '__main__':
+    # p = torch.tensor([0.25, 0.25, 0.25, 0.25]).view(1, 4)
+    # q = torch.tensor([0.25, 0.25, 0.25, 0.25]).view(1, 4)
+    # 0.
+
+    p = torch.tensor([9/25, 12/25, 4/25]).view(1, 3)
+    q = torch.tensor([1/3, 1/3, 1/3]).view(1, 3)
+    kl = torch.nn.functional.kl_div(torch.log(q), p, reduction='sum')
+    # 0.0853
+
+    print(kl)
diff --git a/evaluation/sbatch_sample.sh b/evaluation/sbatch_sample.sh
new file mode 100644
index 0000000..ddabfc3
--- /dev/null
+++ b/evaluation/sbatch_sample.sh
@@ -0,0 +1,178 @@
+#!/bin/bash
+
+# sbatch --partition=gpusmall --time=36:00:00 sbatch_train_mahti.sh
+# sbatch --partition=gpumedium --time=36:00:00 sbatch_train_mahti.sh
+# sbatch --partition=gputest --time=00:15:00 sbatch_train_mahti.sh
+
+#SBATCH --job-name=s
+#SBATCH --account=project_2000936
+#SBATCH --chdir=/projappl/project_2000936/SpecVQGAN
+#SBATCH --output=./sbatch_logs/run_%J.txt
+#SBATCH --error=./sbatch_logs/run_%J.txt
+#SBATCH --verbose
+#SBATCH --ntasks-per-node=1
+#SBATCH --nodes=1
+#SBATCH --cpus-per-task=128
+#SBATCH --gres=gpu:a100:4,nvme:700
+#SBATCH --mem=64G
+
+# exit when any command fails
+set -e
+# if some error happens in the initialation of parallel process then you can get the debug info.
+export NCCL_DEBUG=INFO  # comment it if you are not debugging distributed parallel setup
+export NCCL_DEBUG_SUBSYS=ALL # comment it if you are not debugging distributed parallel setup
+
+# We want names of master and slave nodes. Make sure this node (MASTER) comes first
+MASTER=`/bin/hostname -s`
+if (( $SLURM_JOB_NUM_NODES > 1 )); then
+    SLAVES=`scontrol show hostnames $SLURM_JOB_NODELIST | grep -v $MASTER`
+fi
+# Get a random unused port on this host(MASTER)
+MPORT=`comm -23 <(seq 49152 65535 | sort) <(ss -Htan | awk '{print $4}' | cut -d':' -f2 | sort -u) | shuf | head -n 1`
+# it is ok if slaves are not defined
+HOSTLIST="$MASTER $SLAVES"
+
+# Determine the number of GPU
+NUM_GPUS="${CUDA_VISIBLE_DEVICES//[^[:digit:]]/}"
+NUM_GPUS=${#NUM_GPUS}
+
+# Feature extraction commands
+EXTRACT_SPECS_VGGSOUND="echo \"Extracting input features @ \$HOSTNAME\"
+    for i in \`seq 1 64\`; do
+        i=\`printf %02d \$i\` \
+        && tar -xf $SCRATCH/vladimir/vggsound/mel_tars/melspec_10s_22050hz_\$i.tar -C $LOCAL_SCRATCH \
+        &
+    done; wait"
+EXTRACT_FEATS_VGGSOUND="echo \"Extracting video features @ \$HOSTNAME\"
+    for i in \`seq 1 64\`; do
+        i=\`printf %02d \$i\` \
+        && tar -xf $SCRATCH/vladimir/vggsound/feature_tars/feature_flow_bninception_dim1024_21.5fps_\$i.tar -C $LOCAL_SCRATCH \
+        && tar -xf $SCRATCH/vladimir/vggsound/feature_tars/feature_rgb_bninception_dim1024_21.5fps_\$i.tar -C $LOCAL_SCRATCH \
+        &
+    done; wait"
+
+EXTRACT_SPECS_VAS="echo \"Extracting input features\"
+strings=(\"dog\" \"fireworks\" \"drum\" \"baby\" \"gun\" \"sneeze\" \"cough\" \"hammer\")
+for class in \"\${strings[@]}\"; do
+    mkdir -p $LOCAL_SCRATCH/features/\$class && \
+    tar xf $SCRATCH/vladimir/vas/feature_tars/\${class}_melspec_10s_22050hz.tar -C $LOCAL_SCRATCH/features/\$class &
+done; wait"
+EXTRACT_FEATS_VAS="echo \"Extracting video features\"
+strings=(\"dog\" \"fireworks\" \"drum\" \"baby\" \"gun\" \"sneeze\" \"cough\" \"hammer\")
+for class in \"\${strings[@]}\"; do
+    tar xf $SCRATCH/vladimir/vas/feature_tars/\${class}_feature_rgb_bninception_dim1024_21.5fps.tar -C $LOCAL_SCRATCH/features/\$class & \
+    tar xf $SCRATCH/vladimir/vas/feature_tars/\${class}_feature_flow_bninception_dim1024_21.5fps.tar -C $LOCAL_SCRATCH/features/\$class &
+done; wait"
+
+
+# Path to experiment
+EXPERIMENT_PATH=./logs/2021-06-09T15-17-18_vas_transformer
+
+# Select a dataset here
+DATASET="VAS"
+# DATASET="VGGSound"
+
+# TOP_K_OPTIONS=( "1" "16" "64" "100" "128" "256" "512" "1024" )
+TOP_K_OPTIONS=( "64" )
+# VGGSOUND_SAMPLES_PER_VIDEO=10
+VGGSOUND_SAMPLES_PER_VIDEO=1
+# VAS_SAMPLES_PER_VIDEO=100
+VAS_SAMPLES_PER_VIDEO=10
+
+if [[ "$DATASET" == "VGGSound" ]]; then
+    # EXTRACT_FILES_CMD="$EXTRACT_SPECS_VGGSOUND"
+    EXTRACT_FILES_CMD="$EXTRACT_SPECS_VGGSOUND && $EXTRACT_FEATS_VGGSOUND"
+    SPEC_DIR_PATH="$LOCAL_SCRATCH/melspec_10s_22050hz/"
+    RGB_FEATS_DIR_PATH="$LOCAL_SCRATCH/feature_rgb_bninception_dim1024_21.5fps/"
+    FLOW_FEATS_DIR_PATH="$LOCAL_SCRATCH/feature_flow_bninception_dim1024_21.5fps/"
+    SAMPLES_FOLDER="VGGSound_test"
+    SPLITS="\"[test, ]\""
+    SAMPLER_BATCHSIZE=32
+    SAMPLES_PER_VIDEO=$VGGSOUND_SAMPLES_PER_VIDEO
+elif [[ "$DATASET" == "VAS" ]]; then
+    # EXTRACT_FILES_CMD="$EXTRACT_SPECS_VAS"
+    EXTRACT_FILES_CMD="$EXTRACT_SPECS_VAS && $EXTRACT_FEATS_VAS"
+    SPEC_DIR_PATH="$LOCAL_SCRATCH/features/*/melspec_10s_22050hz/"
+    RGB_FEATS_DIR_PATH="$LOCAL_SCRATCH/features/*/feature_rgb_bninception_dim1024_21.5fps/"
+    FLOW_FEATS_DIR_PATH="$LOCAL_SCRATCH/features/*/feature_flow_bninception_dim1024_21.5fps/"
+    SAMPLES_FOLDER="VAS_validation"
+    SPLITS="\"[validation, ]\""
+    SAMPLER_BATCHSIZE=4
+    SAMPLES_PER_VIDEO=$VAS_SAMPLES_PER_VIDEO
+else
+    echo "NotImplementedError"
+    exit
+fi
+
+# Some info to print
+echo "Local Scratch" $LOCAL_SCRATCH
+echo "Hostlist:" $HOSTLIST
+echo "Samples per video:" $SAMPLES_PER_VIDEO "; Sampler path" $EXPERIMENT_PATH
+echo $EXTRACT_FILES_CMD
+echo $SPEC_DIR_PATH
+echo $RGB_FEATS_DIR_PATH
+echo $FLOW_FEATS_DIR_PATH
+
+# Extracting files at every node
+for node in $HOSTLIST; do
+    ssh -q $node "$EXTRACT_FILES_CMD" &
+done
+wait
+
+for TOP_K in "${TOP_K_OPTIONS[@]}"; do
+    echo "Starting TOP-$TOP_K. Number of Samples/Video: $SAMPLES_PER_VIDEO"
+
+    # Saving the time stamp to reuse it when sampling is done. Random second is used to avoid overalapping \
+    # sample folder names
+    RAND_SEC=$((RANDOM%59+1))
+    NOW=`date +"%Y-%m-%dT%H-%M-$(printf %02d $RAND_SEC)"`
+
+    # Launch the torch.distributed.launch tool, first on master (first in $HOSTLIST) then on the slaves
+    # Escape the '$' from the variable if you want to take variable from the server's environement (where you ssh)
+    # By default bash will execute the tring with the variables from this script
+    # loading conda environment.
+    # We are doing both sampling and evaluation sequentially
+    source $PROJAPPL/miniconda3/etc/profile.d/conda.sh
+    NODE_RANK=0
+    for node in $HOSTLIST; do
+        ssh -q $node "conda activate specvqgan && \
+                    cd $PROJAPPL/SpecVQGAN && \
+                    python -m torch.distributed.launch \
+                        --nproc_per_node=$NUM_GPUS \
+                        --nnodes=$SLURM_JOB_NUM_NODES \
+                        --node_rank=$NODE_RANK \
+                        --master_addr=$MASTER \
+                        --master_port=$MPORT \
+                        --use_env \
+                            evaluation/generate_samples.py \
+                            sampler.config_sampler=evaluation/configs/sampler.yaml \
+                            sampler.model_logdir=$EXPERIMENT_PATH \
+                            sampler.splits=$SPLITS \
+                            sampler.samples_per_video=$SAMPLES_PER_VIDEO \
+                            sampler.batch_size=$SAMPLER_BATCHSIZE \
+                            sampler.top_k=$TOP_K \
+                            data.params.spec_dir_path=$SPEC_DIR_PATH \
+                            data.params.rgb_feats_dir_path=$RGB_FEATS_DIR_PATH \
+                            data.params.flow_feats_dir_path=$FLOW_FEATS_DIR_PATH \
+                            sampler.now=$NOW && \
+                    python -m torch.distributed.launch \
+                        --nproc_per_node=$NUM_GPUS \
+                        --nnodes=$SLURM_JOB_NUM_NODES \
+                        --node_rank=$NODE_RANK \
+                        --master_addr=$MASTER \
+                        --master_port=$MPORT \
+                        --use_env \
+                        evaluate.py \
+                            config=./evaluation/configs/eval_melception_${DATASET,,}.yaml \
+                            input2.path_to_exp=$EXPERIMENT_PATH \
+                            patch.specs_dir=$SPEC_DIR_PATH \
+                            patch.spec_dir_path=$SPEC_DIR_PATH \
+                            patch.rgb_feats_dir_path=$RGB_FEATS_DIR_PATH \
+                            patch.flow_feats_dir_path=$FLOW_FEATS_DIR_PATH \
+                            input1.params.root=$EXPERIMENT_PATH/samples_$NOW/$SAMPLES_FOLDER
+                    " &
+        NODE_RANK=$((NODE_RANK+1))
+    done
+    wait
+    echo "Done TOP-$TOP_K"
+done
diff --git a/feature_extraction/bn_inception/extract_feature.py b/feature_extraction/bn_inception/extract_feature.py
new file mode 100644
index 0000000..91ce127
--- /dev/null
+++ b/feature_extraction/bn_inception/extract_feature.py
@@ -0,0 +1,170 @@
+import argparse
+import os
+import pickle as pkl
+import time
+from glob import glob
+
+import numpy as np
+import torch.nn.parallel
+import torch.optim
+import torchvision
+from PIL import Image
+from torch.utils.data import Dataset
+
+from tsn.models import TSN
+
+
+class GroupScale(object):
+    def __init__(self, size, interpolation=Image.BILINEAR):
+        self.worker = torchvision.transforms.Scale(size, interpolation)
+
+    def __call__(self, img_group):
+        return [self.worker(img) for img in img_group]
+
+class GroupNormalize(object):
+    def __init__(self, mean, std):
+        self.mean = mean
+        self.std = std
+
+    def __call__(self, tensor):
+        rep_mean = self.mean * (tensor.size()[0]//len(self.mean))
+        rep_std = self.std * (tensor.size()[0]//len(self.std))
+
+        for t, m, s in zip(tensor, rep_mean, rep_std):
+            t.sub_(m).div_(s)
+
+        return tensor
+
+class Stack(object):
+
+    def __init__(self, roll=False):
+        self.roll = roll
+
+    def __call__(self, img_group):
+        if img_group[0].mode == 'L':
+            return np.concatenate([np.expand_dims(x, 2) for x in img_group], axis=2)
+        elif img_group[0].mode == 'RGB':
+            if self.roll:
+                return np.concatenate([np.array(x)[:, :, ::-1] for x in img_group], axis=2)
+            else:
+                return np.concatenate(img_group, axis=2)
+
+class ToTorchFormatTensor(object):
+    """ Converts a PIL.Image (RGB) or numpy.ndarray (H x W x C) in the range [0, 255]
+    to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0] """
+    def __init__(self, div=True):
+        self.div = div
+
+    def __call__(self, pic):
+        if isinstance(pic, np.ndarray):
+            # handle numpy array
+            img = torch.from_numpy(pic).permute(2, 0, 1).contiguous()
+        else:
+            # handle PIL Image
+            img = torch.ByteTensor(torch.ByteStorage.from_buffer(pic.tobytes()))
+            img = img.view(pic.size[1], pic.size[0], len(pic.mode))
+            # put it from HWC to CHW format
+            # yikes, this transpose takes 80% of the loading time/CPU
+            img = img.transpose(0, 1).transpose(0, 2).contiguous()
+        return img.float().div(255) if self.div else img.float()
+
+
+class TSNDataSet(Dataset):
+    def __init__(self, root_path, list_file, modality='RGB',
+                 image_tmpl='img_{:05d}.jpg', transform=None):
+
+        self.root_path = root_path
+        self.list_file = list_file
+        self.modality = modality
+        self.image_tmpl = image_tmpl
+        self.transform = transform
+        with open(list_file) as f:
+            self.video_list = [line.strip() for line in f]
+        f.close()
+
+    def _load_image(self, directory, idx):
+        if self.modality == 'RGB':
+            return [Image.open(os.path.join(directory, self.image_tmpl.format(idx))).convert('RGB')]
+        elif self.modality == 'Flow':
+            x_img = Image.open(os.path.join(directory, self.image_tmpl.format('x', idx))).convert('L')
+            y_img = Image.open(os.path.join(directory, self.image_tmpl.format('y', idx))).convert('L')
+            return [x_img, y_img]
+
+    def __getitem__(self, index):
+        video_path = os.path.join(self.root_path, self.video_list[index])
+        images = list()
+        if self.modality == 'RGB':
+            num_frames = len(glob(os.path.join(video_path, "img*.jpg")))
+        elif self.modality == 'Flow':
+            num_frames = len(glob(os.path.join(video_path, "flow_x*.jpg")))
+        for ind in (np.arange(num_frames)+1):
+            images.extend(self._load_image(video_path, ind))
+        process_data = self.transform(images)
+        return process_data, video_path
+
+    def __len__(self):
+        return len(self.video_list)
+
+def eval_video(data):
+    if args.modality == 'RGB':
+        length = 3
+    elif args.modality == 'Flow':
+        length = 2
+    else:
+        raise ValueError("Unknown modality "+args.modality)
+    input_var = torch.autograd.Variable(data.view(-1, length, data.size(2), data.size(3)),
+                                        volatile=True)
+    baseout = np.squeeze(net(input_var).data.cpu().numpy().copy())
+    return baseout
+
+
+if __name__ == '__main__':
+    # options
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-i', '--input_dir', type=str)
+    parser.add_argument('-o', '--output_dir', type=str)
+    parser.add_argument('-m', '--modality', type=str, choices=['RGB', 'Flow'])
+    parser.add_argument('-t', '--test_list', type=str)
+    parser.add_argument('--input_size', type=int, default=224)
+    parser.add_argument('--crop_fusion_type', type=str, default='avg',
+                        choices=['avg', 'max', 'topk'])
+    parser.add_argument('--dropout', type=float, default=0.7)
+    parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
+                        help='number of data loading workers (default: 4)')
+    parser.add_argument('--flow_prefix', type=str, default='')
+
+    args = parser.parse_args()
+
+    net = TSN(args.modality,
+            consensus_type=args.crop_fusion_type,
+            dropout=args.dropout)
+
+    cropping = torchvision.transforms.Compose([
+        GroupScale((net.input_size, net.input_size)),
+    ])
+
+    data_loader = torch.utils.data.DataLoader(
+            TSNDataSet(args.input_dir, args.test_list,
+                    modality=args.modality,
+                    image_tmpl="img_{:05d}.jpg" if args.modality == 'RGB' else args.flow_prefix+"flow_{}_{:05d}.jpg",
+                    transform=torchvision.transforms.Compose([
+                        cropping, Stack(roll=True),
+                        ToTorchFormatTensor(div=False),
+                        GroupNormalize(net.input_mean, net.input_std),
+                    ])),
+            batch_size=1, shuffle=False,
+            num_workers=1, pin_memory=True)
+
+    net = torch.nn.DataParallel(net).cuda()
+    net.eval()
+    for i, (data, video_path) in enumerate(data_loader):
+        os.makedirs(args.output_dir, exist_ok=True)
+        ft_path = os.path.join(args.output_dir, video_path[0].split(os.sep)[-1]+".pkl")
+        if args.modality == 'RGB':
+            length = 3
+        elif args.modality == 'Flow':
+            length = 2
+        input_var = torch.autograd.Variable(data.view(-1, length, data.size(2), data.size(3)),
+                                            volatile=True)
+        rst = np.squeeze(net(input_var).data.cpu().numpy().copy())
+        pkl.dump(rst, open(ft_path, "wb"))
diff --git a/feature_extraction/bn_inception/tsn/bninception/__init__.py b/feature_extraction/bn_inception/tsn/bninception/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/feature_extraction/bn_inception/tsn/bninception/bn_inception.yaml b/feature_extraction/bn_inception/tsn/bninception/bn_inception.yaml
new file mode 100644
index 0000000..52909ea
--- /dev/null
+++ b/feature_extraction/bn_inception/tsn/bninception/bn_inception.yaml
@@ -0,0 +1,558 @@
+inputs: []
+layers:
+- attrs: {kernel_size: 7, num_output: 64, pad: 3, stride: 2}
+  expr: conv1_7x7_s2<=Convolution<=data
+  id: conv1_7x7_s2
+- attrs: {frozen: true}
+  expr: conv1_7x7_s2_bn<=BN<=conv1_7x7_s2
+  id: conv1_7x7_s2_bn
+- {expr: conv1_7x7_s2_bn<=ReLU<=conv1_7x7_s2_bn, id: conv1_relu_7x7}
+- attrs: {kernel_size: 3, mode: max, stride: 2}
+  expr: pool1_3x3_s2<=Pooling<=conv1_7x7_s2_bn
+  id: pool1_3x3_s2
+- attrs: {kernel_size: 1, num_output: 64}
+  expr: conv2_3x3_reduce<=Convolution<=pool1_3x3_s2
+  id: conv2_3x3_reduce
+- attrs: {frozen: true}
+  expr: conv2_3x3_reduce_bn<=BN<=conv2_3x3_reduce
+  id: conv2_3x3_reduce_bn
+- {expr: conv2_3x3_reduce_bn<=ReLU<=conv2_3x3_reduce_bn, id: conv2_relu_3x3_reduce}
+- attrs: {kernel_size: 3, num_output: 192, pad: 1}
+  expr: conv2_3x3<=Convolution<=conv2_3x3_reduce_bn
+  id: conv2_3x3
+- attrs: {frozen: true}
+  expr: conv2_3x3_bn<=BN<=conv2_3x3
+  id: conv2_3x3_bn
+- {expr: conv2_3x3_bn<=ReLU<=conv2_3x3_bn, id: conv2_relu_3x3}
+- attrs: {kernel_size: 3, mode: max, stride: 2}
+  expr: pool2_3x3_s2<=Pooling<=conv2_3x3_bn
+  id: pool2_3x3_s2
+- attrs: {kernel_size: 1, num_output: 64}
+  expr: inception_3a_1x1<=Convolution<=pool2_3x3_s2
+  id: inception_3a_1x1
+- attrs: {frozen: true}
+  expr: inception_3a_1x1_bn<=BN<=inception_3a_1x1
+  id: inception_3a_1x1_bn
+- {expr: inception_3a_1x1_bn<=ReLU<=inception_3a_1x1_bn, id: inception_3a_relu_1x1}
+- attrs: {kernel_size: 1, num_output: 64}
+  expr: inception_3a_3x3_reduce<=Convolution<=pool2_3x3_s2
+  id: inception_3a_3x3_reduce
+- attrs: {frozen: true}
+  expr: inception_3a_3x3_reduce_bn<=BN<=inception_3a_3x3_reduce
+  id: inception_3a_3x3_reduce_bn
+- {expr: inception_3a_3x3_reduce_bn<=ReLU<=inception_3a_3x3_reduce_bn, id: inception_3a_relu_3x3_reduce}
+- attrs: {kernel_size: 3, num_output: 64, pad: 1}
+  expr: inception_3a_3x3<=Convolution<=inception_3a_3x3_reduce_bn
+  id: inception_3a_3x3
+- attrs: {frozen: true}
+  expr: inception_3a_3x3_bn<=BN<=inception_3a_3x3
+  id: inception_3a_3x3_bn
+- {expr: inception_3a_3x3_bn<=ReLU<=inception_3a_3x3_bn, id: inception_3a_relu_3x3}
+- attrs: {kernel_size: 1, num_output: 64}
+  expr: inception_3a_double_3x3_reduce<=Convolution<=pool2_3x3_s2
+  id: inception_3a_double_3x3_reduce
+- attrs: {frozen: true}
+  expr: inception_3a_double_3x3_reduce_bn<=BN<=inception_3a_double_3x3_reduce
+  id: inception_3a_double_3x3_reduce_bn
+- {expr: inception_3a_double_3x3_reduce_bn<=ReLU<=inception_3a_double_3x3_reduce_bn,
+  id: inception_3a_relu_double_3x3_reduce}
+- attrs: {kernel_size: 3, num_output: 96, pad: 1}
+  expr: inception_3a_double_3x3_1<=Convolution<=inception_3a_double_3x3_reduce_bn
+  id: inception_3a_double_3x3_1
+- attrs: {frozen: true}
+  expr: inception_3a_double_3x3_1_bn<=BN<=inception_3a_double_3x3_1
+  id: inception_3a_double_3x3_1_bn
+- {expr: inception_3a_double_3x3_1_bn<=ReLU<=inception_3a_double_3x3_1_bn, id: inception_3a_relu_double_3x3_1}
+- attrs: {kernel_size: 3, num_output: 96, pad: 1}
+  expr: inception_3a_double_3x3_2<=Convolution<=inception_3a_double_3x3_1_bn
+  id: inception_3a_double_3x3_2
+- attrs: {frozen: true}
+  expr: inception_3a_double_3x3_2_bn<=BN<=inception_3a_double_3x3_2
+  id: inception_3a_double_3x3_2_bn
+- {expr: inception_3a_double_3x3_2_bn<=ReLU<=inception_3a_double_3x3_2_bn, id: inception_3a_relu_double_3x3_2}
+- attrs: {kernel_size: 3, mode: ave, pad: 1, stride: 1}
+  expr: inception_3a_pool<=Pooling<=pool2_3x3_s2
+  id: inception_3a_pool
+- attrs: {kernel_size: 1, num_output: 32}
+  expr: inception_3a_pool_proj<=Convolution<=inception_3a_pool
+  id: inception_3a_pool_proj
+- attrs: {frozen: true}
+  expr: inception_3a_pool_proj_bn<=BN<=inception_3a_pool_proj
+  id: inception_3a_pool_proj_bn
+- {expr: inception_3a_pool_proj_bn<=ReLU<=inception_3a_pool_proj_bn, id: inception_3a_relu_pool_proj}
+- {expr: 'inception_3a_output<=Concat<=inception_3a_1x1_bn,inception_3a_3x3_bn,inception_3a_double_3x3_2_bn,inception_3a_pool_proj_bn',
+  id: inception_3a_output}
+- attrs: {kernel_size: 1, num_output: 64}
+  expr: inception_3b_1x1<=Convolution<=inception_3a_output
+  id: inception_3b_1x1
+- attrs: {frozen: true}
+  expr: inception_3b_1x1_bn<=BN<=inception_3b_1x1
+  id: inception_3b_1x1_bn
+- {expr: inception_3b_1x1_bn<=ReLU<=inception_3b_1x1_bn, id: inception_3b_relu_1x1}
+- attrs: {kernel_size: 1, num_output: 64}
+  expr: inception_3b_3x3_reduce<=Convolution<=inception_3a_output
+  id: inception_3b_3x3_reduce
+- attrs: {frozen: true}
+  expr: inception_3b_3x3_reduce_bn<=BN<=inception_3b_3x3_reduce
+  id: inception_3b_3x3_reduce_bn
+- {expr: inception_3b_3x3_reduce_bn<=ReLU<=inception_3b_3x3_reduce_bn, id: inception_3b_relu_3x3_reduce}
+- attrs: {kernel_size: 3, num_output: 96, pad: 1}
+  expr: inception_3b_3x3<=Convolution<=inception_3b_3x3_reduce_bn
+  id: inception_3b_3x3
+- attrs: {frozen: true}
+  expr: inception_3b_3x3_bn<=BN<=inception_3b_3x3
+  id: inception_3b_3x3_bn
+- {expr: inception_3b_3x3_bn<=ReLU<=inception_3b_3x3_bn, id: inception_3b_relu_3x3}
+- attrs: {kernel_size: 1, num_output: 64}
+  expr: inception_3b_double_3x3_reduce<=Convolution<=inception_3a_output
+  id: inception_3b_double_3x3_reduce
+- attrs: {frozen: true}
+  expr: inception_3b_double_3x3_reduce_bn<=BN<=inception_3b_double_3x3_reduce
+  id: inception_3b_double_3x3_reduce_bn
+- {expr: inception_3b_double_3x3_reduce_bn<=ReLU<=inception_3b_double_3x3_reduce_bn,
+  id: inception_3b_relu_double_3x3_reduce}
+- attrs: {kernel_size: 3, num_output: 96, pad: 1}
+  expr: inception_3b_double_3x3_1<=Convolution<=inception_3b_double_3x3_reduce_bn
+  id: inception_3b_double_3x3_1
+- attrs: {frozen: true}
+  expr: inception_3b_double_3x3_1_bn<=BN<=inception_3b_double_3x3_1
+  id: inception_3b_double_3x3_1_bn
+- {expr: inception_3b_double_3x3_1_bn<=ReLU<=inception_3b_double_3x3_1_bn, id: inception_3b_relu_double_3x3_1}
+- attrs: {kernel_size: 3, num_output: 96, pad: 1}
+  expr: inception_3b_double_3x3_2<=Convolution<=inception_3b_double_3x3_1_bn
+  id: inception_3b_double_3x3_2
+- attrs: {frozen: true}
+  expr: inception_3b_double_3x3_2_bn<=BN<=inception_3b_double_3x3_2
+  id: inception_3b_double_3x3_2_bn
+- {expr: inception_3b_double_3x3_2_bn<=ReLU<=inception_3b_double_3x3_2_bn, id: inception_3b_relu_double_3x3_2}
+- attrs: {kernel_size: 3, mode: ave, pad: 1, stride: 1}
+  expr: inception_3b_pool<=Pooling<=inception_3a_output
+  id: inception_3b_pool
+- attrs: {kernel_size: 1, num_output: 64}
+  expr: inception_3b_pool_proj<=Convolution<=inception_3b_pool
+  id: inception_3b_pool_proj
+- attrs: {frozen: true}
+  expr: inception_3b_pool_proj_bn<=BN<=inception_3b_pool_proj
+  id: inception_3b_pool_proj_bn
+- {expr: inception_3b_pool_proj_bn<=ReLU<=inception_3b_pool_proj_bn, id: inception_3b_relu_pool_proj}
+- {expr: 'inception_3b_output<=Concat<=inception_3b_1x1_bn,inception_3b_3x3_bn,inception_3b_double_3x3_2_bn,inception_3b_pool_proj_bn',
+  id: inception_3b_output}
+- attrs: {kernel_size: 1, num_output: 128}
+  expr: inception_3c_3x3_reduce<=Convolution<=inception_3b_output
+  id: inception_3c_3x3_reduce
+- attrs: {frozen: true}
+  expr: inception_3c_3x3_reduce_bn<=BN<=inception_3c_3x3_reduce
+  id: inception_3c_3x3_reduce_bn
+- {expr: inception_3c_3x3_reduce_bn<=ReLU<=inception_3c_3x3_reduce_bn, id: inception_3c_relu_3x3_reduce}
+- attrs: {kernel_size: 3, num_output: 160, pad: 1, stride: 2}
+  expr: inception_3c_3x3<=Convolution<=inception_3c_3x3_reduce_bn
+  id: inception_3c_3x3
+- attrs: {frozen: true}
+  expr: inception_3c_3x3_bn<=BN<=inception_3c_3x3
+  id: inception_3c_3x3_bn
+- {expr: inception_3c_3x3_bn<=ReLU<=inception_3c_3x3_bn, id: inception_3c_relu_3x3}
+- attrs: {kernel_size: 1, num_output: 64}
+  expr: inception_3c_double_3x3_reduce<=Convolution<=inception_3b_output
+  id: inception_3c_double_3x3_reduce
+- attrs: {frozen: true}
+  expr: inception_3c_double_3x3_reduce_bn<=BN<=inception_3c_double_3x3_reduce
+  id: inception_3c_double_3x3_reduce_bn
+- {expr: inception_3c_double_3x3_reduce_bn<=ReLU<=inception_3c_double_3x3_reduce_bn,
+  id: inception_3c_relu_double_3x3_reduce}
+- attrs: {kernel_size: 3, num_output: 96, pad: 1}
+  expr: inception_3c_double_3x3_1<=Convolution<=inception_3c_double_3x3_reduce_bn
+  id: inception_3c_double_3x3_1
+- attrs: {frozen: true}
+  expr: inception_3c_double_3x3_1_bn<=BN<=inception_3c_double_3x3_1
+  id: inception_3c_double_3x3_1_bn
+- {expr: inception_3c_double_3x3_1_bn<=ReLU<=inception_3c_double_3x3_1_bn, id: inception_3c_relu_double_3x3_1}
+- attrs: {kernel_size: 3, num_output: 96, pad: 1, stride: 2}
+  expr: inception_3c_double_3x3_2<=Convolution<=inception_3c_double_3x3_1_bn
+  id: inception_3c_double_3x3_2
+- attrs: {frozen: true}
+  expr: inception_3c_double_3x3_2_bn<=BN<=inception_3c_double_3x3_2
+  id: inception_3c_double_3x3_2_bn
+- {expr: inception_3c_double_3x3_2_bn<=ReLU<=inception_3c_double_3x3_2_bn, id: inception_3c_relu_double_3x3_2}
+- attrs: {kernel_size: 3, mode: max, stride: 2}
+  expr: inception_3c_pool<=Pooling<=inception_3b_output
+  id: inception_3c_pool
+- {expr: 'inception_3c_output<=Concat<=inception_3c_3x3_bn,inception_3c_double_3x3_2_bn,inception_3c_pool',
+  id: inception_3c_output}
+- attrs: {kernel_size: 1, num_output: 224}
+  expr: inception_4a_1x1<=Convolution<=inception_3c_output
+  id: inception_4a_1x1
+- attrs: {frozen: true}
+  expr: inception_4a_1x1_bn<=BN<=inception_4a_1x1
+  id: inception_4a_1x1_bn
+- {expr: inception_4a_1x1_bn<=ReLU<=inception_4a_1x1_bn, id: inception_4a_relu_1x1}
+- attrs: {kernel_size: 1, num_output: 64}
+  expr: inception_4a_3x3_reduce<=Convolution<=inception_3c_output
+  id: inception_4a_3x3_reduce
+- attrs: {frozen: true}
+  expr: inception_4a_3x3_reduce_bn<=BN<=inception_4a_3x3_reduce
+  id: inception_4a_3x3_reduce_bn
+- {expr: inception_4a_3x3_reduce_bn<=ReLU<=inception_4a_3x3_reduce_bn, id: inception_4a_relu_3x3_reduce}
+- attrs: {kernel_size: 3, num_output: 96, pad: 1}
+  expr: inception_4a_3x3<=Convolution<=inception_4a_3x3_reduce_bn
+  id: inception_4a_3x3
+- attrs: {frozen: true}
+  expr: inception_4a_3x3_bn<=BN<=inception_4a_3x3
+  id: inception_4a_3x3_bn
+- {expr: inception_4a_3x3_bn<=ReLU<=inception_4a_3x3_bn, id: inception_4a_relu_3x3}
+- attrs: {kernel_size: 1, num_output: 96}
+  expr: inception_4a_double_3x3_reduce<=Convolution<=inception_3c_output
+  id: inception_4a_double_3x3_reduce
+- attrs: {frozen: true}
+  expr: inception_4a_double_3x3_reduce_bn<=BN<=inception_4a_double_3x3_reduce
+  id: inception_4a_double_3x3_reduce_bn
+- {expr: inception_4a_double_3x3_reduce_bn<=ReLU<=inception_4a_double_3x3_reduce_bn,
+  id: inception_4a_relu_double_3x3_reduce}
+- attrs: {kernel_size: 3, num_output: 128, pad: 1}
+  expr: inception_4a_double_3x3_1<=Convolution<=inception_4a_double_3x3_reduce_bn
+  id: inception_4a_double_3x3_1
+- attrs: {frozen: true}
+  expr: inception_4a_double_3x3_1_bn<=BN<=inception_4a_double_3x3_1
+  id: inception_4a_double_3x3_1_bn
+- {expr: inception_4a_double_3x3_1_bn<=ReLU<=inception_4a_double_3x3_1_bn, id: inception_4a_relu_double_3x3_1}
+- attrs: {kernel_size: 3, num_output: 128, pad: 1}
+  expr: inception_4a_double_3x3_2<=Convolution<=inception_4a_double_3x3_1_bn
+  id: inception_4a_double_3x3_2
+- attrs: {frozen: true}
+  expr: inception_4a_double_3x3_2_bn<=BN<=inception_4a_double_3x3_2
+  id: inception_4a_double_3x3_2_bn
+- {expr: inception_4a_double_3x3_2_bn<=ReLU<=inception_4a_double_3x3_2_bn, id: inception_4a_relu_double_3x3_2}
+- attrs: {kernel_size: 3, mode: ave, pad: 1, stride: 1}
+  expr: inception_4a_pool<=Pooling<=inception_3c_output
+  id: inception_4a_pool
+- attrs: {kernel_size: 1, num_output: 128}
+  expr: inception_4a_pool_proj<=Convolution<=inception_4a_pool
+  id: inception_4a_pool_proj
+- attrs: {frozen: true}
+  expr: inception_4a_pool_proj_bn<=BN<=inception_4a_pool_proj
+  id: inception_4a_pool_proj_bn
+- {expr: inception_4a_pool_proj_bn<=ReLU<=inception_4a_pool_proj_bn, id: inception_4a_relu_pool_proj}
+- {expr: 'inception_4a_output<=Concat<=inception_4a_1x1_bn,inception_4a_3x3_bn,inception_4a_double_3x3_2_bn,inception_4a_pool_proj_bn',
+  id: inception_4a_output}
+- attrs: {kernel_size: 1, num_output: 192}
+  expr: inception_4b_1x1<=Convolution<=inception_4a_output
+  id: inception_4b_1x1
+- attrs: {frozen: true}
+  expr: inception_4b_1x1_bn<=BN<=inception_4b_1x1
+  id: inception_4b_1x1_bn
+- {expr: inception_4b_1x1_bn<=ReLU<=inception_4b_1x1_bn, id: inception_4b_relu_1x1}
+- attrs: {kernel_size: 1, num_output: 96}
+  expr: inception_4b_3x3_reduce<=Convolution<=inception_4a_output
+  id: inception_4b_3x3_reduce
+- attrs: {frozen: true}
+  expr: inception_4b_3x3_reduce_bn<=BN<=inception_4b_3x3_reduce
+  id: inception_4b_3x3_reduce_bn
+- {expr: inception_4b_3x3_reduce_bn<=ReLU<=inception_4b_3x3_reduce_bn, id: inception_4b_relu_3x3_reduce}
+- attrs: {kernel_size: 3, num_output: 128, pad: 1}
+  expr: inception_4b_3x3<=Convolution<=inception_4b_3x3_reduce_bn
+  id: inception_4b_3x3
+- attrs: {frozen: true}
+  expr: inception_4b_3x3_bn<=BN<=inception_4b_3x3
+  id: inception_4b_3x3_bn
+- {expr: inception_4b_3x3_bn<=ReLU<=inception_4b_3x3_bn, id: inception_4b_relu_3x3}
+- attrs: {kernel_size: 1, num_output: 96}
+  expr: inception_4b_double_3x3_reduce<=Convolution<=inception_4a_output
+  id: inception_4b_double_3x3_reduce
+- attrs: {frozen: true}
+  expr: inception_4b_double_3x3_reduce_bn<=BN<=inception_4b_double_3x3_reduce
+  id: inception_4b_double_3x3_reduce_bn
+- {expr: inception_4b_double_3x3_reduce_bn<=ReLU<=inception_4b_double_3x3_reduce_bn,
+  id: inception_4b_relu_double_3x3_reduce}
+- attrs: {kernel_size: 3, num_output: 128, pad: 1}
+  expr: inception_4b_double_3x3_1<=Convolution<=inception_4b_double_3x3_reduce_bn
+  id: inception_4b_double_3x3_1
+- attrs: {frozen: true}
+  expr: inception_4b_double_3x3_1_bn<=BN<=inception_4b_double_3x3_1
+  id: inception_4b_double_3x3_1_bn
+- {expr: inception_4b_double_3x3_1_bn<=ReLU<=inception_4b_double_3x3_1_bn, id: inception_4b_relu_double_3x3_1}
+- attrs: {kernel_size: 3, num_output: 128, pad: 1}
+  expr: inception_4b_double_3x3_2<=Convolution<=inception_4b_double_3x3_1_bn
+  id: inception_4b_double_3x3_2
+- attrs: {frozen: true}
+  expr: inception_4b_double_3x3_2_bn<=BN<=inception_4b_double_3x3_2
+  id: inception_4b_double_3x3_2_bn
+- {expr: inception_4b_double_3x3_2_bn<=ReLU<=inception_4b_double_3x3_2_bn, id: inception_4b_relu_double_3x3_2}
+- attrs: {kernel_size: 3, mode: ave, pad: 1, stride: 1}
+  expr: inception_4b_pool<=Pooling<=inception_4a_output
+  id: inception_4b_pool
+- attrs: {kernel_size: 1, num_output: 128}
+  expr: inception_4b_pool_proj<=Convolution<=inception_4b_pool
+  id: inception_4b_pool_proj
+- attrs: {frozen: true}
+  expr: inception_4b_pool_proj_bn<=BN<=inception_4b_pool_proj
+  id: inception_4b_pool_proj_bn
+- {expr: inception_4b_pool_proj_bn<=ReLU<=inception_4b_pool_proj_bn, id: inception_4b_relu_pool_proj}
+- {expr: 'inception_4b_output<=Concat<=inception_4b_1x1_bn,inception_4b_3x3_bn,inception_4b_double_3x3_2_bn,inception_4b_pool_proj_bn',
+  id: inception_4b_output}
+- attrs: {kernel_size: 1, num_output: 160}
+  expr: inception_4c_1x1<=Convolution<=inception_4b_output
+  id: inception_4c_1x1
+- attrs: {frozen: true}
+  expr: inception_4c_1x1_bn<=BN<=inception_4c_1x1
+  id: inception_4c_1x1_bn
+- {expr: inception_4c_1x1_bn<=ReLU<=inception_4c_1x1_bn, id: inception_4c_relu_1x1}
+- attrs: {kernel_size: 1, num_output: 128}
+  expr: inception_4c_3x3_reduce<=Convolution<=inception_4b_output
+  id: inception_4c_3x3_reduce
+- attrs: {frozen: true}
+  expr: inception_4c_3x3_reduce_bn<=BN<=inception_4c_3x3_reduce
+  id: inception_4c_3x3_reduce_bn
+- {expr: inception_4c_3x3_reduce_bn<=ReLU<=inception_4c_3x3_reduce_bn, id: inception_4c_relu_3x3_reduce}
+- attrs: {kernel_size: 3, num_output: 160, pad: 1}
+  expr: inception_4c_3x3<=Convolution<=inception_4c_3x3_reduce_bn
+  id: inception_4c_3x3
+- attrs: {frozen: true}
+  expr: inception_4c_3x3_bn<=BN<=inception_4c_3x3
+  id: inception_4c_3x3_bn
+- {expr: inception_4c_3x3_bn<=ReLU<=inception_4c_3x3_bn, id: inception_4c_relu_3x3}
+- attrs: {kernel_size: 1, num_output: 128}
+  expr: inception_4c_double_3x3_reduce<=Convolution<=inception_4b_output
+  id: inception_4c_double_3x3_reduce
+- attrs: {frozen: true}
+  expr: inception_4c_double_3x3_reduce_bn<=BN<=inception_4c_double_3x3_reduce
+  id: inception_4c_double_3x3_reduce_bn
+- {expr: inception_4c_double_3x3_reduce_bn<=ReLU<=inception_4c_double_3x3_reduce_bn,
+  id: inception_4c_relu_double_3x3_reduce}
+- attrs: {kernel_size: 3, num_output: 160, pad: 1}
+  expr: inception_4c_double_3x3_1<=Convolution<=inception_4c_double_3x3_reduce_bn
+  id: inception_4c_double_3x3_1
+- attrs: {frozen: true}
+  expr: inception_4c_double_3x3_1_bn<=BN<=inception_4c_double_3x3_1
+  id: inception_4c_double_3x3_1_bn
+- {expr: inception_4c_double_3x3_1_bn<=ReLU<=inception_4c_double_3x3_1_bn, id: inception_4c_relu_double_3x3_1}
+- attrs: {kernel_size: 3, num_output: 160, pad: 1}
+  expr: inception_4c_double_3x3_2<=Convolution<=inception_4c_double_3x3_1_bn
+  id: inception_4c_double_3x3_2
+- attrs: {frozen: true}
+  expr: inception_4c_double_3x3_2_bn<=BN<=inception_4c_double_3x3_2
+  id: inception_4c_double_3x3_2_bn
+- {expr: inception_4c_double_3x3_2_bn<=ReLU<=inception_4c_double_3x3_2_bn, id: inception_4c_relu_double_3x3_2}
+- attrs: {kernel_size: 3, mode: ave, pad: 1, stride: 1}
+  expr: inception_4c_pool<=Pooling<=inception_4b_output
+  id: inception_4c_pool
+- attrs: {kernel_size: 1, num_output: 128}
+  expr: inception_4c_pool_proj<=Convolution<=inception_4c_pool
+  id: inception_4c_pool_proj
+- attrs: {frozen: true}
+  expr: inception_4c_pool_proj_bn<=BN<=inception_4c_pool_proj
+  id: inception_4c_pool_proj_bn
+- {expr: inception_4c_pool_proj_bn<=ReLU<=inception_4c_pool_proj_bn, id: inception_4c_relu_pool_proj}
+- {expr: 'inception_4c_output<=Concat<=inception_4c_1x1_bn,inception_4c_3x3_bn,inception_4c_double_3x3_2_bn,inception_4c_pool_proj_bn',
+  id: inception_4c_output}
+- attrs: {kernel_size: 1, num_output: 96}
+  expr: inception_4d_1x1<=Convolution<=inception_4c_output
+  id: inception_4d_1x1
+- attrs: {frozen: true}
+  expr: inception_4d_1x1_bn<=BN<=inception_4d_1x1
+  id: inception_4d_1x1_bn
+- {expr: inception_4d_1x1_bn<=ReLU<=inception_4d_1x1_bn, id: inception_4d_relu_1x1}
+- attrs: {kernel_size: 1, num_output: 128}
+  expr: inception_4d_3x3_reduce<=Convolution<=inception_4c_output
+  id: inception_4d_3x3_reduce
+- attrs: {frozen: true}
+  expr: inception_4d_3x3_reduce_bn<=BN<=inception_4d_3x3_reduce
+  id: inception_4d_3x3_reduce_bn
+- {expr: inception_4d_3x3_reduce_bn<=ReLU<=inception_4d_3x3_reduce_bn, id: inception_4d_relu_3x3_reduce}
+- attrs: {kernel_size: 3, num_output: 192, pad: 1}
+  expr: inception_4d_3x3<=Convolution<=inception_4d_3x3_reduce_bn
+  id: inception_4d_3x3
+- attrs: {frozen: true}
+  expr: inception_4d_3x3_bn<=BN<=inception_4d_3x3
+  id: inception_4d_3x3_bn
+- {expr: inception_4d_3x3_bn<=ReLU<=inception_4d_3x3_bn, id: inception_4d_relu_3x3}
+- attrs: {kernel_size: 1, num_output: 160}
+  expr: inception_4d_double_3x3_reduce<=Convolution<=inception_4c_output
+  id: inception_4d_double_3x3_reduce
+- attrs: {frozen: true}
+  expr: inception_4d_double_3x3_reduce_bn<=BN<=inception_4d_double_3x3_reduce
+  id: inception_4d_double_3x3_reduce_bn
+- {expr: inception_4d_double_3x3_reduce_bn<=ReLU<=inception_4d_double_3x3_reduce_bn,
+  id: inception_4d_relu_double_3x3_reduce}
+- attrs: {kernel_size: 3, num_output: 192, pad: 1}
+  expr: inception_4d_double_3x3_1<=Convolution<=inception_4d_double_3x3_reduce_bn
+  id: inception_4d_double_3x3_1
+- attrs: {frozen: true}
+  expr: inception_4d_double_3x3_1_bn<=BN<=inception_4d_double_3x3_1
+  id: inception_4d_double_3x3_1_bn
+- {expr: inception_4d_double_3x3_1_bn<=ReLU<=inception_4d_double_3x3_1_bn, id: inception_4d_relu_double_3x3_1}
+- attrs: {kernel_size: 3, num_output: 192, pad: 1}
+  expr: inception_4d_double_3x3_2<=Convolution<=inception_4d_double_3x3_1_bn
+  id: inception_4d_double_3x3_2
+- attrs: {frozen: true}
+  expr: inception_4d_double_3x3_2_bn<=BN<=inception_4d_double_3x3_2
+  id: inception_4d_double_3x3_2_bn
+- {expr: inception_4d_double_3x3_2_bn<=ReLU<=inception_4d_double_3x3_2_bn, id: inception_4d_relu_double_3x3_2}
+- attrs: {kernel_size: 3, mode: ave, pad: 1, stride: 1}
+  expr: inception_4d_pool<=Pooling<=inception_4c_output
+  id: inception_4d_pool
+- attrs: {kernel_size: 1, num_output: 128}
+  expr: inception_4d_pool_proj<=Convolution<=inception_4d_pool
+  id: inception_4d_pool_proj
+- attrs: {frozen: true}
+  expr: inception_4d_pool_proj_bn<=BN<=inception_4d_pool_proj
+  id: inception_4d_pool_proj_bn
+- {expr: inception_4d_pool_proj_bn<=ReLU<=inception_4d_pool_proj_bn, id: inception_4d_relu_pool_proj}
+- {expr: 'inception_4d_output<=Concat<=inception_4d_1x1_bn,inception_4d_3x3_bn,inception_4d_double_3x3_2_bn,inception_4d_pool_proj_bn',
+  id: inception_4d_output}
+- attrs: {kernel_size: 1, num_output: 128}
+  expr: inception_4e_3x3_reduce<=Convolution<=inception_4d_output
+  id: inception_4e_3x3_reduce
+- attrs: {frozen: true}
+  expr: inception_4e_3x3_reduce_bn<=BN<=inception_4e_3x3_reduce
+  id: inception_4e_3x3_reduce_bn
+- {expr: inception_4e_3x3_reduce_bn<=ReLU<=inception_4e_3x3_reduce_bn, id: inception_4e_relu_3x3_reduce}
+- attrs: {kernel_size: 3, num_output: 192, pad: 1, stride: 2}
+  expr: inception_4e_3x3<=Convolution<=inception_4e_3x3_reduce_bn
+  id: inception_4e_3x3
+- attrs: {frozen: true}
+  expr: inception_4e_3x3_bn<=BN<=inception_4e_3x3
+  id: inception_4e_3x3_bn
+- {expr: inception_4e_3x3_bn<=ReLU<=inception_4e_3x3_bn, id: inception_4e_relu_3x3}
+- attrs: {kernel_size: 1, num_output: 192}
+  expr: inception_4e_double_3x3_reduce<=Convolution<=inception_4d_output
+  id: inception_4e_double_3x3_reduce
+- attrs: {frozen: true}
+  expr: inception_4e_double_3x3_reduce_bn<=BN<=inception_4e_double_3x3_reduce
+  id: inception_4e_double_3x3_reduce_bn
+- {expr: inception_4e_double_3x3_reduce_bn<=ReLU<=inception_4e_double_3x3_reduce_bn,
+  id: inception_4e_relu_double_3x3_reduce}
+- attrs: {kernel_size: 3, num_output: 256, pad: 1}
+  expr: inception_4e_double_3x3_1<=Convolution<=inception_4e_double_3x3_reduce_bn
+  id: inception_4e_double_3x3_1
+- attrs: {frozen: true}
+  expr: inception_4e_double_3x3_1_bn<=BN<=inception_4e_double_3x3_1
+  id: inception_4e_double_3x3_1_bn
+- {expr: inception_4e_double_3x3_1_bn<=ReLU<=inception_4e_double_3x3_1_bn, id: inception_4e_relu_double_3x3_1}
+- attrs: {kernel_size: 3, num_output: 256, pad: 1, stride: 2}
+  expr: inception_4e_double_3x3_2<=Convolution<=inception_4e_double_3x3_1_bn
+  id: inception_4e_double_3x3_2
+- attrs: {frozen: true}
+  expr: inception_4e_double_3x3_2_bn<=BN<=inception_4e_double_3x3_2
+  id: inception_4e_double_3x3_2_bn
+- {expr: inception_4e_double_3x3_2_bn<=ReLU<=inception_4e_double_3x3_2_bn, id: inception_4e_relu_double_3x3_2}
+- attrs: {kernel_size: 3, mode: max, stride: 2}
+  expr: inception_4e_pool<=Pooling<=inception_4d_output
+  id: inception_4e_pool
+- {expr: 'inception_4e_output<=Concat<=inception_4e_3x3_bn,inception_4e_double_3x3_2_bn,inception_4e_pool',
+  id: inception_4e_output}
+- attrs: {kernel_size: 1, num_output: 352}
+  expr: inception_5a_1x1<=Convolution<=inception_4e_output
+  id: inception_5a_1x1
+- attrs: {frozen: true}
+  expr: inception_5a_1x1_bn<=BN<=inception_5a_1x1
+  id: inception_5a_1x1_bn
+- {expr: inception_5a_1x1_bn<=ReLU<=inception_5a_1x1_bn, id: inception_5a_relu_1x1}
+- attrs: {kernel_size: 1, num_output: 192}
+  expr: inception_5a_3x3_reduce<=Convolution<=inception_4e_output
+  id: inception_5a_3x3_reduce
+- attrs: {frozen: true}
+  expr: inception_5a_3x3_reduce_bn<=BN<=inception_5a_3x3_reduce
+  id: inception_5a_3x3_reduce_bn
+- {expr: inception_5a_3x3_reduce_bn<=ReLU<=inception_5a_3x3_reduce_bn, id: inception_5a_relu_3x3_reduce}
+- attrs: {kernel_size: 3, num_output: 320, pad: 1}
+  expr: inception_5a_3x3<=Convolution<=inception_5a_3x3_reduce_bn
+  id: inception_5a_3x3
+- attrs: {frozen: true}
+  expr: inception_5a_3x3_bn<=BN<=inception_5a_3x3
+  id: inception_5a_3x3_bn
+- {expr: inception_5a_3x3_bn<=ReLU<=inception_5a_3x3_bn, id: inception_5a_relu_3x3}
+- attrs: {kernel_size: 1, num_output: 160}
+  expr: inception_5a_double_3x3_reduce<=Convolution<=inception_4e_output
+  id: inception_5a_double_3x3_reduce
+- attrs: {frozen: true}
+  expr: inception_5a_double_3x3_reduce_bn<=BN<=inception_5a_double_3x3_reduce
+  id: inception_5a_double_3x3_reduce_bn
+- {expr: inception_5a_double_3x3_reduce_bn<=ReLU<=inception_5a_double_3x3_reduce_bn,
+  id: inception_5a_relu_double_3x3_reduce}
+- attrs: {kernel_size: 3, num_output: 224, pad: 1}
+  expr: inception_5a_double_3x3_1<=Convolution<=inception_5a_double_3x3_reduce_bn
+  id: inception_5a_double_3x3_1
+- attrs: {frozen: true}
+  expr: inception_5a_double_3x3_1_bn<=BN<=inception_5a_double_3x3_1
+  id: inception_5a_double_3x3_1_bn
+- {expr: inception_5a_double_3x3_1_bn<=ReLU<=inception_5a_double_3x3_1_bn, id: inception_5a_relu_double_3x3_1}
+- attrs: {kernel_size: 3, num_output: 224, pad: 1}
+  expr: inception_5a_double_3x3_2<=Convolution<=inception_5a_double_3x3_1_bn
+  id: inception_5a_double_3x3_2
+- attrs: {frozen: true}
+  expr: inception_5a_double_3x3_2_bn<=BN<=inception_5a_double_3x3_2
+  id: inception_5a_double_3x3_2_bn
+- {expr: inception_5a_double_3x3_2_bn<=ReLU<=inception_5a_double_3x3_2_bn, id: inception_5a_relu_double_3x3_2}
+- attrs: {kernel_size: 3, mode: ave, pad: 1, stride: 1}
+  expr: inception_5a_pool<=Pooling<=inception_4e_output
+  id: inception_5a_pool
+- attrs: {kernel_size: 1, num_output: 128}
+  expr: inception_5a_pool_proj<=Convolution<=inception_5a_pool
+  id: inception_5a_pool_proj
+- attrs: {frozen: true}
+  expr: inception_5a_pool_proj_bn<=BN<=inception_5a_pool_proj
+  id: inception_5a_pool_proj_bn
+- {expr: inception_5a_pool_proj_bn<=ReLU<=inception_5a_pool_proj_bn, id: inception_5a_relu_pool_proj}
+- {expr: 'inception_5a_output<=Concat<=inception_5a_1x1_bn,inception_5a_3x3_bn,inception_5a_double_3x3_2_bn,inception_5a_pool_proj_bn',
+  id: inception_5a_output}
+- attrs: {kernel_size: 1, num_output: 352}
+  expr: inception_5b_1x1<=Convolution<=inception_5a_output
+  id: inception_5b_1x1
+- attrs: {frozen: true}
+  expr: inception_5b_1x1_bn<=BN<=inception_5b_1x1
+  id: inception_5b_1x1_bn
+- {expr: inception_5b_1x1_bn<=ReLU<=inception_5b_1x1_bn, id: inception_5b_relu_1x1}
+- attrs: {kernel_size: 1, num_output: 192}
+  expr: inception_5b_3x3_reduce<=Convolution<=inception_5a_output
+  id: inception_5b_3x3_reduce
+- attrs: {frozen: true}
+  expr: inception_5b_3x3_reduce_bn<=BN<=inception_5b_3x3_reduce
+  id: inception_5b_3x3_reduce_bn
+- {expr: inception_5b_3x3_reduce_bn<=ReLU<=inception_5b_3x3_reduce_bn, id: inception_5b_relu_3x3_reduce}
+- attrs: {kernel_size: 3, num_output: 320, pad: 1}
+  expr: inception_5b_3x3<=Convolution<=inception_5b_3x3_reduce_bn
+  id: inception_5b_3x3
+- attrs: {frozen: true}
+  expr: inception_5b_3x3_bn<=BN<=inception_5b_3x3
+  id: inception_5b_3x3_bn
+- {expr: inception_5b_3x3_bn<=ReLU<=inception_5b_3x3_bn, id: inception_5b_relu_3x3}
+- attrs: {kernel_size: 1, num_output: 192}
+  expr: inception_5b_double_3x3_reduce<=Convolution<=inception_5a_output
+  id: inception_5b_double_3x3_reduce
+- attrs: {frozen: true}
+  expr: inception_5b_double_3x3_reduce_bn<=BN<=inception_5b_double_3x3_reduce
+  id: inception_5b_double_3x3_reduce_bn
+- {expr: inception_5b_double_3x3_reduce_bn<=ReLU<=inception_5b_double_3x3_reduce_bn,
+  id: inception_5b_relu_double_3x3_reduce}
+- attrs: {kernel_size: 3, num_output: 224, pad: 1}
+  expr: inception_5b_double_3x3_1<=Convolution<=inception_5b_double_3x3_reduce_bn
+  id: inception_5b_double_3x3_1
+- attrs: {frozen: true}
+  expr: inception_5b_double_3x3_1_bn<=BN<=inception_5b_double_3x3_1
+  id: inception_5b_double_3x3_1_bn
+- {expr: inception_5b_double_3x3_1_bn<=ReLU<=inception_5b_double_3x3_1_bn, id: inception_5b_relu_double_3x3_1}
+- attrs: {kernel_size: 3, num_output: 224, pad: 1}
+  expr: inception_5b_double_3x3_2<=Convolution<=inception_5b_double_3x3_1_bn
+  id: inception_5b_double_3x3_2
+- attrs: {frozen: true}
+  expr: inception_5b_double_3x3_2_bn<=BN<=inception_5b_double_3x3_2
+  id: inception_5b_double_3x3_2_bn
+- {expr: inception_5b_double_3x3_2_bn<=ReLU<=inception_5b_double_3x3_2_bn, id: inception_5b_relu_double_3x3_2}
+- attrs: {kernel_size: 3, mode: max, pad: 1, stride: 1}
+  expr: inception_5b_pool<=Pooling<=inception_5a_output
+  id: inception_5b_pool
+- attrs: {kernel_size: 1, num_output: 128}
+  expr: inception_5b_pool_proj<=Convolution<=inception_5b_pool
+  id: inception_5b_pool_proj
+- attrs: {frozen: true}
+  expr: inception_5b_pool_proj_bn<=BN<=inception_5b_pool_proj
+  id: inception_5b_pool_proj_bn
+- {expr: inception_5b_pool_proj_bn<=ReLU<=inception_5b_pool_proj_bn, id: inception_5b_relu_pool_proj}
+- {expr: 'inception_5b_output<=Concat<=inception_5b_1x1_bn,inception_5b_3x3_bn,inception_5b_double_3x3_2_bn,inception_5b_pool_proj_bn',
+  id: inception_5b_output}
+- attrs: {kernel_size: 7, mode: ave, stride: 1}
+  expr: global_pool<=Pooling<=inception_5b_output
+  id: global_pool
+#- attrs: {num_output: 1000}
+#  expr: fc_action<=InnerProduct<=global_pool
+#  id: fc
+name: BN-Inception
diff --git a/feature_extraction/bn_inception/tsn/bninception/layer_factory.py b/feature_extraction/bn_inception/tsn/bninception/layer_factory.py
new file mode 100644
index 0000000..be02c3f
--- /dev/null
+++ b/feature_extraction/bn_inception/tsn/bninception/layer_factory.py
@@ -0,0 +1,84 @@
+import torch
+from torch import nn
+
+
+LAYER_BUILDER_DICT=dict()
+
+
+def parse_expr(expr):
+    parts = expr.split('<=')
+    return parts[0].split(','), parts[1], parts[2].split(',')
+
+
+def get_basic_layer(info, channels=None, conv_bias=False):
+    id = info['id']
+    attr = info['attrs'] if 'attrs' in info else list()
+
+    out, op, in_vars = parse_expr(info['expr'])
+    assert(len(out) == 1)
+    assert(len(in_vars) == 1)
+    mod, out_channel, = LAYER_BUILDER_DICT[op](attr, channels, conv_bias)
+
+    return id, out[0], mod, out_channel, in_vars[0]
+
+
+def build_conv(attr, channels=None, conv_bias=False):
+    out_channels = attr['num_output']
+    ks = attr['kernel_size'] if 'kernel_size' in attr else (attr['kernel_h'], attr['kernel_w'])
+    if 'pad' in attr or 'pad_w' in attr and 'pad_h' in attr:
+        padding = attr['pad'] if 'pad' in attr else (attr['pad_h'], attr['pad_w'])
+    else:
+        padding = 0
+    if 'stride' in attr or 'stride_w' in attr and 'stride_h' in attr:
+        stride = attr['stride'] if 'stride' in attr else (attr['stride_h'], attr['stride_w'])
+    else:
+        stride = 1
+
+    conv = nn.Conv2d(channels, out_channels, ks, stride, padding, bias=conv_bias)
+
+    return conv, out_channels
+
+
+def build_pooling(attr, channels=None, conv_bias=False):
+    method = attr['mode']
+    pad = attr['pad'] if 'pad' in attr else 0
+    if method == 'max':
+        pool = nn.MaxPool2d(attr['kernel_size'], attr['stride'], pad,
+                            ceil_mode=True) # all Caffe pooling use ceil model
+    elif method == 'ave':
+        pool = nn.AvgPool2d(attr['kernel_size'], attr['stride'], pad,
+                            ceil_mode=True)  # all Caffe pooling use ceil model
+    else:
+        raise ValueError("Unknown pooling method: {}".format(method))
+
+    return pool, channels
+
+
+def build_relu(attr, channels=None, conv_bias=False):
+    return nn.ReLU(inplace=True), channels
+
+
+def build_bn(attr, channels=None, conv_bias=False):
+    return nn.BatchNorm2d(channels, momentum=0.1), channels
+
+
+def build_linear(attr, channels=None, conv_bias=False):
+    return nn.Linear(channels, attr['num_output']), channels
+
+
+def build_dropout(attr, channels=None, conv_bias=False):
+    return nn.Dropout(p=attr['dropout_ratio']), channels
+
+
+LAYER_BUILDER_DICT['Convolution'] = build_conv
+
+LAYER_BUILDER_DICT['Pooling'] = build_pooling
+
+LAYER_BUILDER_DICT['ReLU'] = build_relu
+
+LAYER_BUILDER_DICT['Dropout'] = build_dropout
+
+LAYER_BUILDER_DICT['BN'] = build_bn
+
+LAYER_BUILDER_DICT['InnerProduct'] = build_linear
+
diff --git a/feature_extraction/bn_inception/tsn/bninception/pytorch_load.py b/feature_extraction/bn_inception/tsn/bninception/pytorch_load.py
new file mode 100644
index 0000000..a741178
--- /dev/null
+++ b/feature_extraction/bn_inception/tsn/bninception/pytorch_load.py
@@ -0,0 +1,70 @@
+import torch
+from torch import nn
+from .layer_factory import get_basic_layer, parse_expr
+import torch.utils.model_zoo as model_zoo
+import yaml
+
+
+class BNInception(nn.Module):
+    def __init__(self, model_path='tsn/bninception/bn_inception.yaml', num_classes=101,
+                       weight_url='https://yjxiong.blob.core.windows.net/models/bn_inception-9f5701afb96c8044.pth'):
+        super(BNInception, self).__init__()
+
+        manifest = yaml.load(open(model_path))
+
+        layers = manifest['layers']
+
+        self._channel_dict = dict()
+
+        self._op_list = list()
+        for l in layers:
+            out_var, op, in_var = parse_expr(l['expr'])
+            if op != 'Concat':
+                id, out_name, module, out_channel, in_name = get_basic_layer(l,
+                                                                3 if len(self._channel_dict) == 0 else self._channel_dict[in_var[0]],
+                                                                             conv_bias=True)
+
+                self._channel_dict[out_name] = out_channel
+                setattr(self, id, module)
+                self._op_list.append((id, op, out_name, in_name))
+            else:
+                self._op_list.append((id, op, out_var[0], in_var))
+                channel = sum([self._channel_dict[x] for x in in_var])
+                self._channel_dict[out_var[0]] = channel
+
+        """compatible to modified model"""
+        state_dict = torch.utils.model_zoo.load_url(weight_url)
+        state_dict = {k: v for k, v in state_dict.items() if k in self.state_dict()}
+
+        """compatible to pytorch 0.4.0"""
+        for name, weights in state_dict.items():
+            if self.state_dict()[name].shape != weights.shape:
+                state_dict[name] = weights.squeeze(0)
+
+        self.load_state_dict(state_dict)
+
+    def forward(self, input):
+        data_dict = dict()
+        data_dict[self._op_list[0][-1]] = input
+
+        def get_hook(name):
+
+            def hook(m, grad_in, grad_out):
+                print(name, grad_out[0].data.abs().mean())
+
+            return hook
+        for op in self._op_list:
+            if op[1] != 'Concat' and op[1] != 'InnerProduct':
+                data_dict[op[2]] = getattr(self, op[0])(data_dict[op[-1]])
+                # getattr(self, op[0]).register_backward_hook(get_hook(op[0]))
+            elif op[1] == 'InnerProduct':
+                x = data_dict[op[-1]]
+                data_dict[op[2]] = getattr(self, op[0])(x.view(x.size(0), -1))
+            else:
+                try:
+                    data_dict[op[2]] = torch.cat(tuple(data_dict[x] for x in op[-1]), 1)
+                except:
+                    for x in op[-1]:
+                        print(x,data_dict[x].size())
+                    raise
+        return data_dict[self._op_list[-1][2]]
\ No newline at end of file
diff --git a/feature_extraction/bn_inception/tsn/models.py b/feature_extraction/bn_inception/tsn/models.py
new file mode 100644
index 0000000..d78c7af
--- /dev/null
+++ b/feature_extraction/bn_inception/tsn/models.py
@@ -0,0 +1,76 @@
+from torch import nn
+from .ops.basic_ops import ConsensusModule, Identity
+from torch.nn.init import normal, constant
+from .bninception.pytorch_load import BNInception
+
+class TSN(nn.Module):
+    def __init__(self, modality,
+                 consensus_type='avg', before_softmax=True,
+                 dropout=0.8, partial_bn=True):
+        super(TSN, self).__init__()
+        self.modality = modality
+        self.reshape = True
+        self.before_softmax = before_softmax
+        self.dropout = dropout
+        self.consensus_type = consensus_type
+        if not before_softmax and consensus_type != 'avg':
+            raise ValueError("Only avg consensus can be used after Softmax")
+
+        self._prepare_base_model()
+        if self.modality == 'Flow':
+            print("Converting the ImageNet model to a flow init model")
+            self.base_model = self._construct_flow_model(self.base_model)
+            print("Done. Flow model ready...")
+
+        self.consensus = ConsensusModule(consensus_type)
+
+        if not self.before_softmax:
+            self.softmax = nn.Softmax()
+
+        self._enable_pbn = partial_bn
+        if partial_bn:
+            self.partialBN(True)
+
+    def _prepare_base_model(self):        
+        self.base_model = BNInception()
+        self.base_model.last_layer_name = 'fc'
+        self.input_size = 224
+        self.input_mean = [104, 117, 128]
+        self.input_std = [1]
+
+        if self.modality == 'Flow':
+            self.input_mean = [128]        
+    
+    def partialBN(self, enable):
+        self._enable_pbn = enable
+
+    def forward(self, input):
+        sample_len = 3 if self.modality == "RGB" else 2
+
+        input_reshape = input.view((-1, sample_len) + input.size()[-2:])
+        base_out = self.base_model(input_reshape)
+        if base_out.data.shape[-1] == 1 and base_out.data.shape[-2] == 1:
+            base_out = base_out       
+        return base_out
+
+    def _construct_flow_model(self, base_model):
+        modules = list(self.base_model.modules())
+        first_conv_idx = list(filter(lambda x: isinstance(modules[x], nn.Conv2d), list(range(len(modules)))))[0]
+        conv_layer = modules[first_conv_idx]
+        container = modules[first_conv_idx - 1]
+
+        params = [x.clone() for x in conv_layer.parameters()]
+        kernel_size = params[0].size()
+        new_kernel_size = kernel_size[:1] + (2, ) + kernel_size[2:]
+        new_kernels = params[0].data.mean(dim=1, keepdim=True).expand(new_kernel_size).contiguous()
+
+        new_conv = nn.Conv2d(2, conv_layer.out_channels,
+                             conv_layer.kernel_size, conv_layer.stride, conv_layer.padding,
+                             bias=True if len(params) == 2 else False)
+        new_conv.weight.data = new_kernels
+        if len(params) == 2:
+            new_conv.bias.data = params[1].data # add bias if neccessary
+        layer_name = list(container.state_dict().keys())[0][:-7] # remove .weight suffix to get the layer name
+
+        setattr(container, layer_name, new_conv)
+        return base_model
\ No newline at end of file
diff --git a/feature_extraction/bn_inception/tsn/ops/__init__.py b/feature_extraction/bn_inception/tsn/ops/__init__.py
new file mode 100644
index 0000000..9e2259a
--- /dev/null
+++ b/feature_extraction/bn_inception/tsn/ops/__init__.py
@@ -0,0 +1 @@
+from .basic_ops import *
\ No newline at end of file
diff --git a/feature_extraction/bn_inception/tsn/ops/basic_ops.py b/feature_extraction/bn_inception/tsn/ops/basic_ops.py
new file mode 100644
index 0000000..6a316f9
--- /dev/null
+++ b/feature_extraction/bn_inception/tsn/ops/basic_ops.py
@@ -0,0 +1,47 @@
+import torch
+import math
+
+
+class Identity(torch.nn.Module):
+    def forward(self, input):
+        return input
+
+
+class SegmentConsensus(torch.autograd.Function):
+
+    def __init__(self, consensus_type, dim=1):
+        self.consensus_type = consensus_type
+        self.dim = dim
+        self.shape = None
+
+    def forward(self, input_tensor):
+        self.shape = input_tensor.size()
+        if self.consensus_type == 'avg':
+            output = input_tensor.mean(dim=self.dim, keepdim=True)
+        elif self.consensus_type == 'identity':
+            output = input_tensor
+        else:
+            output = None
+
+        return output
+
+    def backward(self, grad_output):
+        if self.consensus_type == 'avg':
+            grad_in = grad_output.expand(self.shape) / float(self.shape[self.dim])
+        elif self.consensus_type == 'identity':
+            grad_in = grad_output
+        else:
+            grad_in = None
+
+        return grad_in
+
+
+class ConsensusModule(torch.nn.Module):
+
+    def __init__(self, consensus_type, dim=1):
+        super(ConsensusModule, self).__init__()
+        self.consensus_type = consensus_type if consensus_type != 'rnn' else 'identity'
+        self.dim = dim
+
+    def forward(self, input):
+        return SegmentConsensus(self.consensus_type, self.dim)(input)
diff --git a/feature_extraction/bn_inception/tsn/ops/utils.py b/feature_extraction/bn_inception/tsn/ops/utils.py
new file mode 100644
index 0000000..9f71e2b
--- /dev/null
+++ b/feature_extraction/bn_inception/tsn/ops/utils.py
@@ -0,0 +1,35 @@
+import torch
+import numpy as np
+from sklearn.metrics import confusion_matrix
+
+def get_grad_hook(name):
+    def hook(m, grad_in, grad_out):
+        print((name, grad_out[0].data.abs().mean(), grad_in[0].data.abs().mean()))
+        print((grad_out[0].size()))
+        print((grad_in[0].size()))
+
+        print((grad_out[0]))
+        print((grad_in[0]))
+
+    return hook
+
+
+def softmax(scores):
+    es = np.exp(scores - scores.max(axis=-1)[..., None])
+    return es / es.sum(axis=-1)[..., None]
+
+
+def log_add(log_a, log_b):
+    return log_a + np.log(1 + np.exp(log_b - log_a))
+
+
+def class_accuracy(prediction, label):
+    cf = confusion_matrix(prediction, label)
+    cls_cnt = cf.sum(axis=1)
+    cls_hit = np.diag(cf)
+
+    cls_acc = cls_hit / cls_cnt.astype(float)
+
+    mean_cls_acc = cls_acc.mean()
+
+    return cls_acc, mean_cls_acc
\ No newline at end of file
diff --git a/feature_extraction/demo_utils.py b/feature_extraction/demo_utils.py
new file mode 100644
index 0000000..9d99823
--- /dev/null
+++ b/feature_extraction/demo_utils.py
@@ -0,0 +1,344 @@
+'''
+The code is partially borrowed from:
+https://github.com/v-iashin/video_features/blob/861efaa4ed67/utils/utils.py
+and
+https://github.com/PeihaoChen/regnet/blob/199609/extract_audio_and_video.py
+'''
+import os
+import shutil
+import subprocess
+from glob import glob
+from pathlib import Path
+from typing import Dict
+
+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+import torchvision.models as models
+import torchvision.transforms as transforms
+import torchvision.transforms.functional as F
+from omegaconf.omegaconf import OmegaConf
+from sample_visualization import (load_feature_extractor,
+                                  load_model_from_config, load_vocoder)
+from specvqgan.data.vggsound import CropFeats
+from specvqgan.util import download, md5_hash
+from train import instantiate_from_config
+
+from feature_extraction.extract_mel_spectrogram import get_spectrogram
+
+plt.rcParams['savefig.bbox'] = 'tight'
+
+
+def which_ffmpeg() -> str:
+    '''Determines the path to ffmpeg library
+
+    Returns:
+        str -- path to the library
+    '''
+    result = subprocess.run(['which', 'ffmpeg'], stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
+    ffmpeg_path = result.stdout.decode('utf-8').replace('\n', '')
+    return ffmpeg_path
+
+def which_ffprobe() -> str:
+    '''Determines the path to ffprobe library
+
+    Returns:
+        str -- path to the library
+    '''
+    result = subprocess.run(['which', 'ffprobe'], stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
+    ffprobe_path = result.stdout.decode('utf-8').replace('\n', '')
+    return ffprobe_path
+
+
+def get_duration(path):
+    assert which_ffprobe() != '', 'Is ffmpeg installed? Check if the conda environment is activated.'
+    cmd = f'{which_ffprobe()} -hide_banner -loglevel panic' \
+          f' -v error -show_entries format=duration -of default=noprint_wrappers=1:nokey=1 {path}'
+    result = subprocess.run(cmd.split(), stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
+    duration = float(result.stdout.decode('utf-8').replace('\n', ''))
+    return duration
+
+def trim_video(video_path: str, start: int, trim_duration: int = 10, tmp_path: str = './tmp'):
+    assert which_ffmpeg() != '', 'Is ffmpeg installed? Check if the conda environment is activated.'
+    if Path(video_path).suffix != '.mp4':
+        print(f'File Extension is not `mp4` (it is {Path(video_path).suffix}). It will be re-encoded to mp4.')
+
+    video_duration = get_duration(video_path)
+    print('Video Duration:', video_duration)
+    assert video_duration > start, f'Video Duration < Trim Start: {video_duration} < {start}'
+
+    # create tmp dir if doesn't exist
+    os.makedirs(tmp_path, exist_ok=True)
+    trim_vid_path = os.path.join(tmp_path, f'{Path(video_path).stem}_trim_to_{trim_duration}s.mp4')
+    cmd = f'{which_ffmpeg()} -hide_banner -loglevel panic' \
+          f' -i {video_path} -ss {start} -t {trim_duration} -y {trim_vid_path}'
+    subprocess.call(cmd.split())
+    print('Trimmed the input video', video_path, 'and saved the output @', trim_vid_path)
+
+    return trim_vid_path
+
+
+def reencode_video_with_diff_fps(video_path: str, tmp_path: str, extraction_fps: int) -> str:
+    '''Reencodes the video given the path and saves it to the tmp_path folder.
+
+    Args:
+        video_path (str): original video
+        tmp_path (str): the folder where tmp files are stored (will be appended with a proper filename).
+        extraction_fps (int): target fps value
+
+    Returns:
+        str: The path where the tmp file is stored. To be used to load the video from
+    '''
+    assert which_ffmpeg() != '', 'Is ffmpeg installed? Check if the conda environment is activated.'
+    # assert video_path.endswith('.mp4'), 'The file does not end with .mp4. Comment this if expected'
+    # create tmp dir if doesn't exist
+    os.makedirs(tmp_path, exist_ok=True)
+
+    # form the path to tmp directory
+    new_path = os.path.join(tmp_path, f'{Path(video_path).stem}_new_fps.mp4')
+    cmd = f'{which_ffmpeg()} -hide_banner -loglevel panic '
+    cmd += f'-y -i {video_path} -filter:v fps=fps={extraction_fps} {new_path}'
+    subprocess.call(cmd.split())
+
+    return new_path
+
+def maybe_download_model(model_name: str, log_dir: str) -> str:
+    name2info = {
+        '2021-06-20T16-35-20_vggsound_transformer': {
+            'info': 'No Feats',
+            'hash': 'b1f9bb63d831611479249031a1203371',
+            'link': 'https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a'
+                    '/specvqgan_public/models/2021-06-20T16-35-20_vggsound_transformer.tar.gz',
+        },
+        '2021-07-30T21-03-22_vggsound_transformer': {
+            'info': '1 ResNet50 Feature',
+            'hash': '27a61d4b74a72578d13579333ed056f6',
+            'link': 'https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a'
+                    '/specvqgan_public/models/2021-07-30T21-03-22_vggsound_transformer.tar.gz',
+        },
+        '2021-07-30T21-34-25_vggsound_transformer': {
+            'info': '5 ResNet50 Features',
+            'hash': 'f4d7105811589d441b69f00d7d0b8dc8',
+            'link': 'https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a'
+                    '/specvqgan_public/models/2021-07-30T21-34-25_vggsound_transformer.tar.gz',
+        },
+        '2021-07-30T21-34-41_vggsound_transformer': {
+            'info': '212 ResNet50 Features',
+            'hash': 'b222cc0e7aeb419f533d5806a08669fe',
+            'link': 'https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a'
+                    '/specvqgan_public/models/2021-07-30T21-34-41_vggsound_transformer.tar.gz',
+        },
+        '2021-06-03T00-43-28_vggsound_transformer': {
+            'info': 'Class Label',
+            'hash': '98a3788ab973f1c3cc02e2e41ad253bc',
+            'link': 'https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a'
+                    '/specvqgan_public/models/2021-06-03T00-43-28_vggsound_transformer.tar.gz',
+        },
+    }
+    print(f'Using: {model_name} ({name2info[model_name]["info"]})')
+    model_dir = os.path.join(log_dir, model_name)
+    if not os.path.exists(model_dir):
+        tar_local_path = os.path.join(log_dir, f'{model_name}.tar.gz')
+        # check if tar already exists and its md5sum
+        if not os.path.exists(tar_local_path) or md5_hash(tar_local_path) != name2info[model_name]['hash']:
+            down_link = name2info[model_name]['link']
+            download(down_link, tar_local_path)
+            print('Unpacking', tar_local_path, 'to', log_dir)
+            shutil.unpack_archive(tar_local_path, log_dir)
+            # clean-up space as we already have unpacked folder
+            os.remove(tar_local_path)
+    return model_dir
+
+def load_config(model_dir: str):
+    # Load the config
+    config_main = sorted(glob(os.path.join(model_dir, 'configs/*-project.yaml')))[-1]
+    config_pylt = sorted(glob(os.path.join(model_dir, 'configs/*-lightning.yaml')))[-1]
+    config = OmegaConf.merge(
+        OmegaConf.load(config_main),
+        OmegaConf.load(config_pylt),
+    )
+    # patch config. E.g. if the model is trained on another machine with different paths
+    for a in ['spec_dir_path', 'rgb_feats_dir_path', 'flow_feats_dir_path']:
+        if config.data.params[a] is not None:
+            if 'vggsound.VGGSound' in config.data.params.train.target:
+                base_path = './data/vggsound/'
+            elif 'vas.VAS' in config.data.params.train.target:
+                base_path = './data/vas/features/*/'
+            else:
+                raise NotImplementedError
+            config.data.params[a] = os.path.join(base_path, Path(config.data.params[a]).name)
+    return config
+
+def load_model(model_name, log_dir, device):
+    model_dir = maybe_download_model(model_name, log_dir)
+    config = load_config(model_dir)
+
+    # Sampling model
+    ckpt = sorted(glob(os.path.join(model_dir, 'checkpoints/*.ckpt')))[-1]
+    pl_sd = torch.load(ckpt, map_location='cpu')
+    sampler = load_model_from_config(config.model, pl_sd['state_dict'])['model']
+    sampler.to(device)
+
+    # aux models (vocoder and melception)
+    ckpt_melgan = config.lightning.callbacks.image_logger.params.vocoder_cfg.params.ckpt_vocoder
+    melgan = load_vocoder(ckpt_melgan, eval_mode=True)['model'].to(device)
+    melception = load_feature_extractor(True if device.type == 'cuda' else False, eval_mode=True)
+    return config, sampler, melgan, melception
+
+
+class LeftmostCropOrTile(object):
+    def __init__(self, crop_or_tile_to):
+        self.crop_or_tile_to = crop_or_tile_to
+
+    def __call__(self, item: Dict):
+        # tile or crop features to the `crop_or_tile_to`
+        T, D = item['feature'].shape
+        if T != self.crop_or_tile_to:
+            how_many_tiles_needed = 1 + (self.crop_or_tile_to // T)
+            item['feature'] = np.tile(item['feature'], (how_many_tiles_needed, 1))[:self.crop_or_tile_to, :]
+        return item
+
+class ExtractResNet50(torch.nn.Module):
+
+    def __init__(self, extraction_fps, feat_cfg, device, batch_size=32, tmp_dir='./tmp'):
+        super(ExtractResNet50, self).__init__()
+        self.tmp_path = tmp_dir
+        self.extraction_fps = extraction_fps
+        self.batch_size = batch_size
+        self.feat_cfg = feat_cfg
+
+        self.means = [0.485, 0.456, 0.406]
+        self.stds = [0.229, 0.224, 0.225]
+        self.transforms = transforms.Compose([
+            transforms.ToPILImage(),
+            transforms.Resize(256),
+            transforms.CenterCrop(224),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=self.means, std=self.stds)
+        ])
+        random_crop = False
+        self.post_transforms = transforms.Compose([
+            LeftmostCropOrTile(feat_cfg.feat_len),
+            CropFeats([feat_cfg.feat_crop_len, feat_cfg.feat_depth], random_crop),
+            (lambda x: x) if feat_cfg.feat_sampler_cfg is None else instantiate_from_config(feat_cfg.feat_sampler_cfg),
+        ])
+        self.device = device
+        self.model = models.resnet50(pretrained=True).to(device)
+        self.model.eval()
+        # save the pre-trained classifier for show_preds and replace it in the net with identity
+        self.model_class = self.model.fc
+        self.model.fc = torch.nn.Identity()
+
+    @torch.no_grad()
+    def forward(self, video_path: str) -> Dict[str, np.ndarray]:
+
+        if self.feat_cfg.replace_feats_with_random:
+            T, D = self.feat_cfg.feat_sampler_cfg.params.feat_sample_size, self.feat_cfg.feat_depth
+            print(f'Since we are in "No Feats" setting, returning a random feature: [{T}, {D}]')
+            random_features = {'feature': torch.rand(T, D)}
+            return random_features, []
+
+        # take the video, change fps and save to the tmp folder
+        if self.extraction_fps is not None:
+            video_path = reencode_video_with_diff_fps(video_path, self.tmp_path, self.extraction_fps)
+
+        # read a video
+        cap = cv2.VideoCapture(video_path)
+        batch_list = []
+        vid_feats = []
+        cached_frames = []
+        transforms_for_show = transforms.Compose(self.transforms.transforms[:4])
+        # sometimes when the target fps is 1 or 2, the first frame of the reencoded video is missing
+        # and cap.read returns None but the rest of the frames are ok. timestep is 0.0 for the 2nd frame in
+        # this case
+        first_frame = True
+
+        # iterating through the opened video frame-by-frame and occationally run the model once a batch is
+        # formed
+        while cap.isOpened():
+            frame_exists, rgb = cap.read()
+
+            if first_frame and not frame_exists:
+                continue
+            first_frame = False
+
+            if frame_exists:
+                # prepare data and cache if needed
+                rgb = cv2.cvtColor(rgb, cv2.COLOR_BGR2RGB)
+                cached_frames.append(transforms_for_show(rgb))
+                rgb = self.transforms(rgb).unsqueeze(0).to(self.device)
+                batch_list.append(rgb)
+                # when batch is formed to inference
+                if len(batch_list) == self.batch_size:
+                    batch_feats = self.model(torch.cat(batch_list))
+                    vid_feats.extend(batch_feats.tolist())
+                    # clean up the batch list
+                    batch_list = []
+            else:
+                # if the last batch was smaller than the batch size, we still need to process those frames
+                if len(batch_list) != 0:
+                    batch_feats = self.model(torch.cat(batch_list))
+                    vid_feats.extend(batch_feats.tolist())
+                cap.release()
+                break
+
+        vid_feats = np.array(vid_feats)
+        features = {'feature': vid_feats}
+        print('Raw Extracted Representation:', features['feature'].shape)
+
+        if self.post_transforms is not None:
+            features = self.post_transforms(features)
+            # using 'feature' as the key to reuse the feature resampling transform
+            cached_frames = self.post_transforms.transforms[-1]({'feature': torch.stack(cached_frames)})['feature']
+
+        print('Post-processed Representation:', features['feature'].shape)
+
+        return features, cached_frames
+
+def extract_melspectrogram(in_path: str, sr: int, duration: int = 10, tmp_path: str = './tmp') -> np.ndarray:
+    '''Extract Melspectrogram similar to RegNet.'''
+    assert which_ffmpeg() != '', 'Is ffmpeg installed? Check if the conda environment is activated.'
+    # assert in_path.endswith('.mp4'), 'The file does not end with .mp4. Comment this if expected'
+    # create tmp dir if doesn't exist
+    os.makedirs(tmp_path, exist_ok=True)
+
+    # Extract audio from a video if needed
+    if in_path.endswith('.wav'):
+        audio_raw = in_path
+    else:
+        audio_raw = os.path.join(tmp_path, f'{Path(in_path).stem}.wav')
+        cmd = f'{which_ffmpeg()} -i {in_path} -hide_banner -loglevel panic -f wav -vn -y {audio_raw}'
+        subprocess.call(cmd.split())
+
+    # Extract audio from a video
+    audio_new = os.path.join(tmp_path, f'{Path(in_path).stem}_{sr}hz.wav')
+    cmd = f'{which_ffmpeg()} -i {audio_raw} -hide_banner -loglevel panic -ac 1 -ab 16k -ar {sr} -y {audio_new}'
+    subprocess.call(cmd.split())
+
+    length = int(duration * sr)
+    audio_zero_pad, spec = get_spectrogram(audio_new, save_dir=None, length=length, save_results=False)
+
+    # specvqgan expects inputs to be in [-1, 1] but spectrograms are in [0, 1]
+    spec = 2 * spec - 1
+
+    return spec
+
+
+def show_grid(imgs):
+    print('Rendering the Plot with Frames Used in Conditioning')
+    figsize = ((imgs.shape[1] // 228 + 1) * 5, (imgs.shape[2] // 228 + 1) * 5)
+    if not isinstance(imgs, list):
+        imgs = [imgs]
+    fig, axs = plt.subplots(ncols=len(imgs), squeeze=False, figsize=figsize)
+    for i, img in enumerate(imgs):
+        img = img.detach()
+        img = F.to_pil_image(img)
+        axs[0, i].imshow(np.asarray(img))
+        axs[0, i].set(xticklabels=[], yticklabels=[], xticks=[], yticks=[])
+    return fig
+
+
+if __name__ == '__main__':
+    # if empty, it wasn't found
+    print(which_ffmpeg())
diff --git a/feature_extraction/extract_audio_and_video.py b/feature_extraction/extract_audio_and_video.py
new file mode 100644
index 0000000..d4d1368
--- /dev/null
+++ b/feature_extraction/extract_audio_and_video.py
@@ -0,0 +1,86 @@
+import argparse
+import os
+import os.path as P
+from functools import partial
+from glob import glob
+from multiprocessing import Pool
+
+
+def execCmd(cmd):
+    r = os.popen(cmd)
+    text = r.read()
+    r.close()
+    return text
+
+def pipline(video_path, output_dir, fps, sr, duration_target):
+    video_name = os.path.basename(video_path)
+    audio_name = video_name.replace(".mp4", ".wav")
+
+    # Extract Original Audio
+    ori_audio_dir = P.join(output_dir, "audio_ori")
+    os.makedirs(ori_audio_dir, exist_ok=True)
+    os.system(f"ffmpeg -i {video_path} -loglevel error -f wav -vn -y {P.join(ori_audio_dir, audio_name)}")
+
+    # Cut Video According to Audio
+    align_video_dir = P.join(output_dir, "videos_algin")
+    os.makedirs(align_video_dir, exist_ok=True)
+    duration = execCmd(f"ffmpeg -i {P.join(ori_audio_dir, audio_name)}  2>&1 | grep 'Duration' | cut -d ' ' -f 4 | sed s/,//")
+    duration = duration.replace('\n', "")
+    os.system("ffmpeg -ss 0 -t {} -i {} -loglevel error -c:v libx264 -c:a aac -strict experimental -b:a 98k -y {}".format(
+            duration, video_path, P.join(align_video_dir, video_name)))
+
+    # Repeat Video
+    repeat_video_dir = P.join(output_dir, "videos_repeat")
+    os.makedirs(repeat_video_dir, exist_ok=True)
+    hour, min, sec = [float(_) for _ in duration.split(":")]
+    duration_second = 3600*hour + 60*min + sec
+    n_repeat = duration_target//duration_second + 1
+    os.system("ffmpeg -stream_loop {} -i {} -loglevel error -c copy -fflags +genpts -y {}".format(n_repeat,
+            P.join(align_video_dir, video_name), P.join(repeat_video_dir, video_name)))
+
+    # Cut Video
+    cut_video_dir = P.join(output_dir, f"videos_{duration_target}s")
+    os.makedirs(cut_video_dir, exist_ok=True)
+    os.system("ffmpeg -ss 0 -t {} -i {} -loglevel error -c:v libx264 -c:a aac -strict experimental -b:a 98k -y {}".format(duration_target,
+            P.join(repeat_video_dir, video_name), P.join(cut_video_dir, video_name)))
+
+    # Extract Audio
+    cut_audio_dir = P.join(output_dir, f"audio_{duration_target}s")
+    os.makedirs(cut_audio_dir, exist_ok=True)
+    os.system("ffmpeg -i {} -loglevel error -f wav -vn -y {}".format(
+            P.join(cut_video_dir, video_name), P.join(cut_audio_dir, audio_name)))
+
+    # change audio sample rate
+    sr_audio_dir = P.join(output_dir, f"audio_{duration_target}s_{sr}hz")
+    os.makedirs(sr_audio_dir, exist_ok=True)
+    os.system("ffmpeg -i {} -loglevel error -ac 1 -ab 16k -ar {} -y {}".format(
+            P.join(cut_audio_dir, audio_name), sr, P.join(sr_audio_dir, audio_name)))
+
+    # change video fps
+    fps_audio_dir = P.join(output_dir, f"videos_{duration_target}s_{fps}fps")
+    os.makedirs(fps_audio_dir, exist_ok=True)
+    os.system("ffmpeg -y -i {} -loglevel error -r {} -c:v libx264 -strict -2 {}".format(
+              P.join(cut_video_dir, video_name), fps, P.join(fps_audio_dir, video_name)))
+
+
+if __name__ == '__main__':
+    paser = argparse.ArgumentParser()
+    paser.add_argument("-i", "--input_dir", default="data/VAS/dog/videos")
+    paser.add_argument("-o", "--output_dir", default="data/features/dog")
+    paser.add_argument("-d", "--duration", type=int, default=10)
+    paser.add_argument("-a", '--audio_sample_rate', default='22050')
+    paser.add_argument("-v", '--video_fps', default='21.5')
+    paser.add_argument("-n", '--num_worker', type=int, default=32)
+    args = paser.parse_args()
+    input_dir = args.input_dir
+    output_dir = args.output_dir
+    duration_target = args.duration
+    sr = args.audio_sample_rate
+    fps = args.video_fps
+
+    video_paths = glob(P.join(input_dir, "*.mp4"))
+    video_paths.sort()
+
+    with Pool(args.num_worker) as p:
+        p.map(partial(pipline, output_dir=output_dir,
+        sr=sr, fps=fps, duration_target=duration_target), video_paths)
diff --git a/feature_extraction/extract_mel_spectrogram.py b/feature_extraction/extract_mel_spectrogram.py
new file mode 100644
index 0000000..0d64c08
--- /dev/null
+++ b/feature_extraction/extract_mel_spectrogram.py
@@ -0,0 +1,207 @@
+import argparse
+import os
+import os.path as P
+from copy import deepcopy
+from functools import partial
+from glob import glob
+from multiprocessing import Pool
+from pathlib import Path
+
+import librosa
+import numpy as np
+import torchvision
+
+
+class MelSpectrogram(object):
+    def __init__(self, sr, nfft, fmin, fmax, nmels, hoplen, spec_power, inverse=False):
+        self.sr = sr
+        self.nfft = nfft
+        self.fmin = fmin
+        self.fmax = fmax
+        self.nmels = nmels
+        self.hoplen = hoplen
+        self.spec_power = spec_power
+        self.inverse = inverse
+
+        self.mel_basis = librosa.filters.mel(sr=sr, n_fft=nfft, fmin=fmin, fmax=fmax, n_mels=nmels)
+
+    def __call__(self, x):
+        if self.inverse:
+            spec = librosa.feature.inverse.mel_to_stft(
+                x, sr=self.sr, n_fft=self.nfft, fmin=self.fmin, fmax=self.fmax, power=self.spec_power
+            )
+            wav = librosa.griffinlim(spec, hop_length=self.hoplen)
+            return wav
+        else:
+            spec = np.abs(librosa.stft(x, n_fft=self.nfft, hop_length=self.hoplen)) ** self.spec_power
+            mel_spec = np.dot(self.mel_basis, spec)
+            return mel_spec
+
+class LowerThresh(object):
+    def __init__(self, min_val, inverse=False):
+        self.min_val = min_val
+        self.inverse = inverse
+
+    def __call__(self, x):
+        if self.inverse:
+            return x
+        else:
+            return np.maximum(self.min_val, x)
+
+class Add(object):
+    def __init__(self, val, inverse=False):
+        self.inverse = inverse
+        self.val = val
+
+    def __call__(self, x):
+        if self.inverse:
+            return x - self.val
+        else:
+            return x + self.val
+
+class Subtract(Add):
+    def __init__(self, val, inverse=False):
+        self.inverse = inverse
+        self.val = val
+
+    def __call__(self, x):
+        if self.inverse:
+            return x + self.val
+        else:
+            return x - self.val
+
+class Multiply(object):
+    def __init__(self, val, inverse=False) -> None:
+        self.val = val
+        self.inverse = inverse
+
+    def __call__(self, x):
+        if self.inverse:
+            return x / self.val
+        else:
+            return x * self.val
+
+class Divide(Multiply):
+    def __init__(self, val, inverse=False):
+        self.inverse = inverse
+        self.val = val
+
+    def __call__(self, x):
+        if self.inverse:
+            return x * self.val
+        else:
+            return x / self.val
+
+
+class Log10(object):
+    def __init__(self, inverse=False):
+        self.inverse = inverse
+
+    def __call__(self, x):
+        if self.inverse:
+            return 10 ** x
+        else:
+            return np.log10(x)
+
+class Clip(object):
+    def __init__(self, min_val, max_val, inverse=False):
+        self.min_val = min_val
+        self.max_val = max_val
+        self.inverse = inverse
+
+    def __call__(self, x):
+        if self.inverse:
+            return x
+        else:
+            return np.clip(x, self.min_val, self.max_val)
+
+class TrimSpec(object):
+    def __init__(self, max_len, inverse=False):
+        self.max_len = max_len
+        self.inverse = inverse
+
+    def __call__(self, x):
+        if self.inverse:
+            return x
+        else:
+            return x[:, :self.max_len]
+
+class MaxNorm(object):
+    def __init__(self, inverse=False):
+        self.inverse = inverse
+        self.eps = 1e-10
+
+    def __call__(self, x):
+        if self.inverse:
+            return x
+        else:
+            return x / (x.max() + self.eps)
+
+
+TRANSFORMS = torchvision.transforms.Compose([
+    MelSpectrogram(sr=22050, nfft=1024, fmin=125, fmax=7600, nmels=80, hoplen=1024//4, spec_power=1),
+    LowerThresh(1e-5),
+    Log10(),
+    Multiply(20),
+    Subtract(20),
+    Add(100),
+    Divide(100),
+    Clip(0, 1.0),
+    TrimSpec(860)
+])
+
+
+def inv_transforms(x, folder_name='melspec_10s_22050hz'):
+    '''relies on the GLOBAL contant TRANSFORMS which should be defined in this document'''
+    if folder_name == 'melspec_10s_22050hz':
+        i_transforms = deepcopy(TRANSFORMS.transforms[::-1])
+    else:
+        raise NotImplementedError
+    for t in i_transforms:
+        t.inverse = True
+    i_transforms = torchvision.transforms.Compose(i_transforms)
+    return i_transforms(x)
+
+
+def get_spectrogram(audio_path, save_dir, length, folder_name='melspec_10s_22050hz', save_results=True):
+    wav, _ = librosa.load(audio_path, sr=None)
+    # this cannot be a transform without creating a huge overhead with inserting audio_name in each
+    y = np.zeros(length)
+    if wav.shape[0] < length:
+        y[:len(wav)] = wav
+    else:
+        y = wav[:length]
+
+    if folder_name == 'melspec_10s_22050hz':
+        print('using', folder_name)
+        mel_spec = TRANSFORMS(y)
+    else:
+        raise NotImplementedError
+
+    if save_results:
+        os.makedirs(save_dir, exist_ok=True)
+        audio_name = os.path.basename(audio_path).split('.')[0]
+        np.save(P.join(save_dir, audio_name + '_mel.npy'), mel_spec)
+        np.save(P.join(save_dir, audio_name + '_audio.npy'), y)
+    else:
+        return y, mel_spec
+
+
+if __name__ == '__main__':
+    paser = argparse.ArgumentParser()
+    paser.add_argument("-i", "--input_dir", default="data/features/dog/audio_10s_22050hz")
+    paser.add_argument("-o", "--output_dir", default="data/features/dog/melspec_10s_22050hz")
+    paser.add_argument("-l", "--length", default=220500)
+    paser.add_argument("-n", '--num_worker', type=int, default=32)
+    args = paser.parse_args()
+    input_dir = args.input_dir
+    output_dir = args.output_dir
+    length = args.length
+
+    audio_paths = glob(P.join(input_dir, "*.wav"))
+    audio_paths.sort()
+
+    with Pool(args.num_worker) as p:
+        p.map(partial(
+            get_spectrogram, save_dir=output_dir, length=length, folder_name=Path(output_dir).name
+        ), audio_paths)
diff --git a/sample_visualization.py b/sample_visualization.py
new file mode 100644
index 0000000..fd08b10
--- /dev/null
+++ b/sample_visualization.py
@@ -0,0 +1,920 @@
+import argparse
+import csv
+import glob
+import os
+import sys
+import time
+from datetime import datetime
+from pathlib import Path
+
+import streamlit as st
+import torch
+import torchvision
+import yaml
+from omegaconf import OmegaConf
+
+from specvqgan.util import get_ckpt_path
+
+sys.path.insert(0, '.')  # nopep8
+import matplotlib.pyplot as plt
+import soundfile
+from torch.utils.data.dataloader import default_collate
+
+from feature_extraction.extract_mel_spectrogram import inv_transforms
+from train import instantiate_from_config
+from vocoder.modules import Generator
+
+
+def get_parser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "-r",
+        "--resume",
+        type=str,
+        nargs="?",
+        help="load from logdir or checkpoint in logdir",
+    )
+    parser.add_argument(
+        "-b",
+        "--base",
+        nargs="*",
+        metavar="base_config.yaml",
+        help="paths to base configs. Loaded from left-to-right. "
+        "Parameters can be overwritten or added with command-line options of the form `--key value`.",
+        default=list(),
+    )
+    parser.add_argument(
+        "-c",
+        "--config",
+        nargs="?",
+        metavar="single_config.yaml",
+        help="path to single config. If specified, base configs will be ignored "
+        "(except for the last one if left unspecified).",
+        const=True,
+        default="",
+    )
+    parser.add_argument(
+        "--ignore_base_data",
+        action="store_true",
+        help="Ignore data specification from base configs. Useful if you want "
+        "to specify a custom datasets on the command line.",
+    )
+    parser.add_argument(
+        '--vocoder_path',
+        default='./vocoder/logs/vggsound/',
+        help='The path to the folder with pre-trained Vocoder (a folder from ./vocoder/logs)'
+    )
+    parser.add_argument(
+        '--logdir',
+        default='./logs/',
+        help='Path to the log dir with pre-trained GPT'
+    )
+    return parser
+
+def rename_models(x):
+    x = x[x.index('T')+1:]
+    name2type = {
+        '00-43-28_vggsound_transformer': 'VGGSound – Class – VGGSound Codebook',
+        '14-41-19_vas_transformer': 'VAS – Class – VGGSound Codebook',
+        '09-42-07_vas_transformer': 'VAS – Class – VAS Codebook',
+        '16-35-20_vggsound_transformer': 'VGGSound – No Feats – VGGSound Codebook',
+        '11-18-51_vggsound_transformer': 'VGGSound – 1 Feat BN – VGGSound Codebook',
+        '09-34-10_vggsound_transformer': 'VGGSound – 5 Feats BN – VGGSound Codebook',
+        '07-27-58_vggsound_transformer': 'VGGSound – 212 Feats BN – VGGSound Codebook',
+        '16-34-36_vas_transformer': 'VAS – No Feats – VGGSound Codebook',
+        '06-32-51_vas_transformer': 'VAS – 1 Feat BN – VGGSound Codebook',
+        '05-51-34_vas_transformer': 'VAS – 5 Feats BN – VGGSound Codebook',
+        '05-38-40_vas_transformer': 'VAS – 212 Feats BN – VGGSound Codebook',
+        '16-24-38_vas_transformer': 'VAS – No Feats – VAS Codebook',
+        '13-31-37_vas_transformer': 'VAS – 1 Feats BN – VAS Codebook',
+        '14-14-24_vas_transformer': 'VAS – 5 Feats BN – VAS Codebook',
+        '15-17-18_vas_transformer': 'VAS – 212 Feats BN – VAS Codebook',
+        '11-47-40_vas_transformer': 'VAS – 1 Feat RN50 – VGGSound Codebook',
+        '11-36-00_vas_transformer': 'VAS – 5 Feats RN50 – VGGSound Codebook',
+        '11-52-28_vas_transformer': 'VAS – 212 Feats RN50 – VGGSound Codebook',
+        '14-59-49_vas_transformer': 'VAS – 1 Feat RN50 – VAS Codebook',
+        '14-51-25_vas_transformer': 'VAS – 5 Feats RN50 – VAS Codebook',
+        '13-34-39_vas_transformer': 'VAS – 212 Feats RN50 – VAS Codebook',
+        '21-03-22_vggsound_transformer': 'VGGSound – 1 Feat RN50 – VGGSound Codebook',
+        '21-34-25_vggsound_transformer': 'VGGSound – 5 Feats RN50 – VGGSound Codebook',
+        '21-34-41_vggsound_transformer': 'VGGSound – 212 Feats RN50 – VGGSound Codebook',
+    }
+    if x in name2type:
+        x = f'{name2type[x]} ({x})'
+    return x
+
+def load_model_from_config(config, sd, gpu=True, eval_mode=True):
+    if "ckpt_path" in config.params:
+        st.warning("Deleting the restore-ckpt path from the config...")
+        config.params.ckpt_path = None
+    if "downsample_cond_size" in config.params:
+        st.warning("Deleting downsample-cond-size from the config and setting factor=0.5 instead...")
+        config.params.downsample_cond_size = -1
+        config.params["downsample_cond_factor"] = 0.5
+    try:
+        if "ckpt_path" in config.params.first_stage_config.params:
+            config.params.first_stage_config.params.ckpt_path = None
+            st.warning("Deleting the first-stage restore-ckpt path from the config...")
+        if "ckpt_path" in config.params.cond_stage_config.params:
+            config.params.cond_stage_config.params.ckpt_path = None
+            st.warning("Deleting the cond-stage restore-ckpt path from the config...")
+    except:
+        pass
+
+    model = instantiate_from_config(config)
+    if sd is not None:
+        missing, unexpected = model.load_state_dict(sd, strict=False)
+        st.warning(f"Missing Keys in State Dict: {missing}")
+        st.warning(f"Unexpected Keys in State Dict: {unexpected}")
+    if gpu:
+        model.cuda()
+    if eval_mode:
+        model.eval()
+    return {"model": model}
+
+def load_vocoder(ckpt_vocoder: str, eval_mode: bool):
+    ckpt_vocoder = Path(ckpt_vocoder)
+    vocoder_sd = torch.load(ckpt_vocoder / 'best_netG.pt', map_location='cpu')
+
+    with open(ckpt_vocoder / 'args.yml', 'r') as f:
+        args = yaml.load(f, Loader=yaml.UnsafeLoader)
+
+    vocoder = Generator(args.n_mel_channels, args.ngf, args.n_residual_layers)
+    vocoder.load_state_dict(vocoder_sd)
+
+    if eval_mode:
+        vocoder.eval()
+
+    return {'model': vocoder}
+
+def load_feature_extractor(gpu, eval_mode=True):
+    s = '''
+    feature_extractor:
+        target: evaluation.feature_extractors.melception.Melception
+        params:
+            num_classes: 309
+            features_list: ['logits']
+            feature_extractor_weights_path: ./evaluation/logs/21-05-10T09-28-40/melception-21-05-10T09-28-40.pt
+    transform_dset_out_to_inception_in:
+    - target: evaluation.datasets.transforms.FromMinusOneOneToZeroOne
+    - target: specvqgan.modules.losses.vggishish.transforms.StandardNormalizeAudio
+      params:
+        specs_dir: ./data/vggsound/melspec_10s_22050hz
+        cache_path: ./specvqgan/modules/losses/vggishish/data/
+    - target: evaluation.datasets.transforms.GetInputFromBatchByKey
+      params:
+        input_key: image
+    - target: evaluation.datasets.transforms.ToFloat32'''
+    feat_extractor_cfg = OmegaConf.create(s)
+    # downloading the checkpoint for melception
+    get_ckpt_path('melception', 'evaluation/logs/21-05-10T09-28-40')
+    pl_sd = torch.load(feat_extractor_cfg.feature_extractor.params.feature_extractor_weights_path,
+                       map_location="cpu")
+
+    # use gpu=False to compute it on CPU
+    feat_extractor = load_model_from_config(
+        feat_extractor_cfg.feature_extractor, pl_sd['model'], gpu=gpu, eval_mode=eval_mode)['model']
+
+    if feat_extractor_cfg.transform_dset_out_to_inception_in is not None:
+        transforms = [instantiate_from_config(c) for c in feat_extractor_cfg.transform_dset_out_to_inception_in]
+    else:
+        transforms = [lambda x: x]
+    transforms = torchvision.transforms.Compose(transforms)
+
+    vggsound_meta = list(csv.reader(open('./data/vggsound.csv'), quotechar='"'))
+    unique_classes = sorted(list(set(row[2] for row in vggsound_meta)))
+    label2target = {label: target for target, label in enumerate(unique_classes)}
+    target2label = {target: label for label, target in label2target.items()}
+    return {'model': feat_extractor, 'transforms': transforms, 'target2label': target2label}
+
+@st.cache(allow_output_mutation=True, suppress_st_warning=True)
+def load_model_and_dataset(config, ckpt, ckpt_vocoder, gpu=True, eval_mode=True):
+    # get data
+    dsets = instantiate_from_config(config.data)
+    dsets.prepare_data()
+    dsets.setup()
+
+    # now load the specified checkpoint
+    if ckpt:
+        pl_sd = torch.load(ckpt, map_location="cpu")
+        global_step = pl_sd["global_step"]
+    else:
+        pl_sd = {"state_dict": None}
+        global_step = None
+
+    # loading the vocoder
+    if ckpt_vocoder:
+        vocoder = load_vocoder(ckpt_vocoder, eval_mode)['model']
+        vocoder = vocoder.to('cuda') if gpu else vocoder
+
+    model = load_model_from_config(config.model, pl_sd['state_dict'], gpu=gpu, eval_mode=eval_mode)['model']
+
+    # patch config for the adjusted input length which could be longer than during training (infinite samples)
+    # local_permuter = model.first_stage_permuter
+    # if config.model.params.first_stage_permuter_config.params.W is not None:
+    #     config.model.params.first_stage_permuter_config.params.W *= W_scale
+    #     model.first_stage_permuter = instantiate_from_config(config.model.params.first_stage_permuter_config).cuda().eval()
+    #     print(config.model.params.first_stage_permuter_config)
+
+    feat_extractor = load_feature_extractor(gpu, eval_mode)
+
+    return dsets, model, vocoder, global_step, feat_extractor
+
+def bchw_to_st(x, to_scale=True, flip_dims=None):
+    if flip_dims is not None:
+        # dims is a tuple. To flip only 2nd dim use: `flip_dims=(2,)`
+        x = x.flip(dims=flip_dims)
+    if to_scale:
+        # (-1, 1) -> (0, 1)
+        return (x.detach().cpu().numpy().transpose(0, 2, 3, 1) + 1.) / 2.
+    else:
+        return x.detach().cpu().numpy().transpose(0, 2, 3, 1)
+
+def tensor_to_plt(x, vmin=None, vmax=None, flip_dims=None):
+    if flip_dims is not None:
+        # dims is a tuple. To flip only 2nd dim use: `flip_dims=(2,)`
+        x = x.flip(dims=flip_dims)
+    # remove batch dim and make channel-last
+    if len(x.shape) > 3:
+        x = x.squeeze(0)
+    # if the figure is taller than it is wider rotate (transpose). Also clipping it as feats can be large
+    if x.shape[-1] < x.shape[-2]:
+        x = x.clip(-2, 2).transpose(-1, -2)
+    x = x.cpu()
+    if len(x.shape) == 3:
+        x = x.permute(1, 2, 0)
+    # fig, arr = plt.subplots(nrows=1, ncols=1)
+    # # arr[i].set_title(f'{vid_name}_{name}')
+    # arr.imshow(x)
+    # arr.set_frame_on(False)
+    fig = plt.figure(frameon=False)
+    ax = fig.add_axes([0, 0, 1, 1])
+    ax.axis('off')
+    # for facehq
+    # TODO: if x.shape[0] == 3:
+    #     x = x.flip(dims=(1,)).permute(1, 2, 0)
+    #     x = (x + 1) / 2
+    #     x = x.clip(0, 1)
+    ax.imshow(x, cmap=plt.get_cmap('gray'), vmin=vmin, vmax=vmax)
+    # ax.set_title('Some', fontsize=8)
+    return fig
+
+def save_results(spec_plt, waves_dict, topk_preds, logdir, batch, mode, sample_rate, specs_key_in_batch):
+    # implemented only for B=1, otherwise mind the batch[key][0]
+    label = ''.join(filter(lambda x: str.isalnum(x) or ' ', batch['label'][0])).replace(' ', '_')
+    target = int(batch['target'][0])
+    vid_id = Path(batch[specs_key_in_batch][0]).name.replace('_mel.npy', '')
+    time_stamp = datetime.now().strftime('%Y-%m-%dT%H-%M-%S')
+    save_dir = Path(logdir) / 'streamlit' / f'{target:03d}_{label}' / vid_id
+    os.makedirs(save_dir, exist_ok=True)
+    dpi = 300
+    for wave_type, wave in waves_dict.items():
+        soundfile.write(save_dir / f'{mode}_{time_stamp}_{wave_type}.wav', wave, sample_rate, 'PCM_24')
+        if len(wave) > sample_rate * 10:
+            dpi *= 10
+    spec_plt.savefig(save_dir / f'{mode}_{time_stamp}.png', bbox_inches='tight', pad_inches=0, dpi=dpi)
+    with open(save_dir / f'{mode}_{time_stamp}_topkpreds.txt', 'w') as out_f:
+        out_f.write(topk_preds)
+
+def show_wave_in_streamlit(wave_npy, sample_rate, caption):
+    # showing in streamlit. We cannot just show the npy wave and we need to save it first
+    temp_wav_file_path = 'todel.wav'
+    soundfile.write(temp_wav_file_path, wave_npy, sample_rate, 'PCM_24')
+    st.text(caption)
+    st.audio(temp_wav_file_path, format='audio/wav')
+    os.remove(temp_wav_file_path)
+
+def spec_to_audio_to_st(x, spec_dir_path, sample_rate, show_griffin_lim, vocoder=None, show_in_st=True):
+    # audios are in [-1, 1], making them in [0, 1]
+    spec = (x.data.squeeze(0) + 1) / 2
+
+    out = {}
+    if vocoder:
+        # (L,) <- wave: (1, 1, L).squeeze() <- spec: (1, F, T)
+        wave_from_vocoder = vocoder(spec).squeeze().cpu().numpy()
+        out['vocoder'] = wave_from_vocoder
+        if show_in_st:
+            show_wave_in_streamlit(wave_from_vocoder, sample_rate, 'Reconstructed Wave via MelGAN')
+
+    if show_griffin_lim:
+        spec = spec.squeeze(0).cpu().numpy()
+        wave_from_griffinlim = inv_transforms(spec, Path(spec_dir_path).stem)
+        out['inv_transforms'] = wave_from_griffinlim
+        if show_in_st:
+            show_wave_in_streamlit(wave_from_griffinlim, sample_rate, 'Reconstructed Wave via Griffin Lim')
+
+    return out
+
+def all_attention_to_st(attention, placeholders=None, scale_by_prior=None):
+    if scale_by_prior:
+        B, H, T, T = attention.shape
+        # attention weight is 1/T: if we have a seq with length 3 the weights are 1/3, 1/3, and 1/3
+        # making T by T matrix with zeros in the upper triangular part
+        attention_uniform_prior = 1 / torch.arange(1, T+1).view(1, T, 1).repeat(B, 1, T)
+        attention_uniform_prior = attention_uniform_prior.tril().view(B, 1, T, T).to(attention.device)
+        attention = attention - attention_uniform_prior
+
+    attention_agg = attention.sum(dim=1, keepdims=True)
+    att_st = tensor_to_plt(attention_agg)
+    # z_att_st = tensor_to_plt(z_att, flip_z_dims)
+    if placeholders is None:
+        return att_st
+    else:
+        placeholders['title_z_att'].text(f'Attention to All. {list(attention_agg.squeeze().shape)}')
+        placeholders['z_att'].write(att_st)
+        placeholders['title_c_att'].empty()
+        placeholders['c_att'].empty()
+
+def last_attention_to_st(attention, z_curr_step, c_length, z_permuter, c_permuter, quant_c_shape,
+                         quant_z_shape, placeholders=None, flip_c_dims=None, flip_z_dims=None):
+    B, H, T, T = attention.shape
+    # Since the attention ignores the last (target) element, we will visualize it as 0 – padding last 2 dims
+    # (B, H, T+1, T+1)
+    attention = torch.nn.functional.pad(attention, pad=(0, 1, 0, 1), value=0)
+    current_step = c_length + z_curr_step
+    attention_at_curr_step = attention[:, :, current_step-1, :]
+    # (B, H, c_length), (B, H, z_length) <-
+    c_att, z_att = attention_at_curr_step[:, :, :c_length], attention_at_curr_step[:, :, c_length:]
+    # aggregate through all heads H -> (B, c_length), (B, z_length)
+    c_att = c_att.sum(dim=1)  # * 10
+    z_att = z_att.sum(dim=1)  # * 10
+    # (B, length) -> (B, 1, *2d_or_1d_code_book_shape). *shpae[2:] will take 2 elems if 2d and 1 if 1d
+    c_att = c_permuter(c_att, reverse=True).reshape(B, 1, *quant_c_shape[2:])
+    z_att = z_permuter(z_att, reverse=True).reshape(B, 1, *quant_z_shape[2:])
+    # we don't need to flip 1d cond but we do need it for 2d input because of the spectrograms (upside-down)
+    # making value in two plots in the same range
+    # vmin = min(c_att.min(), z_att.min())
+    # vmax = max(c_att.max(), z_att.max())
+    vmin = None
+    vmax = None
+    c_att_st = tensor_to_plt(c_att, vmin, vmax, flip_c_dims)
+    z_att_st = tensor_to_plt(z_att, vmin, vmax, flip_z_dims)
+    c_att_weight = c_att.sum() / H
+    z_att_weight = z_att.sum() / H
+    if placeholders is None:
+        return c_att_st, z_att_st
+    else:
+        if len(c_att.squeeze().shape) > 0:
+            placeholders['title_c_att'].text(f'Attention to C. {list(c_att.squeeze().shape)}. Sum {c_att_weight:.2f}')
+            placeholders['c_att'].pyplot(c_att_st)
+        else:
+            placeholders['c_att'].empty()
+            placeholders['title_c_att'].text(f'Attention to C. Sum {c_att_weight:.2f}')
+        placeholders['title_z_att'].text(f'Attention to Z. {list(z_att.squeeze().shape)}. Sum {z_att_weight:.2f}')
+        placeholders['z_att'].write(z_att_st)
+
+def get_class_preditions(x, feat_extractor, k=10):
+    # use device=torch.device('cpu') to compute on cpu and save some memory
+    device = x.device
+    x = {'image': x.squeeze(0).cpu()}
+    x = feat_extractor['transforms'](x).to(device)
+    features = feat_extractor['model'](x)
+    featuresdict = feat_extractor['model'].convert_features_tuple_to_dict(features)
+    probs = featuresdict['logits'].softmax(dim=1)
+    topk_probs, topk_targets = probs.topk(k)
+    to_print = f'Spectrogram Classifier (K={k}):\n'
+    for p, y in zip(topk_probs.squeeze(0).cpu().tolist(), topk_targets.squeeze(0).cpu().tolist()):
+        to_print += f'\t{feat_extractor["target2label"][y]}: {p:.5f}\n'
+    return to_print
+
+
+def sample_conditionally(z_indices, sampling_shape, c_indices, quant_c, full_att_mat, scale_att_by_prior,
+                         temperature, top_x, update_every, placeholders,
+                         cond_stage_model_name, flip_z_dims, flip_c_dims, to_save_results, logdir, batch,
+                         specs_key_in_batch, vocoder, feat_sampler_cfg, show_griffin_lim, feat_extractor,
+                         mode):
+    start_t = time.time()
+
+    # for facehq
+    # patch_size_j = 16
+    # patch_size_i = 16
+    patch_size_i = 5
+    patch_size_j = 53
+
+    B, D, hr_h, hr_w = sampling_shape
+    # assert hr_w % patch_size_j == 0 and hr_w // patch_size_j == int(hr_w // patch_size_j)
+
+    if mode == 'full':
+        start_step = 0
+    else:
+        start_step = (patch_size_j // 2) * patch_size_i
+
+    z_pred_indices = torch.zeros((B, hr_h*hr_w)).long().to(z_indices.device)
+    z_pred_indices[:, :start_step] = z_indices[:, :start_step]
+
+    for step in range(start_step, hr_w * hr_h):
+        i = step % hr_h
+        j = step // hr_h
+
+        i_start = min(max(0, i - (patch_size_i // 2)), hr_h - patch_size_i)
+        j_start = min(max(0, j - (patch_size_j // 2)), hr_w - patch_size_j)
+        i_end = i_start + patch_size_i
+        j_end = j_start + patch_size_j
+
+        local_i = i - i_start
+        local_j = j - j_start
+
+        patch_2d_shape = (B, D, patch_size_i, patch_size_j)
+
+        placeholders['time'].text(f"Time: {time.time() - start_t:3.2f} seconds")
+        placeholders['info'].text(
+            f"Step: ({i},{j}) | Local: ({local_i},{local_j}) | Crop: ({i_start}:{i_end},{j_start}:{j_end})"
+        )
+
+        # TODO: faceshq – we don't need to permute the reshaped indices (1st and 2nd time)
+        # slicing the possibly permuted flat sequence:
+        # 1D z_pred_indices is permuted: A_flat = [1, 2, 3, 4, 5, 6, 7, 8, 9].
+        # the 2D input should be: A = [[1, 4, 7], [2, 5, 8], [3, 6, 9]].
+        # Therefore, after the first reshape it will be A.T = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
+        # the last reshape flattens is back
+        patch = z_pred_indices \
+            .reshape(B, hr_w, hr_h) \
+            .permute(0, 2, 1)[:, i_start:i_end, j_start:j_end].permute(0, 2, 1) \
+            .reshape(B, patch_size_i * patch_size_j)
+
+        # if cond_stage_model_name == 'CoordStage':
+        #     cpatch = c_indices \
+        #         .reshape(B, hr_w, hr_h) \
+        #         .permute(0, 2, 1)[:, i_start:i_end, j_start:j_end].permute(0, 2, 1) \
+        #         .reshape(B, patch_size_i * patch_size_j)
+        # elif cond_stage_model_name == 'VQModel1d':
+        #     cpatch = c_indices[:, j_start:j_end]
+        # elif cond_stage_model_name == 'FeatsClassStage':
+        #     features = quant_c['feature']
+        #     if feat_sampler_cfg is None:
+        #         time_step_coeff = features.shape[-1] / sampling_shape[-1]
+        #         assert time_step_coeff == int(time_step_coeff), f'{features.shape}, {sampling_shape}'
+        #         j_start_feats = int(j_start * time_step_coeff)
+        #         j_end_feats = int(j_end * time_step_coeff)
+        #     else:
+        #         feat_sample_size = feat_sampler_cfg.params.feat_sample_size
+        #         times_to_repeat_after_resample = feat_sampler_cfg.params.times_to_repeat_after_resample
+        #         if times_to_repeat_after_resample is not None:
+        #             feat_sample_size *= times_to_repeat_after_resample
+        #         patches_in_z = sampling_shape[-1] // patch_size_j
+        #         patches_in_c = features.shape[-1] // feat_sample_size
+        #         # assert patches_in_c == patches_in_z, f'{features.shape}, {sampling_shape}'
+        #         j_start_feats = j_start // patch_size_j
+        #         j_end_feats = j_start + feat_sample_size
+        #     cpatch = {
+        #         'target': quant_c['target'],
+        #         'feature': c_indices['feature'][:, :, j_start_feats:j_end_feats]
+        #     }
+        # elif cond_stage_model_name in ['RawFeatsStage', 'FeatClusterStage']:
+        #     if feat_sampler_cfg is None:
+        #         time_step_coeff = quant_c.shape[-1] / sampling_shape[-1]
+        #         assert time_step_coeff == int(time_step_coeff), f'{quant_c.shape}, {sampling_shape}'
+        #         j_start_feats = int(j_start * time_step_coeff)
+        #         j_end_feats = int(j_end * time_step_coeff)
+        #     else:
+        #         feat_sample_size = feat_sampler_cfg.params.feat_sample_size
+        #         times_to_repeat_after_resample = feat_sampler_cfg.params.times_to_repeat_after_resample
+        #         if times_to_repeat_after_resample is not None:
+        #             feat_sample_size *= times_to_repeat_after_resample
+        #         patches_in_z = sampling_shape[-1] // patch_size_j
+        #         patches_in_c = quant_c.shape[-1] // feat_sample_size
+        #         print(patches_in_c, patches_in_z)
+        #         # assert patches_in_c == patches_in_z, f'{quant_c.shape}, {sampling_shape}'
+        #         j_start_feats = j_start // patch_size_j
+        #         j_end_feats = j_start + feat_sample_size
+        #     if cond_stage_model_name == 'FeatClusterStage':
+        #         cpatch = c_indices[:, j_start_feats:j_end_feats]
+        #     else:
+        #         cpatch = c_indices[:, :, j_start_feats:j_end_feats]
+        # elif cond_stage_model_name == 'ClassOnlyStage':
+        #     cpatch = c_indices
+        # else:
+        #     raise NotImplementedError
+
+        # assuming we don't crop the conditioning and just use the whole c, if not desired uncomment the above
+        cpatch = c_indices
+
+        if cond_stage_model_name in ['RawFeatsStage', 'ClassOnlyStage', 'FeatsClassStage']:
+            logits, _, attention = model.transformer(patch[:, :-1], cpatch)
+        else:
+            patch = torch.cat((cpatch, patch), dim=1)
+            logits, _, attention = model.transformer(patch[:, :-1])
+        # remove conditioning
+        logits = logits[:, -patch_size_j*patch_size_i:, :]
+
+        local_pos_in_flat = local_j * patch_size_i + local_i
+        logits = logits[:, local_pos_in_flat, :]
+
+        logits = logits / temperature
+
+        if top_x is not None:
+            logits = model.top_k_logits(logits, top_x)
+        # apply softmax to convert to probabilities
+        probs = torch.nn.functional.softmax(logits, dim=-1)
+        # sample from the distribution
+        ix = torch.multinomial(probs, num_samples=1)
+        z_pred_indices[:, j * hr_h + i] = ix
+        # print(
+        #     z_pred_indices \
+        #     .reshape(B, hr_w, hr_h).permute(0, 2, 1)[:, i_start:i_end, j_start:j_end].permute(0, 2, 1)
+        # )
+        # print(z_pred_indices.reshape(B, hr_w, hr_h).permute(0, 2, 1).permute(0, 2, 1))
+
+        if step % update_every == 0:
+            z_pred_img = model.decode_to_img(z_pred_indices, sampling_shape)
+            placeholders['title_gen_spec'].text(f'Sampling {mode}. {list(z_pred_img.squeeze().shape)}')
+            # fliping the spectrogram just for illustration purposes (low freqs to bottom, high - top)
+            z_pred_img_st = tensor_to_plt(z_pred_img, flip_dims=flip_z_dims)
+            placeholders['gen_spec'].write(z_pred_img_st)
+
+            if full_att_mat:
+                all_attention_to_st(attention, placeholders, scale_att_by_prior)
+            else:
+                if cond_stage_model_name == 'FeatsClassStage':
+                    # 212 + 1
+                    c_length = cpatch['feature'].shape[-1] + cpatch['target'].shape[-1]
+                    quant_c_shape = [None, None, c_length]
+                else:
+                    c_length = cpatch.shape[-1]
+                    quant_c_shape = quant_c.shape
+                # quant_z_shape = sampling_shape
+                last_attention_to_st(attention, local_pos_in_flat, c_length, model.first_stage_permuter,
+                                     model.cond_stage_permuter, quant_c_shape, patch_2d_shape, placeholders,
+                                     flip_c_dims, flip_z_dims)
+
+    # quant_z_shape = sampling_shape
+    z_pred_img = model.decode_to_img(z_pred_indices, sampling_shape)
+
+    print(f'Time: {time.time() - start_t:3.2f} seconds')
+
+    # showing the final image
+    placeholders['title_gen_spec'].text(f'Sampling {mode}. {list(z_pred_img.squeeze().shape)}')
+    z_pred_img_st = tensor_to_plt(z_pred_img, flip_dims=flip_z_dims)
+    placeholders['gen_spec'].write(z_pred_img_st)
+
+    if full_att_mat:
+        all_attention_to_st(attention, placeholders, scale_att_by_prior)
+    else:
+        if cond_stage_model_name == 'FeatsClassStage':
+            # 212 + 1
+            c_length = cpatch['feature'].shape[-1] + cpatch['target'].shape[-1]
+            quant_c_shape = [None, None, c_length]
+        else:
+            c_length = cpatch.shape[-1]
+            quant_c_shape = quant_c.shape
+
+        last_attention_to_st(attention, local_pos_in_flat, c_length, model.first_stage_permuter,
+                             model.cond_stage_permuter, quant_c_shape, patch_2d_shape, placeholders,
+                             flip_c_dims, flip_z_dims)
+
+    topk_preds = get_class_preditions(z_pred_img, feat_extractor)
+    st.text(topk_preds)
+
+    waves = spec_to_audio_to_st(z_pred_img, config.data.params.spec_dir_path,
+                                config.data.params.sample_rate, show_griffin_lim, vocoder)
+
+    if to_save_results:
+        save_results(z_pred_img_st, waves, topk_preds, logdir, batch, mode, config.data.params.sample_rate,
+                     specs_key_in_batch)
+
+    st.info('Done')
+
+
+if __name__ == "__main__":
+    st.sidebar.info('''
+    Hi there 👋
+
+    This is a demo for **Visually Guided Sound Generation** project 🖼️ 👉 🔉.
+
+    [Project Page](https://v-iashin.github.io/specvqgan) • [Code](https://github.com/v-iashin/SpecVQGAN)
+    ''')
+
+    sys.path.append(os.getcwd())
+
+    parser = get_parser()
+
+    opt, unknown = parser.parse_known_args()
+
+    avail_models = Path(opt.logdir).rglob('*/checkpoints')
+    # 'T' is an empty model which prevents loading the first model by default
+    avail_models = ['T'] + sorted([str(p.parent) for p in avail_models])
+    # filtering out codebook models as we need only samplers
+    avail_models = [m for m in avail_models if 'codebook' not in m]
+    assert len(avail_models) > 0, f'There is no model in {opt.logdir}'
+    st.sidebar.header('Select a Model')
+    model_ckpt = st.sidebar.selectbox('', avail_models, 0, format_func=rename_models)
+    if model_ckpt == 'T':
+        st.stop()
+
+    opt.resume = model_ckpt
+
+    ckpt_vocoder = opt.vocoder_path
+    ckpt = None
+    if opt.resume:
+        if not os.path.exists(opt.resume):
+            raise ValueError("Cannot find {}".format(opt.resume))
+        if os.path.isfile(opt.resume):
+            paths = opt.resume.split("/")
+            try:
+                template_idx = len(paths)-paths[::-1].index("logs")+1
+            except ValueError:
+                idx = -2  # take a guess: path/to/logdir/checkpoints/model.ckpt
+            logdir = "/".join(paths[:idx])
+            ckpt = opt.resume
+        else:
+            assert os.path.isdir(opt.resume), opt.resume
+            logdir = opt.resume.rstrip("/")
+            ckpt_dir = os.path.join(logdir, "checkpoints")
+            ckpt_file = sorted(os.listdir(ckpt_dir))
+            if len(ckpt_file) > 1:
+                print(f'Warning: Found more than one checkpoint in {ckpt_dir}: {ckpt_file}')
+            ckpt_file = ckpt_file[0]
+            print(f'Using {ckpt_file}')
+            ckpt = os.path.join(logdir, 'checkpoints', ckpt_file)
+        print(f"logdir:{logdir}")
+        base_configs = sorted(glob.glob(os.path.join(logdir, "configs/*-project.yaml")))
+        opt.base = base_configs+opt.base
+
+    if opt.config:
+        if type(opt.config) == str:
+            opt.base = [opt.config]
+        else:
+            opt.base = [opt.base[-1]]
+
+    configs = [OmegaConf.load(cfg) for cfg in opt.base]
+    cli = OmegaConf.from_dotlist(unknown)
+    if opt.ignore_base_data:
+        for config in configs:
+            if hasattr(config, "data"):
+                del config["data"]
+    config = OmegaConf.merge(*configs, cli)
+
+    # determine the data folder
+    if 'vggsound.VGGSound' in config.data.params.train.target:
+        datapath = './data/vggsound/'
+        raw_vids_dir = os.path.join(datapath, 'video')
+    elif 'vas.VAS' in config.data.params.train.target:
+        datapath = './data/vas/'
+        raw_vids_dir = os.path.join(datapath, 'videos', '*')
+    else:
+        raise NotImplementedError
+
+    # patch config. E.g. if the model is trained on another machine with different paths
+    for a in ['spec_dir_path', 'rgb_feats_dir_path', 'flow_feats_dir_path']:
+        if config.data.params[a] is not None:
+            if 'vggsound.VGGSound' in config.data.params.train.target:
+                config.data.params[a] = os.path.join(datapath, Path(config.data.params[a]).name)
+            elif 'vas.VAS' in config.data.params.train.target:
+                config.data.params[a] = os.path.join(datapath, 'features', '*', Path(config.data.params[a]).name)
+
+    with st.beta_expander('Streamlit Logs'):
+        dsets, model, vocoder, global_step, feat_extractor = load_model_and_dataset(
+            config, ckpt, ckpt_vocoder, gpu=True, eval_mode=True
+        )
+
+    with st.beta_expander('Sampler Model Config'):
+        st.text(f'Global step: {global_step}')
+        st.text(f'Checkpoint: {ckpt}')
+        st.json(OmegaConf.to_container(config))
+
+    with torch.no_grad():
+        if len(dsets.datasets) > 1:
+            splits = sorted(dsets.datasets.keys())
+            if 'vas.VAS' in config.data.params.train.target:
+                # prevent loading train on demo  which results in a error in streamlit
+                splits = ['validation', 'train']
+            st.sidebar.header('Select Data')
+            split = st.sidebar.radio('Split', splits)
+            dset = dsets.datasets[split]
+        else:
+            dset = next(iter(dsets.datasets.values()))
+
+        # filter dataset for available items using set intersection
+        if 'vggsound.VGGSound' in config.data.params.train.target:
+            avail_dataset = glob.glob(config.data.params['spec_dir_path'] + '/*_mel.npy')
+            avail_dataset = sorted(list(set(avail_dataset).intersection(dset.specs_dataset.dataset)))
+            avail_targets = list({dset.specs_dataset.video2target[Path(c).stem[:11]] for c in avail_dataset})
+            avail_label2target = {dset.specs_dataset.target2label[t]: t for t in avail_targets}
+            dset.specs_dataset.label2target = avail_label2target
+            dset.specs_dataset.dataset = avail_dataset
+            if hasattr(dset, 'feats_dataset'):
+                avail_dataset = glob.glob(config.data.params['rgb_feats_dir_path'] + '/*.pkl')
+                avail_dataset = [Path(p).stem for p in avail_dataset]
+                avail_dataset = sorted(list(set(avail_dataset).intersection(dset.feats_dataset.dataset)))
+                dset.feats_dataset.dataset = avail_dataset
+        elif 'vas.VAS' in config.data.params.train.target:
+            avail_dataset = glob.glob(config.data.params['spec_dir_path'] + '/*_mel.npy')
+            avail_dataset = [os.path.join(Path(p).parent.parent.stem, Path(p).stem.replace('_mel', '')) for p in avail_dataset]
+            avail_dataset = sorted(list(set(avail_dataset).intersection(dset.specs_dataset.dataset)))
+            dset.specs_dataset.dataset = avail_dataset
+            if hasattr(dset, 'feats_dataset'):
+                avail_dataset = glob.glob(config.data.params['rgb_feats_dir_path'] + '/*.pkl')
+                avail_dataset = [os.path.join(Path(p).parent.parent.stem, Path(p).stem) for p in avail_dataset]
+                avail_dataset = sorted(list(set(avail_dataset).intersection(dset.feats_dataset.dataset)))
+                dset.feats_dataset.dataset = avail_dataset
+
+        if len(dset) == 0:
+            st.sidebar.info('There are no samples for this split. Please select another split.')
+            st.stop()
+
+        select_specific_class = st.sidebar.checkbox('Select Specific Class...', value=False)
+
+        # add available classes
+        if select_specific_class:
+            labels = dset.specs_dataset.label2target.keys()
+            label_choice = st.sidebar.selectbox('Select a Class', sorted(labels))
+            # filter dataset for observations belonging to a specific class
+            label2target = dset.specs_dataset.label2target
+            if 'vggsound.VGGSound' in config.data.params.train.target:
+                video2target = dset.specs_dataset.video2target
+                paths = dset.specs_dataset.dataset
+                filter_paths = [c for c in paths if video2target[Path(c).stem[:11]] == label2target[label_choice]]
+                dset.specs_dataset.dataset = filter_paths
+                # if we have another first stage we need to do something extra
+                if hasattr(dset, 'feats_dataset'):
+                    paths_feats = dset.feats_dataset.dataset
+                    filter_paths_feats = [c for c in paths_feats if video2target[Path(c).stem[:11]] == label2target[label_choice]]
+                    dset.feats_dataset.dataset = filter_paths_feats
+            elif 'vas.VAS' in config.data.params.train.target:
+                paths = dset.specs_dataset.dataset
+                filter_paths = [c for c in paths if c.startswith(label_choice)]
+                dset.specs_dataset.dataset = filter_paths
+                # if we have another first stage we need to do something extra
+                if hasattr(dset, 'feats_dataset'):
+                    paths_feats = dset.feats_dataset.dataset
+                    filter_paths_feats = [c for c in paths_feats if c.startswith(label_choice)]
+                    dset.feats_dataset.dataset = filter_paths_feats
+
+        batch_size = 1
+        start_index = st.sidebar.number_input(f'Example Index in the Dataset [0, {len(dset)-1}]',
+                                              value=0, min_value=0, max_value=len(dset)-batch_size)
+        indices = list(range(start_index, start_index+batch_size))
+
+        batch = default_collate([dset[i] for i in indices])
+
+        if select_specific_class:
+            # restoring original dataset because we cached the dataset class and filtered for one class.
+            # Next time, the filtered dataset will be filtered again which empties the dataset.
+            dset.specs_dataset.dataset = paths
+            # if we have another first stage we need to do something extra
+            if hasattr(dset, 'feats_dataset'):
+                dset.feats_dataset.dataset = paths_feats
+
+        feat_sampler_cfg = dset.condition_dataset_cfg.feat_sampler_cfg
+        cond_stage_model_name = model.cond_stage_model.__class__.__name__
+        transformer_model_name = model.transformer.__class__.__name__
+
+        if (cond_stage_model_name in ['VQModel1d', 'FeatClusterStage']
+            or transformer_model_name in ['GPTFeats', 'GPTFeatsClass']):
+            specs_key_in_batch = 'file_path_specs_'
+            flip_c_dims = None
+        elif transformer_model_name == 'GPTClass':
+            specs_key_in_batch = 'file_path_'
+            flip_c_dims = None
+        else:
+            specs_key_in_batch = 'file_path_'
+            flip_c_dims = (2,)
+        flip_z_dims = (2,)
+
+        st.text('')
+        with st.beta_expander(f'Original Video. Class: {batch["label"]}.'):
+            vid_fname = Path(batch[specs_key_in_batch][0]).name.replace('_mel.npy', '.mp4')
+            st.text(f'Video file name: {vid_fname}')
+            if 'vggsound.VGGSound' in config.data.params.train.target:
+                video_file = open(os.path.join(raw_vids_dir, vid_fname), 'rb').read()
+            elif 'vas.VAS' in config.data.params.train.target:
+                cls = batch['label'][0]
+                video_file = open(os.path.join(raw_vids_dir.replace('*', cls), vid_fname), 'rb').read()
+            st.video(video_file, format='video/mp4')
+
+        x = model.get_input(model.first_stage_key, batch).to(model.device)
+        c = model.get_input(model.cond_stage_key, batch)
+        if isinstance(c, dict):
+            c = {k: v.to(model.device) for k, v in c.items()}
+        else:
+            c = c.to(model.device)
+
+        quant_z, z_indices = model.encode_to_z(x)
+        quant_c, c_indices = model.encode_to_c(c)
+
+        xrec = model.first_stage_model.decode(quant_z)
+        crec = model.cond_stage_model.decode(quant_c)
+
+        if transformer_model_name == 'GPTFeatsClass':
+            orig_cond_shape = c['feature'].squeeze().shape
+            rec_cond_shape = crec["feature"].squeeze().shape
+        else:
+            orig_cond_shape = c.squeeze().shape
+            rec_cond_shape = crec.squeeze().shape
+
+        st.text('')
+        with st.beta_expander(f'Conditioning {list(orig_cond_shape)}'):
+            if transformer_model_name == 'GPTClass':
+                st.write(batch['label'])
+            elif transformer_model_name == 'GPTFeatsClass':
+                st.write(batch['label'])
+                st.write(tensor_to_plt(c['feature'], flip_dims=flip_c_dims))
+            else:
+                st.write(tensor_to_plt(c, flip_dims=flip_c_dims))
+        # with st.beta_expander(f'Conditioning Reconstruction {list(rec_cond_shape)}'):
+        #     if transformer_model_name == 'GPTClass':
+        #         st.write(batch['label'])
+        #     elif transformer_model_name == 'GPTFeatsClass':
+        #         st.write(batch['label'])
+        #         st.write(tensor_to_plt(crec['feature'], flip_dims=flip_c_dims))
+        #     else:
+        #         st.write(tensor_to_plt(crec, flip_dims=flip_c_dims))
+
+        st.sidebar.header('Results Handling')
+        update_every = st.sidebar.number_input('Display Result Every ... Step', value=3)
+        show_griffin_lim = st.sidebar.checkbox(
+            'Also Show Griffin-Lim', value=False,
+            help='Show spectrogram reconstruction from Griffin-Lim algorithm along the pre-trained vocoder')
+        to_save_results = st.sidebar.checkbox('Save Results', value=True)
+
+        st.text('')
+        with st.beta_expander(f'Input {list(x.squeeze().shape)}'):
+            st.write(tensor_to_plt(x, flip_dims=flip_z_dims))
+            topk_results = get_class_preditions(x, feat_extractor)
+            st.text(topk_results)
+            if st.button('Get Audio (Input)'):
+                spec_to_audio_to_st(x, config.data.params.spec_dir_path,
+                                    config.data.params.sample_rate, show_griffin_lim, vocoder)
+        with st.beta_expander(f'Input Reconstruction from SpecVQGAN {list(xrec.squeeze().shape)}', expanded=True):
+            st.write(tensor_to_plt(xrec, flip_dims=flip_z_dims))
+            topk_results = get_class_preditions(xrec, feat_extractor)
+            st.text(topk_results)
+            if st.button('Get Audio (Input Reconstruction)'):
+                spec_to_audio_to_st(xrec, config.data.params.spec_dir_path,
+                                    config.data.params.sample_rate, show_griffin_lim, vocoder)
+
+        st.sidebar.header('Sampling Parameters')
+        temperature = st.sidebar.number_input(
+            'Softmax Temperature', value=1.0,
+            help='$T$ in $\exp(x_i/T) / \Sigma_j \exp(x_j/T)$'
+        )
+        top_x = st.sidebar.number_input(
+            'Top X', value=config.model.params.first_stage_config.params.n_embed // 2,
+            help='Cuts sampling space of the next token to Top $X$ highest probability tokens. '
+                 + 'It increases diversity of samples but at the cost of relevance. '
+                 + 'As a rule of thumb, use `X = |codebook| // 2`.'
+        )
+        W_scale = st.sidebar.number_input(
+            'Temporal Scale', value=1, min_value=1,
+            help='The output length is `temporal_scale * 9.8 seconds`.')
+        sample_half = st.sidebar.checkbox(
+            'Prime with GT Tokens', value=False,
+            help='If checked, the first half of the tokens will be taken from the ground truth audio'
+            + ' codebook representation and sampling will continue this sequence.')
+        full_att_mat = st.sidebar.checkbox(
+            'Show Full Attention Matrix', value=False,
+            help='The attention will be shown for each time stamp instead of only the current one.')
+        if full_att_mat:
+            scale_att_by_prior = st.sidebar.checkbox(
+                'Subtract Prior from Attention', value=True,
+                help='If checked, subtracts $1/S$ from each attention weight, where $S$ is number of'
+                + ' previous tokens. For example, $[2/3, 1/6, 1/6]~–~[1/3, 1/3, 1/3] = [1/3, -1/6, -1/6]$')
+        else:
+            scale_att_by_prior = False
+
+        st.header('Sampling Results:')
+
+        # dummy outputs just to reserver some space
+        placeholders = {
+            'info': st.text('Step: (?,?) | Local: (?,?) | Crop: (?:?,?:?)'),
+            'time': st.text('Time: ?'),
+            'mode': st.text('Mode: ?'),
+            'title_c_att': st.text('Attention to C.'),
+            'c_att': st.pyplot(tensor_to_plt(torch.zeros_like(x))),
+            'title_z_att': st.text('Attention to Z.'),
+            'z_att': st.pyplot(tensor_to_plt(torch.zeros_like(x))),
+            'title_gen_spec': st.text('Generated sample'),
+            'gen_spec': st.pyplot(tensor_to_plt(torch.zeros_like(x))),
+            'title_rec_audio': st.text('Reconstructed Audio of the Generated Sample'),
+        }
+
+        sampling_shape = list(quant_z.shape)
+        # hr_w * w_scale
+        sampling_shape[3] *= W_scale
+
+        if st.sidebar.button('Start Sampling'):
+            mode = 'half' if sample_half else 'full'
+            sample_conditionally(
+                z_indices,
+                sampling_shape,
+                c_indices,
+                quant_c,
+                full_att_mat,
+                scale_att_by_prior,
+                temperature,
+                top_x,
+                update_every,
+                placeholders,
+                cond_stage_model_name,
+                flip_z_dims,
+                flip_c_dims,
+                to_save_results,
+                logdir,
+                batch,
+                specs_key_in_batch,
+                vocoder,
+                feat_sampler_cfg,
+                show_griffin_lim,
+                feat_extractor,
+                mode
+            )
diff --git a/specvqgan/data/vas.py b/specvqgan/data/vas.py
new file mode 100644
index 0000000..db957bd
--- /dev/null
+++ b/specvqgan/data/vas.py
@@ -0,0 +1,439 @@
+import os
+import pickle
+from glob import glob
+from pathlib import Path
+
+import numpy as np
+import torch
+
+import sys
+sys.path.insert(0, '.')  # nopep8
+from specvqgan.modules.losses.vggishish.transforms import Crop
+from train import instantiate_from_config
+
+
+def make_split_files(split_path, feat_folder, feat_suffix):
+    # feat_suffix is e.g. '_mel.npy' or '.pkl
+    # output: train and valid files with rows like `<class>/video_<id>`
+    train_dataset = []
+    valid_dataset = []
+    filepaths = sorted(glob(os.path.join(feat_folder, '*' + feat_suffix)))
+    assert len(filepaths) > 0, 'Empty filelist'
+    # [`<class>/video_<id>`]
+    classes = [Path(f).parent.parent.stem for f in filepaths]
+    vid_ids = [Path(f).name.replace(feat_suffix, '') for f in filepaths]
+
+    for cls in sorted(list(set(classes))):
+        n_valid = 128 if cls in ['dog', 'fireworks', 'baby', 'drum'] else 64
+        cls_dataset = []
+        for c, v in zip(classes, vid_ids):
+            if c == cls:
+                cls_dataset.append(f'{c}/{v}')
+        train_dataset.extend(cls_dataset[:-n_valid])
+        valid_dataset.extend(cls_dataset[-n_valid:])
+
+    save_train_path = Path(split_path).parent / 'vas_train.txt'
+    save_valid_path = Path(split_path).parent / 'vas_valid.txt'
+
+    with open(save_train_path, 'w') as outf:
+        for row in train_dataset:
+            outf.write(f'{row}\n')
+    with open(save_valid_path, 'w') as outf:
+        for row in valid_dataset:
+            outf.write(f'{row}\n')
+
+class CropImage(Crop):
+    def __init__(self, *crop_args):
+        super().__init__(*crop_args)
+
+class CropFeats(Crop):
+    def __init__(self, *crop_args):
+        super().__init__(*crop_args)
+
+    def __call__(self, item):
+        item['feature'] = self.preprocessor(image=item['feature'])['image']
+        return item
+
+class CropCoords(Crop):
+    def __init__(self, *crop_args):
+        super().__init__(*crop_args)
+
+    def __call__(self, item):
+        item['coord'] = self.preprocessor(image=item['coord'])['image']
+        return item
+
+
+class ResampleFrames(object):
+    def __init__(self, feat_sample_size, times_to_repeat_after_resample=None):
+        self.feat_sample_size = feat_sample_size
+        self.times_to_repeat_after_resample = times_to_repeat_after_resample
+
+    def __call__(self, item):
+        feat_len = item['feature'].shape[0]
+
+        ## resample
+        assert feat_len >= self.feat_sample_size
+        # evenly spaced points (abcdefghkl -> aoooofoooo)
+        idx = np.linspace(0, feat_len, self.feat_sample_size, dtype=np.int, endpoint=False)
+        # xoooo xoooo -> ooxoo ooxoo
+        shift = feat_len // (self.feat_sample_size + 1)
+        idx = idx + shift
+
+        ## repeat after resampling (abc -> aaaabbbbcccc)
+        if self.times_to_repeat_after_resample is not None and self.times_to_repeat_after_resample > 1:
+            idx = np.repeat(idx, self.times_to_repeat_after_resample)
+
+        item['feature'] = item['feature'][idx, :]
+        return item
+
+class VASSpecs(torch.utils.data.Dataset):
+
+    def __init__(self, split, spec_dir_path, mel_num=None, spec_len=None, spec_crop_len=None,
+                 random_crop=None, crop_coord=None, for_which_class=None):
+        super().__init__()
+        self.split = split
+        self.spec_dir_path = spec_dir_path
+        # fixing split_path in here because of compatibility with vggsound which hangles it in vggishish
+        self.split_path = f'./data/vas_{split}.txt'
+        self.feat_suffix = '_mel.npy'
+
+        if not os.path.exists(self.split_path):
+            print(f'split does not exist in {self.split_path}. Creating new ones...')
+            make_split_files(self.split_path, spec_dir_path, self.feat_suffix)
+
+        full_dataset = open(self.split_path).read().splitlines()
+        # ['baby/video_00000', ..., 'dog/video_00000', ...]
+        if for_which_class:
+            self.dataset = [v for v in full_dataset if v.startswith(for_which_class)]
+        else:
+            self.dataset = full_dataset
+
+        unique_classes = sorted(list(set([cls_vid.split('/')[0] for cls_vid in self.dataset])))
+        self.label2target = {label: target for target, label in enumerate(unique_classes)}
+
+        self.transforms = CropImage([mel_num, spec_crop_len], random_crop)
+
+    def __getitem__(self, idx):
+        item = {}
+
+        cls, vid = self.dataset[idx].split('/')
+        spec_path = os.path.join(self.spec_dir_path.replace('*', cls), f'{vid}{self.feat_suffix}')
+
+        spec = np.load(spec_path)
+        item['input'] = spec
+        item['file_path_'] = spec_path
+
+        item['label'] = cls
+        item['target'] = self.label2target[cls]
+
+        if self.transforms is not None:
+            item = self.transforms(item)
+
+        # specvqgan expects `image` and `file_path_` keys in the item
+        # it also expects inputs in [-1, 1] but specs are in [0, 1]
+        item['image'] = 2 * item['input'] - 1
+        item.pop('input')
+
+        return item
+
+    def __len__(self):
+        return len(self.dataset)
+
+
+class VASSpecsTrain(VASSpecs):
+    def __init__(self, specs_dataset_cfg):
+        super().__init__('train', **specs_dataset_cfg)
+
+class VASSpecsValidation(VASSpecs):
+    def __init__(self, specs_dataset_cfg):
+        super().__init__('valid', **specs_dataset_cfg)
+
+class VASSpecsTest(VASSpecs):
+    def __init__(self, specs_dataset_cfg):
+        super().__init__('test', **specs_dataset_cfg)
+
+
+class VASFeats(torch.utils.data.Dataset):
+
+    def __init__(self, split, rgb_feats_dir_path, flow_feats_dir_path, feat_len, feat_depth, feat_crop_len,
+                 replace_feats_with_random, random_crop, split_path, for_which_class, feat_sampler_cfg):
+        super().__init__()
+        self.split = split
+        self.rgb_feats_dir_path = rgb_feats_dir_path
+        self.flow_feats_dir_path = flow_feats_dir_path
+        self.feat_len = feat_len
+        self.feat_depth = feat_depth
+        self.feat_crop_len = feat_crop_len
+        self.split_path = split_path
+        self.feat_suffix = '.pkl'
+        self.feat_sampler_cfg = feat_sampler_cfg
+        self.replace_feats_with_random = replace_feats_with_random
+
+        if not os.path.exists(split_path):
+            print(f'split does not exist in {split_path}. Creating new ones...')
+            make_split_files(split_path, rgb_feats_dir_path, self.feat_suffix)
+
+        full_dataset = open(split_path).read().splitlines()
+        if for_which_class:
+            # ['baby/video_00000', ..., 'dog/video_00000', ...]
+            self.dataset = [v for v in full_dataset if v.startswith(for_which_class)]
+        else:
+            self.dataset = full_dataset
+
+        unique_classes = sorted(list(set([cls_vid.split('/')[0] for cls_vid in self.dataset])))
+        self.label2target = {label: target for target, label in enumerate(unique_classes)}
+
+        self.feats_transforms = CropFeats([feat_crop_len, feat_depth], random_crop)
+        # self.normalizer = StandardNormalizeFeats(rgb_feats_dir_path, flow_feats_dir_path, feat_len)
+        # ResampleFrames
+        self.feat_sampler = None if feat_sampler_cfg is None else instantiate_from_config(feat_sampler_cfg)
+
+    def __getitem__(self, idx):
+        item = dict()
+        cls, vid = self.dataset[idx].split('/')
+
+        rgb_path = os.path.join(self.rgb_feats_dir_path.replace('*', cls), f'{vid}{self.feat_suffix}')
+        # just a dummy random features acting like a fake interface for no features experiment
+        if self.replace_feats_with_random:
+            rgb_feats = np.random.rand(self.feat_len, self.feat_depth//2).astype(np.float32)
+        else:
+            rgb_feats = pickle.load(open(rgb_path, 'rb'), encoding='bytes')
+        feats = rgb_feats
+        item['file_path_'] = (rgb_path, )
+
+        # also preprocess flow
+        if self.flow_feats_dir_path is not None:
+            flow_path = os.path.join(self.flow_feats_dir_path.replace('*', cls), f'{vid}{self.feat_suffix}')
+            # just a dummy random features acting like a fake interface for no features experiment
+            if self.replace_feats_with_random:
+                flow_feats = np.random.rand(self.feat_len, self.feat_depth//2).astype(np.float32)
+            else:
+                flow_feats = pickle.load(open(flow_path, 'rb'), encoding='bytes')
+            # (T, 2*D)
+            feats = np.concatenate((rgb_feats, flow_feats), axis=1)
+            item['file_path_'] = (rgb_path, flow_path)
+
+        # pad or trim
+        feats_padded = np.zeros((self.feat_len, feats.shape[1]))
+        feats_padded[:feats.shape[0], :] = feats[:self.feat_len, :]
+        item['feature'] = feats_padded
+
+        item['label'] = cls
+        item['target'] = self.label2target[cls]
+
+        if self.feats_transforms is not None:
+            item = self.feats_transforms(item)
+
+        if self.feat_sampler is not None:
+            item = self.feat_sampler(item)
+
+        return item
+
+    def __len__(self):
+        return len(self.dataset)
+
+
+# class VGGSoundFeatsTrain(VASFeats):
+#     def __init__(self, condition_dataset_cfg):
+#         super().__init__('train', **condition_dataset_cfg)
+
+# class VGGSoundFeatsValidation(VASFeats):
+#     def __init__(self, condition_dataset_cfg):
+#         super().__init__('valid', **condition_dataset_cfg)
+
+# class VGGSoundFeatsTest(VASFeats):
+#     def __init__(self, condition_dataset_cfg):
+#         super().__init__('test', **condition_dataset_cfg)
+
+
+class VASSpecsCondOnFeats(torch.utils.data.Dataset):
+
+    def __init__(self, split, specs_dataset_cfg, condition_dataset_cfg):
+        self.specs_dataset_cfg = specs_dataset_cfg
+        self.condition_dataset_cfg = condition_dataset_cfg
+
+        self.specs_dataset = VASSpecs(split, **specs_dataset_cfg)
+        self.feats_dataset = VASFeats(split, **condition_dataset_cfg)
+        assert len(self.specs_dataset) == len(self.feats_dataset)
+
+    def __getitem__(self, idx):
+        specs_item = self.specs_dataset[idx]
+        feats_item = self.feats_dataset[idx]
+
+        # sanity check and removing those from one of the dicts
+        for key in ['target', 'label']:
+            assert specs_item[key] == feats_item[key]
+            feats_item.pop(key)
+
+        # keeping both sets of paths to features
+        specs_item['file_path_specs_'] = specs_item.pop('file_path_')
+        feats_item['file_path_feats_'] = feats_item.pop('file_path_')
+
+        # merging both dicts
+        specs_feats_item = dict(**specs_item, **feats_item)
+
+        return specs_feats_item
+
+    def __len__(self):
+        return len(self.specs_dataset)
+
+
+class VASSpecsCondOnFeatsTrain(VASSpecsCondOnFeats):
+    def __init__(self, specs_dataset_cfg, condition_dataset_cfg):
+        super().__init__('train', specs_dataset_cfg, condition_dataset_cfg)
+
+class VASSpecsCondOnFeatsValidation(VASSpecsCondOnFeats):
+    def __init__(self, specs_dataset_cfg, condition_dataset_cfg):
+        super().__init__('valid', specs_dataset_cfg, condition_dataset_cfg)
+
+
+class VASSpecsCondOnCoords(torch.utils.data.Dataset):
+
+    def __init__(self, split, specs_dataset_cfg, condition_dataset_cfg):
+        self.specs_dataset_cfg = specs_dataset_cfg
+        self.condition_dataset_cfg = condition_dataset_cfg
+
+        self.crop_coord = self.specs_dataset_cfg.crop_coord
+        if self.crop_coord:
+            print('DID YOU EXPECT THAT COORDS ARE CROPPED NOW?')
+            self.F = self.specs_dataset_cfg.mel_num
+            self.T = self.specs_dataset_cfg.spec_len
+            self.T_crop = self.specs_dataset_cfg.spec_crop_len
+            self.transforms = CropCoords([self.F, self.T_crop], self.specs_dataset_cfg.random_crop)
+
+        self.specs_dataset = VASSpecs(split, **specs_dataset_cfg)
+
+    def __getitem__(self, idx):
+        specs_item = self.specs_dataset[idx]
+        if self.crop_coord:
+            coord = np.arange(self.F * self.T).reshape(self.T, self.F) / (self.T * self.F)
+            coord = coord.T
+            specs_item['coord'] = coord
+            specs_item = self.transforms(specs_item)
+        else:
+            F, T = specs_item['image'].shape
+            coord = np.arange(F * T).reshape(T, F) / (T * F)
+            coord = coord.T
+            specs_item['coord'] = coord
+
+        return specs_item
+
+    def __len__(self):
+        return len(self.specs_dataset)
+
+
+class VASSpecsCondOnCoordsTrain(VASSpecsCondOnCoords):
+    def __init__(self, specs_dataset_cfg, condition_dataset_cfg):
+        super().__init__('train', specs_dataset_cfg, condition_dataset_cfg)
+
+class VASSpecsCondOnCoordsValidation(VASSpecsCondOnCoords):
+    def __init__(self, specs_dataset_cfg, condition_dataset_cfg):
+        super().__init__('valid', specs_dataset_cfg, condition_dataset_cfg)
+
+
+class VASSpecsCondOnClass(torch.utils.data.Dataset):
+
+    def __init__(self, split, specs_dataset_cfg, condition_dataset_cfg):
+        self.specs_dataset_cfg = specs_dataset_cfg
+        # not used anywhere else. Kept for compatibility
+        self.condition_dataset_cfg = condition_dataset_cfg
+        self.specs_dataset = VASSpecs(split, **specs_dataset_cfg)
+
+    def __getitem__(self, idx):
+        specs_item = self.specs_dataset[idx]
+        return specs_item
+
+    def __len__(self):
+        return len(self.specs_dataset)
+
+class VASSpecsCondOnClassTrain(VASSpecsCondOnClass):
+    def __init__(self, specs_dataset_cfg, condition_dataset_cfg):
+        super().__init__('train', specs_dataset_cfg, condition_dataset_cfg)
+
+class VASSpecsCondOnClassValidation(VASSpecsCondOnClass):
+    def __init__(self, specs_dataset_cfg, condition_dataset_cfg):
+        super().__init__('valid', specs_dataset_cfg, condition_dataset_cfg)
+
+
+class VASSpecsCondOnFeatsAndClass(VASSpecsCondOnFeats):
+    def __init__(self, split, specs_dataset_cfg, condition_dataset_cfg):
+        super().__init__(split, specs_dataset_cfg, condition_dataset_cfg)
+
+class VASSpecsCondOnFeatsAndClassTrain(VASSpecsCondOnFeatsAndClass):
+    def __init__(self, specs_dataset_cfg, condition_dataset_cfg):
+        super().__init__('train', specs_dataset_cfg, condition_dataset_cfg)
+
+class VASSpecsCondOnFeatsAndClassValidation(VASSpecsCondOnFeatsAndClass):
+    def __init__(self, specs_dataset_cfg, condition_dataset_cfg):
+        super().__init__('valid', specs_dataset_cfg, condition_dataset_cfg)
+
+class VASSpecsCondOnFeatsAndClassTest(VASSpecsCondOnFeatsAndClass):
+    def __init__(self, specs_dataset_cfg, condition_dataset_cfg):
+        super().__init__('test', specs_dataset_cfg, condition_dataset_cfg)
+
+# class StandardNormalizeFeats(object):
+#     def __init__(self, rgb_feats_dir_path, flow_feats_dir_path, feat_len,
+#                  train_ids_path='./data/vggsound_test.txt', cache_path='./data/'):
+#         self.rgb_feats_dir_path = rgb_feats_dir_path
+#         self.flow_feats_dir_path = flow_feats_dir_path
+#         self.train_ids_path = train_ids_path
+#         self.feat_len = feat_len
+#         # making the stats filename to match the specs dir name
+#         self.cache_path = os.path.join(
+#             cache_path, f'train_means_stds_{Path(rgb_feats_dir_path).stem}.txt'.replace('_rgb', '')
+#         )
+#         logger.info('Assuming that the input stats are calculated using preprocessed spectrograms (log)')
+#         self.train_stats = self.calculate_or_load_stats()
+
+#     def __call__(self, rgb_flow_feats):
+#         return (rgb_flow_feats - self.train_stats['means']) / self.train_stats['stds']
+
+#     def calculate_or_load_stats(self):
+#         try:
+#             # (F, 2)
+#             train_stats = np.loadtxt(self.cache_path)
+#             means, stds = train_stats.T
+#             logger.info('Trying to load train stats for Standard Normalization of inputs')
+#         except OSError:
+#             logger.info('Could not find the precalculated stats for Standard Normalization. Calculating...')
+#             train_vid_ids = open(self.train_ids_path).read().splitlines()
+#             means = [None] * len(train_vid_ids)
+#             stds = [None] * len(train_vid_ids)
+#             for i, vid_id in enumerate(tqdm(train_vid_ids)):
+#                 rgb_path = os.path.join(self.rgb_feats_dir_path, f'{vid_id}.pkl')
+#                 flow_path = os.path.join(self.flow_feats_dir_path, f'{vid_id}.pkl')
+#                 with open(rgb_path, 'rb') as f:
+#                     rgb_feats = pickle.load(f, encoding='bytes')
+#                 with open(flow_path, 'rb') as f:
+#                     flow_feats = pickle.load(f, encoding='bytes')
+#                 # (T, 2*D)
+#                 feats = np.concatenate((rgb_feats, flow_feats), axis=1)
+#                 # pad or trim
+#                 feats_padded = np.zeros((self.feat_len, feats.shape[1]))
+#                 feats_padded[:feats.shape[0], :] = feats[:self.feat_len, :]
+#                 feats = feats_padded
+#                 means[i] = feats.mean(axis=1)
+#                 stds[i] = feats.std(axis=1)
+#             # (F) <- (num_files, D)
+#             means = np.array(means).mean(axis=0)
+#             stds = np.array(stds).mean(axis=0)
+#             # saving in two columns
+#             np.savetxt(self.cache_path, np.vstack([means, stds]).T, fmt='%0.8f')
+#         means = means.reshape(-1, 1)
+#         stds = stds.reshape(-1, 1)
+#         assert 'train' in self.train_ids_path
+#         return {'means': means, 'stds': stds}
+
+
+if __name__ == '__main__':
+    from omegaconf import OmegaConf
+
+    # SPECTROGRAMS + FEATURES
+    cfg = OmegaConf.load('./configs/vas_transformer.yaml')
+    data = instantiate_from_config(cfg.data)
+    data.prepare_data()
+    data.setup()
+    print(data.datasets['train'][24])
+    print(data.datasets['validation'][24])
+    print(data.datasets['validation'][-1]['feature'].shape)
+    print(data.datasets['validation'][-1]['image'].shape)
diff --git a/specvqgan/data/vggsound.py b/specvqgan/data/vggsound.py
new file mode 100644
index 0000000..3f4153b
--- /dev/null
+++ b/specvqgan/data/vggsound.py
@@ -0,0 +1,413 @@
+import csv
+import os
+import pickle
+import sys
+
+import numpy as np
+import torch
+
+sys.path.insert(0, '.')  # nopep8
+from specvqgan.modules.losses.vggishish.dataset import VGGSound as VGGSoundSpectrogramDataset
+from specvqgan.modules.losses.vggishish.transforms import Crop
+from train import instantiate_from_config
+
+
+class CropImage(Crop):
+    def __init__(self, *crop_args):
+        super().__init__(*crop_args)
+
+class CropFeats(Crop):
+    def __init__(self, *crop_args):
+        super().__init__(*crop_args)
+
+    def __call__(self, item):
+        item['feature'] = self.preprocessor(image=item['feature'])['image']
+        return item
+
+class CropCoords(Crop):
+    def __init__(self, *crop_args):
+        super().__init__(*crop_args)
+
+    def __call__(self, item):
+        item['coord'] = self.preprocessor(image=item['coord'])['image']
+        return item
+
+# class SelectFrames(object):
+#     def __init__(self, split, feat_sample_size):
+#         self.split = split
+#         self.feat_sample_size = feat_sample_size
+#         self.stochastic_on_train = False
+
+#     def __call__(self, item):
+#         feat_len = item['feature'].shape[0]
+#         if self.stochastic_on_train and self.split == 'train':
+#             idx = np.random.choice(feat_len, self.feat_sample_size)
+#         else:
+#             assert feat_len >= self.feat_sample_size
+#             # evenly spaced points
+#             idx = np.linspace(0, feat_len, self.feat_sample_size, dtype=np.int, endpoint=False)
+#             # xoooxooo -> ooxooxoo
+#             shift = feat_len // (self.feat_sample_size + 1)
+#             idx = idx + shift
+#         item['feature'] = item['feature'][idx, :]
+#         return item
+
+class ResampleFrames(object):
+    def __init__(self, feat_sample_size, times_to_repeat_after_resample=None):
+        self.feat_sample_size = feat_sample_size
+        self.times_to_repeat_after_resample = times_to_repeat_after_resample
+
+    def __call__(self, item):
+        feat_len = item['feature'].shape[0]
+
+        ## resample
+        assert feat_len >= self.feat_sample_size
+        # evenly spaced points (abcdefghkl -> aoooofoooo)
+        idx = np.linspace(0, feat_len, self.feat_sample_size, dtype=np.int, endpoint=False)
+        # xoooo xoooo -> ooxoo ooxoo
+        shift = feat_len // (self.feat_sample_size + 1)
+        idx = idx + shift
+
+        ## repeat after resampling (abc -> aaaabbbbcccc)
+        if self.times_to_repeat_after_resample is not None and self.times_to_repeat_after_resample > 1:
+            idx = np.repeat(idx, self.times_to_repeat_after_resample)
+
+        item['feature'] = item['feature'][idx, :]
+        return item
+
+
+class VGGSoundSpecs(VGGSoundSpectrogramDataset):
+
+    def __init__(self, split, spec_dir_path, mel_num=None, spec_len=None, spec_crop_len=None,
+                 random_crop=None, crop_coord=None, for_which_class=None):
+        super().__init__(split, spec_dir_path)
+        if for_which_class:
+            raise NotImplementedError
+        self.transforms = CropImage([mel_num, spec_crop_len], random_crop)
+
+    def __getitem__(self, idx):
+        # accessing VGGSoundSpectrogramDataset
+        item = super().__getitem__(idx)
+        # specvqgan expects `image` and `file_path_` keys in the item
+        # it also expects inputs in [-1, 1] but vggsound are in [0, 1]
+        item['image'] = 2 * item['input'] - 1
+        item['file_path_'] = item['input_path']
+        item.pop('input')
+        item.pop('input_path')
+        return item
+
+
+class VGGSoundSpecsTrain(VGGSoundSpecs):
+    def __init__(self, specs_dataset_cfg):
+        super().__init__('train', **specs_dataset_cfg)
+
+class VGGSoundSpecsValidation(VGGSoundSpecs):
+    def __init__(self, specs_dataset_cfg):
+        super().__init__('valid', **specs_dataset_cfg)
+
+class VGGSoundSpecsTest(VGGSoundSpecs):
+    def __init__(self, specs_dataset_cfg):
+        super().__init__('test', **specs_dataset_cfg)
+
+
+class VGGSoundFeats(torch.utils.data.Dataset):
+
+    def __init__(self, split, rgb_feats_dir_path, flow_feats_dir_path, feat_len, feat_depth, feat_crop_len,
+                 replace_feats_with_random, random_crop, split_path, meta_path='./data/vggsound.csv',
+                 for_which_class=None, feat_sampler_cfg=None):
+        super().__init__()
+        self.split = split
+        self.meta_path = meta_path
+        self.rgb_feats_dir_path = rgb_feats_dir_path
+        self.flow_feats_dir_path = flow_feats_dir_path
+        self.feat_len = feat_len
+        self.feat_depth = feat_depth
+        self.feat_crop_len = feat_crop_len
+        self.split_path = split_path
+        self.feat_sampler_cfg = feat_sampler_cfg
+        self.replace_feats_with_random = replace_feats_with_random
+
+        vggsound_meta = list(csv.reader(open(meta_path), quotechar='"'))
+        unique_classes = sorted(list(set(row[2] for row in vggsound_meta)))
+        self.label2target = {label: target for target, label in enumerate(unique_classes)}
+        self.target2label = {target: label for label, target in self.label2target.items()}
+        self.video2target = {row[0]: self.label2target[row[2]] for row in vggsound_meta}
+
+        if not os.path.exists(split_path):
+            raise NotImplementedError(f'The splits with clips shoud be available @ {split_path}')
+
+        if for_which_class:
+            raise NotImplementedError
+        self.dataset = open(split_path).read().splitlines()
+
+        self.feats_transforms = CropFeats([feat_crop_len, feat_depth], random_crop)
+
+        # ResampleFrames
+        self.feat_sampler = None if feat_sampler_cfg is None else instantiate_from_config(feat_sampler_cfg)
+
+    def __getitem__(self, idx):
+        item = dict()
+        video_clip_name = self.dataset[idx]
+        # '/path/zyTX_1BXKDE_16000_26000_mel.npy' -> 'zyTX_1BXKDE_16000_26000'
+        # video_clip_name = Path(item['file_path_']).stem.replace('_mel.npy', '')
+        video_name = video_clip_name[:11]
+
+        rgb_path = os.path.join(self.rgb_feats_dir_path, f'{video_clip_name}.pkl')
+        if self.replace_feats_with_random:
+            rgb_feats = np.random.rand(self.feat_len, self.feat_depth//2).astype(np.float32)
+        else:
+            rgb_feats = pickle.load(open(rgb_path, 'rb'), encoding='bytes')
+        feats = rgb_feats
+        item['file_path_'] = (rgb_path, )
+
+        # also preprocess flow
+        if self.flow_feats_dir_path is not None:
+            flow_path = os.path.join(self.flow_feats_dir_path, f'{video_clip_name}.pkl')
+            # just a dummy random features acting like a fake interface for no features experiment
+            if self.replace_feats_with_random:
+                flow_feats = np.random.rand(self.feat_len, self.feat_depth//2).astype(np.float32)
+            else:
+                flow_feats = pickle.load(open(flow_path, 'rb'), encoding='bytes')
+            # (T, 2*D)
+            feats = np.concatenate((rgb_feats, flow_feats), axis=1)
+            item['file_path_'] = (rgb_path, flow_path)
+
+        # pad or trim
+        feats_padded = np.zeros((self.feat_len, feats.shape[1]))
+        feats_padded[:feats.shape[0], :] = feats[:self.feat_len, :]
+        item['feature'] = feats_padded
+
+        target = self.video2target[video_name]
+        item['target'] = target
+        item['label'] = self.target2label[target]
+
+        if self.feats_transforms is not None:
+            item = self.feats_transforms(item)
+
+        if self.feat_sampler is not None:
+            item = self.feat_sampler(item)
+
+        return item
+
+    def __len__(self):
+        return len(self.dataset)
+
+class VGGSoundFeatsTrain(VGGSoundFeats):
+    def __init__(self, condition_dataset_cfg):
+        super().__init__('train', **condition_dataset_cfg)
+
+class VGGSoundFeatsValidation(VGGSoundFeats):
+    def __init__(self, condition_dataset_cfg):
+        super().__init__('valid', **condition_dataset_cfg)
+
+class VGGSoundFeatsTest(VGGSoundFeats):
+    def __init__(self, condition_dataset_cfg):
+        super().__init__('test', **condition_dataset_cfg)
+
+
+class VGGSoundSpecsCondOnFeats(torch.utils.data.Dataset):
+
+    def __init__(self, split, specs_dataset_cfg, condition_dataset_cfg):
+        self.specs_dataset_cfg = specs_dataset_cfg
+        self.condition_dataset_cfg = condition_dataset_cfg
+
+        self.specs_dataset = VGGSoundSpecs(split, **specs_dataset_cfg)
+        self.feats_dataset = VGGSoundFeats(split, **condition_dataset_cfg)
+        assert len(self.specs_dataset) == len(self.feats_dataset)
+
+    def __getitem__(self, idx):
+        specs_item = self.specs_dataset[idx]
+        feats_item = self.feats_dataset[idx]
+
+        # sanity check and removing those from one of the dicts
+        for key in ['target', 'label']:
+            assert specs_item[key] == feats_item[key]
+            feats_item.pop(key)
+
+        # keeping both sets of paths to features
+        specs_item['file_path_specs_'] = specs_item.pop('file_path_')
+        feats_item['file_path_feats_'] = feats_item.pop('file_path_')
+
+        # merging both dicts
+        specs_feats_item = dict(**specs_item, **feats_item)
+
+        return specs_feats_item
+
+    def __len__(self):
+        return len(self.specs_dataset)
+
+
+class VGGSoundSpecsCondOnFeatsTrain(VGGSoundSpecsCondOnFeats):
+    def __init__(self, specs_dataset_cfg, condition_dataset_cfg):
+        super().__init__('train', specs_dataset_cfg, condition_dataset_cfg)
+
+class VGGSoundSpecsCondOnFeatsValidation(VGGSoundSpecsCondOnFeats):
+    def __init__(self, specs_dataset_cfg, condition_dataset_cfg):
+        super().__init__('valid', specs_dataset_cfg, condition_dataset_cfg)
+
+class VGGSoundSpecsCondOnFeatsTest(VGGSoundSpecsCondOnFeats):
+    def __init__(self, specs_dataset_cfg, condition_dataset_cfg):
+        super().__init__('test', specs_dataset_cfg, condition_dataset_cfg)
+
+class VGGSoundSpecsCondOnCoords(torch.utils.data.Dataset):
+
+    def __init__(self, split, specs_dataset_cfg, condition_dataset_cfg):
+        self.specs_dataset_cfg = specs_dataset_cfg
+        self.condition_dataset_cfg = condition_dataset_cfg
+
+        self.crop_coord = self.specs_dataset_cfg.crop_coord
+        if self.crop_coord:
+            print('DID YOU EXPECT THAT COORDS ARE CROPPED NOW?')
+            self.F = self.specs_dataset_cfg.mel_num
+            self.T = self.specs_dataset_cfg.spec_len
+            self.T_crop = self.specs_dataset_cfg.spec_crop_len
+            self.transforms = CropCoords([self.F, self.T_crop], self.specs_dataset_cfg.random_crop)
+
+        self.specs_dataset = VGGSoundSpecs(split, **specs_dataset_cfg)
+
+    def __getitem__(self, idx):
+        specs_item = self.specs_dataset[idx]
+        if self.crop_coord:
+            coord = np.arange(self.F * self.T).reshape(self.T, self.F) / (self.T * self.F)
+            coord = coord.T
+            specs_item['coord'] = coord
+            specs_item = self.transforms(specs_item)
+        else:
+            F, T = specs_item['image'].shape
+            coord = np.arange(F * T).reshape(T, F) / (T * F)
+            coord = coord.T
+            specs_item['coord'] = coord
+
+        return specs_item
+
+    def __len__(self):
+        return len(self.specs_dataset)
+
+
+class VGGSoundSpecsCondOnCoordsTrain(VGGSoundSpecsCondOnCoords):
+    def __init__(self, specs_dataset_cfg, condition_dataset_cfg):
+        super().__init__('train', specs_dataset_cfg, condition_dataset_cfg)
+
+class VGGSoundSpecsCondOnCoordsValidation(VGGSoundSpecsCondOnCoords):
+    def __init__(self, specs_dataset_cfg, condition_dataset_cfg):
+        super().__init__('valid', specs_dataset_cfg, condition_dataset_cfg)
+
+class VGGSoundSpecsCondOnCoordsTest(VGGSoundSpecsCondOnCoords):
+    def __init__(self, specs_dataset_cfg, condition_dataset_cfg):
+        super().__init__('test', specs_dataset_cfg, condition_dataset_cfg)
+
+
+class VGGSoundSpecsCondOnClass(torch.utils.data.Dataset):
+
+    def __init__(self, split, specs_dataset_cfg, condition_dataset_cfg):
+        self.specs_dataset_cfg = specs_dataset_cfg
+        # condition_dataset_cfg is not used anywhere else. Kept for compatibility
+        self.condition_dataset_cfg = condition_dataset_cfg
+        self.specs_dataset = VGGSoundSpecs(split, **specs_dataset_cfg)
+
+    def __getitem__(self, idx):
+        specs_item = self.specs_dataset[idx]
+        return specs_item
+
+    def __len__(self):
+        return len(self.specs_dataset)
+
+class VGGSoundSpecsCondOnClassTrain(VGGSoundSpecsCondOnClass):
+    def __init__(self, specs_dataset_cfg, condition_dataset_cfg):
+        super().__init__('train', specs_dataset_cfg, condition_dataset_cfg)
+
+class VGGSoundSpecsCondOnClassValidation(VGGSoundSpecsCondOnClass):
+    def __init__(self, specs_dataset_cfg, condition_dataset_cfg):
+        super().__init__('valid', specs_dataset_cfg, condition_dataset_cfg)
+
+class VGGSoundSpecsCondOnClassTest(VGGSoundSpecsCondOnClass):
+    def __init__(self, specs_dataset_cfg, condition_dataset_cfg):
+        super().__init__('test', specs_dataset_cfg, condition_dataset_cfg)
+
+
+class VGGSoundSpecsCondOnFeatsAndClass(VGGSoundSpecsCondOnFeats):
+
+    def __init__(self, split, specs_dataset_cfg, condition_dataset_cfg):
+        super().__init__(split, specs_dataset_cfg, condition_dataset_cfg)
+
+class VGGSoundSpecsCondOnFeatsAndClassTrain(VGGSoundSpecsCondOnFeatsAndClass):
+    def __init__(self, specs_dataset_cfg, condition_dataset_cfg):
+        super().__init__('train', specs_dataset_cfg, condition_dataset_cfg)
+
+class VGGSoundSpecsCondOnFeatsAndClassValidation(VGGSoundSpecsCondOnFeatsAndClass):
+    def __init__(self, specs_dataset_cfg, condition_dataset_cfg):
+        super().__init__('valid', specs_dataset_cfg, condition_dataset_cfg)
+
+class VGGSoundSpecsCondOnFeatsAndClassTest(VGGSoundSpecsCondOnFeatsAndClass):
+    def __init__(self, specs_dataset_cfg, condition_dataset_cfg):
+        super().__init__('test', specs_dataset_cfg, condition_dataset_cfg)
+
+# class StandardNormalizeFeats(object):
+#     def __init__(self, rgb_feats_dir_path, flow_feats_dir_path, feat_len,
+#                  train_ids_path='./data/vggsound_test.txt', cache_path='./data/'):
+#         self.rgb_feats_dir_path = rgb_feats_dir_path
+#         self.flow_feats_dir_path = flow_feats_dir_path
+#         self.train_ids_path = train_ids_path
+#         self.feat_len = feat_len
+#         # making the stats filename to match the specs dir name
+#         self.cache_path = os.path.join(
+#             cache_path, f'train_means_stds_{Path(rgb_feats_dir_path).stem}.txt'.replace('_rgb', '')
+#         )
+#         logger.info('Assuming that the input stats are calculated using preprocessed spectrograms (log)')
+#         self.train_stats = self.calculate_or_load_stats()
+
+#     def __call__(self, rgb_flow_feats):
+#         return (rgb_flow_feats - self.train_stats['means']) / self.train_stats['stds']
+
+#     def calculate_or_load_stats(self):
+#         try:
+#             # (F, 2)
+#             train_stats = np.loadtxt(self.cache_path)
+#             means, stds = train_stats.T
+#             logger.info('Trying to load train stats for Standard Normalization of inputs')
+#         except OSError:
+#             logger.info('Could not find the precalculated stats for Standard Normalization. Calculating...')
+#             train_vid_ids = open(self.train_ids_path).read().splitlines()
+#             means = [None] * len(train_vid_ids)
+#             stds = [None] * len(train_vid_ids)
+#             for i, vid_id in enumerate(tqdm(train_vid_ids)):
+#                 rgb_path = os.path.join(self.rgb_feats_dir_path, f'{vid_id}.pkl')
+#                 flow_path = os.path.join(self.flow_feats_dir_path, f'{vid_id}.pkl')
+#                 with open(rgb_path, 'rb') as f:
+#                     rgb_feats = pickle.load(f, encoding='bytes')
+#                 with open(flow_path, 'rb') as f:
+#                     flow_feats = pickle.load(f, encoding='bytes')
+#                 # (T, 2*D)
+#                 feats = np.concatenate((rgb_feats, flow_feats), axis=1)
+#                 # pad or trim
+#                 feats_padded = np.zeros((self.feat_len, feats.shape[1]))
+#                 feats_padded[:feats.shape[0], :] = feats[:self.feat_len, :]
+#                 feats = feats_padded
+#                 means[i] = feats.mean(axis=1)
+#                 stds[i] = feats.std(axis=1)
+#             # (F) <- (num_files, D)
+#             means = np.array(means).mean(axis=0)
+#             stds = np.array(stds).mean(axis=0)
+#             # saving in two columns
+#             np.savetxt(self.cache_path, np.vstack([means, stds]).T, fmt='%0.8f')
+#         means = means.reshape(-1, 1)
+#         stds = stds.reshape(-1, 1)
+#         assert 'train' in self.train_ids_path
+#         return {'means': means, 'stds': stds}
+
+
+if __name__ == '__main__':
+    import sys
+
+    from omegaconf import OmegaConf
+
+    # SPECTROGRAMS + FEATS (Subsample)
+    cfg = OmegaConf.load('./configs/vggsound_transformer.yaml')
+    data = instantiate_from_config(cfg.data)
+    data.prepare_data()
+    data.setup()
+    print(len(data.datasets['train']))
+    print(data.datasets['train'][24])
+    print(data.datasets['validation'][24])
+    print(data.datasets['test'][24])
+    print(data.datasets['train'][24]['feature'].shape)
diff --git a/specvqgan/models/cond_transformer.py b/specvqgan/models/cond_transformer.py
new file mode 100644
index 0000000..e6ef5e7
--- /dev/null
+++ b/specvqgan/models/cond_transformer.py
@@ -0,0 +1,420 @@
+import sys
+
+import pytorch_lightning as pl
+import torch
+import torch.nn.functional as F
+from omegaconf.listconfig import ListConfig
+
+sys.path.insert(0, '.')  # nopep8
+from specvqgan.modules.transformer.mingpt import (GPTClass, GPTFeats, GPTFeatsClass)
+from train import instantiate_from_config
+
+
+def disabled_train(self, mode=True):
+    """Overwrite model.train with this function to make sure train/eval mode
+    does not change anymore."""
+    return self
+
+
+class Net2NetTransformer(pl.LightningModule):
+    def __init__(self, transformer_config, first_stage_config,
+                 cond_stage_config,
+                 first_stage_permuter_config=None, cond_stage_permuter_config=None,
+                 ckpt_path=None, ignore_keys=[],
+                 first_stage_key="image",
+                 cond_stage_key="depth",
+                 downsample_cond_size=-1,
+                 pkeep=1.0):
+
+        super().__init__()
+        self.init_first_stage_from_ckpt(first_stage_config)
+        self.init_cond_stage_from_ckpt(cond_stage_config)
+        if first_stage_permuter_config is None:
+            first_stage_permuter_config = {"target": "specvqgan.modules.transformer.permuter.Identity"}
+        if cond_stage_permuter_config is None:
+            cond_stage_permuter_config = {"target": "specvqgan.modules.transformer.permuter.Identity"}
+        self.first_stage_permuter = instantiate_from_config(config=first_stage_permuter_config)
+        self.cond_stage_permuter = instantiate_from_config(config=cond_stage_permuter_config)
+        self.transformer = instantiate_from_config(config=transformer_config)
+
+        if ckpt_path is not None:
+            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
+        self.first_stage_key = first_stage_key
+        self.cond_stage_key = cond_stage_key
+        self.downsample_cond_size = downsample_cond_size
+        self.pkeep = pkeep
+
+    def init_from_ckpt(self, path, ignore_keys=list()):
+        sd = torch.load(path, map_location="cpu")["state_dict"]
+        for k in sd.keys():
+            for ik in ignore_keys:
+                if k.startswith(ik):
+                    self.print("Deleting key {} from state_dict.".format(k))
+                    del sd[k]
+        self.load_state_dict(sd, strict=False)
+        print(f"Restored from {path}")
+
+    def init_first_stage_from_ckpt(self, config):
+        model = instantiate_from_config(config)
+        model = model.eval()
+        model.train = disabled_train
+        self.first_stage_model = model
+
+    def init_cond_stage_from_ckpt(self, config):
+        model = instantiate_from_config(config)
+        model = model.eval()
+        model.train = disabled_train
+        self.cond_stage_model = model
+
+    def forward(self, x, c):
+        # one step to produce the logits
+        _, z_indices = self.encode_to_z(x)
+        _, c_indices = self.encode_to_c(c)
+
+        if self.training and self.pkeep < 1.0:
+            mask = torch.bernoulli(self.pkeep * torch.ones(z_indices.shape, device=z_indices.device))
+            mask = mask.round().to(dtype=torch.int64)
+            r_indices = torch.randint_like(z_indices, self.transformer.config.vocab_size)
+            a_indices = mask*z_indices+(1-mask)*r_indices
+        else:
+            a_indices = z_indices
+
+        # target includes all sequence elements (no need to handle first one
+        # differently because we are conditioning)
+        target = z_indices
+
+        # in the case we do not want to encode condition anyhow (e.g. inputs are features)
+        if isinstance(self.transformer, (GPTFeats, GPTClass, GPTFeatsClass)):
+            # make the prediction
+            logits, _, _ = self.transformer(z_indices[:, :-1], c)
+            # cut off conditioning outputs - output i corresponds to p(z_i | z_{<i}, c)
+            if isinstance(self.transformer, GPTFeatsClass):
+                cond_size = c['feature'].size(-1) + c['target'].size(-1)
+            else:
+                cond_size = c.size(-1)
+            logits = logits[:, cond_size-1:]
+        else:
+            cz_indices = torch.cat((c_indices, a_indices), dim=1)
+            # make the prediction
+            logits, _, _ = self.transformer(cz_indices[:, :-1])
+            # cut off conditioning outputs - output i corresponds to p(z_i | z_{<i}, c)
+            logits = logits[:, c_indices.shape[1]-1:]
+
+        return logits, target
+
+    def top_k_logits(self, logits, k):
+        v, ix = torch.topk(logits, k)
+        out = logits.clone()
+        out[out < v[..., [-1]]] = -float('Inf')
+        return out
+
+    @torch.no_grad()
+    def sample(self, x, c, steps, temperature=1.0, sample=False, top_k=None,
+               callback=lambda k: None):
+        x = x if isinstance(self.transformer, (GPTFeats, GPTClass, GPTFeatsClass)) else torch.cat((c, x), dim=1)
+        block_size = self.transformer.get_block_size()
+        assert not self.transformer.training
+        if self.pkeep <= 0.0:
+            raise NotImplementedError('Implement for GPTFeatsCLass')
+            raise NotImplementedError('Implement for GPTFeats')
+            raise NotImplementedError('Implement for GPTClass')
+            raise NotImplementedError('also the model outputs attention')
+            # one pass suffices since input is pure noise anyway
+            assert len(x.shape)==2
+            # noise_shape = (x.shape[0], steps-1)
+            # noise = torch.randint(self.transformer.config.vocab_size, noise_shape).to(x)
+            noise = c.clone()[:,x.shape[1]-c.shape[1]:-1]
+            x = torch.cat((x,noise),dim=1)
+            logits, _ = self.transformer(x)
+            # take all logits for now and scale by temp
+            logits = logits / temperature
+            # optionally crop probabilities to only the top k options
+            if top_k is not None:
+                logits = self.top_k_logits(logits, top_k)
+            # apply softmax to convert to probabilities
+            probs = F.softmax(logits, dim=-1)
+            # sample from the distribution or take the most likely
+            if sample:
+                shape = probs.shape
+                probs = probs.reshape(shape[0]*shape[1],shape[2])
+                ix = torch.multinomial(probs, num_samples=1)
+                probs = probs.reshape(shape[0],shape[1],shape[2])
+                ix = ix.reshape(shape[0],shape[1])
+            else:
+                _, ix = torch.topk(probs, k=1, dim=-1)
+            # cut off conditioning
+            x = ix[:, c.shape[1]-1:]
+        else:
+            for k in range(steps):
+                callback(k)
+                if isinstance(self.transformer, (GPTFeats, GPTClass, GPTFeatsClass)):
+                    # if assert is removed, you need to make sure that the combined len is not longer block_s
+                    if isinstance(self.transformer, GPTFeatsClass):
+                        cond_size = c['feature'].size(-1) + c['target'].size(-1)
+                    else:
+                        cond_size = c.size(-1)
+                    assert x.size(1) + cond_size <= block_size
+
+                    x_cond = x
+                    c_cond = c
+                    logits, _, att = self.transformer(x_cond, c_cond)
+                else:
+                    assert x.size(1) <= block_size  # make sure model can see conditioning
+                    x_cond = x if x.size(1) <= block_size else x[:, -block_size:]  # crop context if needed
+                    logits, _, att = self.transformer(x_cond)
+                # pluck the logits at the final step and scale by temperature
+                logits = logits[:, -1, :] / temperature
+                # optionally crop probabilities to only the top k options
+                if top_k is not None:
+                    logits = self.top_k_logits(logits, top_k)
+                # apply softmax to convert to probabilities
+                probs = F.softmax(logits, dim=-1)
+                # sample from the distribution or take the most likely
+                if sample:
+                    ix = torch.multinomial(probs, num_samples=1)
+                else:
+                    _, ix = torch.topk(probs, k=1, dim=-1)
+                # append to the sequence and continue
+                x = torch.cat((x, ix), dim=1)
+            # cut off conditioning
+            x = x if isinstance(self.transformer, (GPTFeats, GPTClass, GPTFeatsClass)) else x[:, c.shape[1]:]
+        return x, att.detach().cpu()
+
+    @torch.no_grad()
+    def encode_to_z(self, x):
+        quant_z, _, info = self.first_stage_model.encode(x)
+        indices = info[2].view(quant_z.shape[0], -1)
+        indices = self.first_stage_permuter(indices)
+        return quant_z, indices
+
+    @torch.no_grad()
+    def encode_to_c(self, c):
+        if self.downsample_cond_size > -1:
+            c = F.interpolate(c, size=(self.downsample_cond_size, self.downsample_cond_size))
+        quant_c, _, info = self.cond_stage_model.encode(c)
+        if isinstance(self.transformer, (GPTFeats, GPTClass, GPTFeatsClass)):
+            # these are not indices but raw features or a class
+            indices = info[2]
+        else:
+            indices = info[2].view(quant_c.shape[0], -1)
+            indices = self.cond_stage_permuter(indices)
+        return quant_c, indices
+
+    @torch.no_grad()
+    def decode_to_img(self, index, zshape, stage='first'):
+        if stage == 'first':
+            index = self.first_stage_permuter(index, reverse=True)
+        elif stage == 'cond':
+            print('in cond stage in decode_to_img which is unexpected ')
+            index = self.cond_stage_permuter(index, reverse=True)
+        else:
+            raise NotImplementedError
+
+        bhwc = (zshape[0], zshape[2], zshape[3], zshape[1])
+        quant_z = self.first_stage_model.quantize.get_codebook_entry(index.reshape(-1), shape=bhwc)
+        x = self.first_stage_model.decode(quant_z)
+        return x
+
+    @torch.no_grad()
+    def log_images(self, batch, temperature=None, top_k=None, callback=None, lr_interface=False, **kwargs):
+        log = dict()
+
+        N = 4
+        if lr_interface:
+            x, c = self.get_xc(batch, N, diffuse=False, upsample_factor=8)
+        else:
+            x, c = self.get_xc(batch, N)
+        x = x.to(device=self.device)
+        # c = c.to(device=self.device)
+        if isinstance(c, dict):
+            c = {k: v.to(self.device) for k, v in c.items()}
+        else:
+            c = c.to(self.device)
+
+        quant_z, z_indices = self.encode_to_z(x)
+        quant_c, c_indices = self.encode_to_c(c)  # output can be features or a single class or a featcls dict
+
+        # create a "half"" sample
+        z_start_indices = z_indices[:, :z_indices.shape[1]//2]
+        index_sample, att_half = self.sample(z_start_indices, c_indices,
+                                   steps=z_indices.shape[1]-z_start_indices.shape[1],
+                                   temperature=temperature if temperature is not None else 1.0,
+                                   sample=True,
+                                   top_k=top_k if top_k is not None else 100,
+                                   callback=callback if callback is not None else lambda k: None)
+        x_sample = self.decode_to_img(index_sample, quant_z.shape)
+
+        # sample
+        z_start_indices = z_indices[:, :0]
+        index_sample, att_nopix = self.sample(z_start_indices, c_indices,
+                                              steps=z_indices.shape[1],
+                                              temperature=temperature if temperature is not None else 1.0,
+                                              sample=True,
+                                              top_k=top_k if top_k is not None else 100,
+                                              callback=callback if callback is not None else lambda k: None)
+        x_sample_nopix = self.decode_to_img(index_sample, quant_z.shape)
+
+        # det sample
+        z_start_indices = z_indices[:, :0]
+        index_sample, att_det = self.sample(z_start_indices, c_indices,
+                                            steps=z_indices.shape[1],
+                                            sample=False,
+                                            callback=callback if callback is not None else lambda k: None)
+        x_sample_det = self.decode_to_img(index_sample, quant_z.shape)
+
+        # reconstruction
+        x_rec = self.decode_to_img(z_indices, quant_z.shape)
+
+        log["inputs"] = x
+        log["reconstructions"] = x_rec
+
+        if isinstance(self.cond_stage_key, str):
+            cond_is_not_image = self.cond_stage_key != "image"
+            cond_has_segmentation = self.cond_stage_key == "segmentation"
+        elif isinstance(self.cond_stage_key, ListConfig):
+            cond_is_not_image = 'image' not in self.cond_stage_key
+            cond_has_segmentation = 'segmentation' in self.cond_stage_key
+        else:
+            raise NotImplementedError
+
+        if cond_is_not_image:
+            cond_rec = self.cond_stage_model.decode(quant_c)
+            if cond_has_segmentation:
+                # get image from segmentation mask
+                num_classes = cond_rec.shape[1]
+
+                c = torch.argmax(c, dim=1, keepdim=True)
+                c = F.one_hot(c, num_classes=num_classes)
+                c = c.squeeze(1).permute(0, 3, 1, 2).float()
+                c = self.cond_stage_model.to_rgb(c)
+
+                cond_rec = torch.argmax(cond_rec, dim=1, keepdim=True)
+                cond_rec = F.one_hot(cond_rec, num_classes=num_classes)
+                cond_rec = cond_rec.squeeze(1).permute(0, 3, 1, 2).float()
+                cond_rec = self.cond_stage_model.to_rgb(cond_rec)
+            log["conditioning_rec"] = cond_rec
+            log["conditioning"] = c
+
+        log["samples_half"] = x_sample
+        log["samples_nopix"] = x_sample_nopix
+        log["samples_det"] = x_sample_det
+        log["att_half"] = att_half
+        log["att_nopix"] = att_nopix
+        log["att_det"] = att_det
+        return log
+
+    def get_input(self, key, batch):
+        if isinstance(key, str):
+            # if batch[key] is 1D; else the batch[key] is 2D
+            if key in ['feature', 'target']:
+                x = self.cond_stage_model.get_input(batch, key)
+            else:
+                x = batch[key]
+                if len(x.shape) == 3:
+                    x = x[..., None]
+                x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format)
+            if x.dtype == torch.double:
+                x = x.float()
+        elif isinstance(key, ListConfig):
+            x = self.cond_stage_model.get_input(batch, key)
+            for k, v in x.items():
+                if v.dtype == torch.double:
+                    x[k] = v.float()
+        return x
+
+    def get_xc(self, batch, N=None):
+        x = self.get_input(self.first_stage_key, batch)
+        c = self.get_input(self.cond_stage_key, batch)
+        if N is not None:
+            x = x[:N]
+            if isinstance(self.cond_stage_key, ListConfig):
+                c = {k: v[:N] for k, v in c.items()}
+            else:
+                c = c[:N]
+        return x, c
+
+    def shared_step(self, batch, batch_idx):
+        x, c = self.get_xc(batch)
+        logits, target = self(x, c)
+        loss = F.cross_entropy(logits.reshape(-1, logits.size(-1)), target.reshape(-1))
+        return loss
+
+    def training_step(self, batch, batch_idx):
+        loss = self.shared_step(batch, batch_idx)
+        self.log("train/loss", loss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
+        return loss
+
+    def validation_step(self, batch, batch_idx):
+        loss = self.shared_step(batch, batch_idx)
+        self.log("val/loss", loss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
+        return loss
+
+    def configure_optimizers(self):
+        """
+        Following minGPT:
+        This long function is unfortunately doing something very simple and is being very defensive:
+        We are separating out all parameters of the model into two buckets: those that will experience
+        weight decay for regularization and those that won't (biases, and layernorm/embedding weights).
+        We are then returning the PyTorch optimizer object.
+        """
+        # separate out all parameters to those that will and won't experience regularizing weight decay
+        decay = set()
+        no_decay = set()
+        whitelist_weight_modules = (torch.nn.Linear, )
+
+        blacklist_weight_modules = (torch.nn.LayerNorm, torch.nn.Embedding, torch.nn.Conv1d, torch.nn.LSTM, torch.nn.GRU)
+        for mn, m in self.transformer.named_modules():
+            for pn, p in m.named_parameters():
+                fpn = '%s.%s' % (mn, pn) if mn else pn # full param name
+
+                if pn.endswith('bias'):
+                    # all biases will not be decayed
+                    no_decay.add(fpn)
+                elif pn.endswith('weight') and isinstance(m, whitelist_weight_modules):
+                    # weights of whitelist modules will be weight decayed
+                    decay.add(fpn)
+                elif pn.endswith('weight') and isinstance(m, blacklist_weight_modules):
+                    # weights of blacklist modules will NOT be weight decayed
+                    no_decay.add(fpn)
+                elif ('weight' in pn or 'bias' in pn) and isinstance(m, (torch.nn.LSTM, torch.nn.GRU)):
+                    no_decay.add(fpn)
+
+        # special case the position embedding parameter in the root GPT module as not decayed
+        no_decay.add('pos_emb')
+
+        # validate that we considered every parameter
+        param_dict = {pn: p for pn, p in self.transformer.named_parameters()}
+        inter_params = decay & no_decay
+        union_params = decay | no_decay
+        assert len(inter_params) == 0, "parameters %s made it into both decay/no_decay sets!" % (str(inter_params), )
+        assert len(param_dict.keys() - union_params) == 0, "parameters %s were not separated into either decay/no_decay set!" \
+                                                    % (str(param_dict.keys() - union_params), )
+
+        # create the pytorch optimizer object
+        optim_groups = [
+            {"params": [param_dict[pn] for pn in sorted(list(decay))], "weight_decay": 0.01},
+            {"params": [param_dict[pn] for pn in sorted(list(no_decay))], "weight_decay": 0.0},
+        ]
+        optimizer = torch.optim.AdamW(optim_groups, lr=self.learning_rate, betas=(0.9, 0.95))
+        return optimizer
+
+
+if __name__ == '__main__':
+    from omegaconf import OmegaConf
+
+    cfg_image = OmegaConf.load('./configs/vggsound_transformer.yaml')
+    cfg_image.model.params.first_stage_config.params.ckpt_path = './logs/2021-05-19T22-16-54_vggsound_specs_vqgan/checkpoints/epoch_39.ckpt'
+
+    transformer_cfg = cfg_image.model.params.transformer_config
+    first_stage_cfg = cfg_image.model.params.first_stage_config
+    cond_stage_cfg = cfg_image.model.params.cond_stage_config
+    permuter_cfg = cfg_image.model.params.permuter_config
+    transformer = Net2NetTransformer(
+        transformer_cfg, first_stage_cfg, cond_stage_cfg, permuter_cfg
+    )
+
+    c = torch.rand(2, 2048, 212)
+    x = torch.rand(2, 1, 80, 848)
+
+    logits, target = transformer(x, c)
+    print(logits.shape, target.shape)
diff --git a/specvqgan/models/vqgan.py b/specvqgan/models/vqgan.py
new file mode 100644
index 0000000..6313b03
--- /dev/null
+++ b/specvqgan/models/vqgan.py
@@ -0,0 +1,349 @@
+import torch
+import torch.nn.functional as F
+import pytorch_lightning as pl
+
+import sys
+sys.path.insert(0, '.')  # nopep8
+from train import instantiate_from_config
+
+from specvqgan.modules.diffusionmodules.model import Encoder, Decoder, Encoder1d, Decoder1d
+from specvqgan.modules.vqvae.quantize import VectorQuantizer, VectorQuantizer1d
+
+
+class VQModel(pl.LightningModule):
+    def __init__(self,
+                 ddconfig,
+                 lossconfig,
+                 n_embed,
+                 embed_dim,
+                 ckpt_path=None,
+                 ignore_keys=[],
+                 image_key="image",
+                 colorize_nlabels=None,
+                 monitor=None
+                 ):
+        super().__init__()
+        self.image_key = image_key
+        # we need this one for compatibility in train.ImageLogger.log_img if statement
+        self.first_stage_key = image_key
+        self.encoder = Encoder(**ddconfig)
+        self.decoder = Decoder(**ddconfig)
+        self.loss = instantiate_from_config(lossconfig)
+        self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25)
+        self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1)
+        self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
+        if ckpt_path is not None:
+            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
+        if colorize_nlabels is not None:
+            assert type(colorize_nlabels)==int
+            self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
+        if monitor is not None:
+            self.monitor = monitor
+        self.used_codes = []
+        self.counts = [0 for _ in range(self.quantize.n_e)]
+
+    def init_from_ckpt(self, path, ignore_keys=list()):
+        sd = torch.load(path, map_location="cpu")["state_dict"]
+        keys = list(sd.keys())
+        for k in keys:
+            for ik in ignore_keys:
+                if k.startswith(ik):
+                    print("Deleting key {} from state_dict.".format(k))
+                    del sd[k]
+        self.load_state_dict(sd, strict=False)
+        print(f"Restored from {path}")
+
+    def encode(self, x):
+        h = self.encoder(x)  # 2d: (B, 256, 16, 16) <- (B, 3, 256, 256)
+        h = self.quant_conv(h)  # 2d: (B, 256, 16, 16)
+        quant, emb_loss, info = self.quantize(h)  # (B, 256, 16, 16), (), ((), (768, 1024), (768, 1))
+        if not self.training:
+            self.counts = [info[2].squeeze().tolist().count(i) + self.counts[i] for i in range(self.quantize.n_e)]
+        return quant, emb_loss, info
+
+    def decode(self, quant):
+        quant = self.post_quant_conv(quant)
+        dec = self.decoder(quant)
+        return dec
+
+    def decode_code(self, code_b):
+        quant_b = self.quantize.embed_code(code_b)
+        dec = self.decode(quant_b)
+        return dec
+
+    def forward(self, input):
+        quant, diff, _ = self.encode(input)
+        dec = self.decode(quant)
+        return dec, diff
+
+    def get_input(self, batch, k):
+        x = batch[k]
+        if len(x.shape) == 3:
+            x = x[..., None]
+        x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format)
+        return x.float()
+
+    def training_step(self, batch, batch_idx, optimizer_idx):
+        x = self.get_input(batch, self.image_key)
+        xrec, qloss = self(x)
+
+        if optimizer_idx == 0:
+            # autoencode
+            aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
+                                            last_layer=self.get_last_layer(), split="train")
+
+            self.log("train/aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
+            self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
+            return aeloss
+
+        if optimizer_idx == 1:
+            # discriminator
+            discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
+                                            last_layer=self.get_last_layer(), split="train")
+            self.log("train/disc_loss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
+            self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True)
+            return discloss
+
+    def validation_step(self, batch, batch_idx):
+        if batch_idx == 0 and self.global_step != 0 and sum(self.counts) > 0:
+            print(f'Previous Epoch counts: {self.counts}')
+            zero_hit_codes = len([1 for count in self.counts if count == 0])
+            used_codes = []
+            for c, count in enumerate(self.counts):
+                used_codes.extend([c] * count)
+            self.logger.experiment.add_histogram('val/code_hits', torch.tensor(used_codes), self.global_step)
+            self.logger.experiment.add_scalar('val/zero_hit_codes', zero_hit_codes, self.global_step)
+            self.counts = [0 for _ in range(self.quantize.n_e)]
+        x = self.get_input(batch, self.image_key)
+        xrec, qloss = self(x)
+        aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0, self.global_step,
+                                        last_layer=self.get_last_layer(), split="val")
+
+        discloss, log_dict_disc = self.loss(qloss, x, xrec, 1, self.global_step,
+                                            last_layer=self.get_last_layer(), split="val")
+        rec_loss = log_dict_ae['val/rec_loss']
+        self.log('val/rec_loss', rec_loss, prog_bar=True, logger=True, on_step=True, on_epoch=True, sync_dist=True)
+        self.log('val/aeloss', aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True, sync_dist=True)
+        self.log_dict(log_dict_ae)
+        self.log_dict(log_dict_disc)
+        return self.log_dict
+
+    def configure_optimizers(self):
+        lr = self.learning_rate
+        opt_ae = torch.optim.Adam(list(self.encoder.parameters()) +
+                                  list(self.decoder.parameters()) +
+                                  list(self.quantize.parameters()) +
+                                  list(self.quant_conv.parameters()) +
+                                  list(self.post_quant_conv.parameters()),
+                                  lr=lr, betas=(0.5, 0.9))
+        opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
+                                    lr=lr, betas=(0.5, 0.9))
+        return [opt_ae, opt_disc], []
+
+    def get_last_layer(self):
+        return self.decoder.conv_out.weight
+
+    def log_images(self, batch, **kwargs):
+        log = dict()
+        x = self.get_input(batch, self.image_key)
+        x = x.to(self.device)
+        xrec, _ = self(x)
+        if x.shape[1] > 3:
+            # colorize with random projection
+            assert xrec.shape[1] > 3
+            x = self.to_rgb(x)
+            xrec = self.to_rgb(xrec)
+        log["inputs"] = x
+        log["reconstructions"] = xrec
+        return log
+
+    def to_rgb(self, x):
+        assert self.image_key == "segmentation"
+        if not hasattr(self, "colorize"):
+            self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
+        x = F.conv2d(x, weight=self.colorize)
+        x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
+        return x
+
+
+class VQModel1d(VQModel):
+    def __init__(self, ddconfig, lossconfig, n_embed, embed_dim, ckpt_path=None, ignore_keys=[],
+                 image_key='feature', colorize_nlabels=None, monitor=None):
+        # ckpt_path is none to super because otherwise will try to load 1D checkpoint into 2D model
+        super().__init__(ddconfig, lossconfig, n_embed, embed_dim)
+        self.image_key = image_key
+        # we need this one for compatibility in train.ImageLogger.log_img if statement
+        self.first_stage_key = image_key
+        self.encoder = Encoder1d(**ddconfig)
+        self.decoder = Decoder1d(**ddconfig)
+        self.loss = instantiate_from_config(lossconfig)
+        self.quantize = VectorQuantizer1d(n_embed, embed_dim, beta=0.25)
+        self.quant_conv = torch.nn.Conv1d(ddconfig['z_channels'], embed_dim, 1)
+        self.post_quant_conv = torch.nn.Conv1d(embed_dim, ddconfig['z_channels'], 1)
+        if ckpt_path is not None:
+            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
+        if colorize_nlabels is not None:
+            assert type(colorize_nlabels)==int
+            self.register_buffer('colorize', torch.randn(3, colorize_nlabels, 1, 1))
+        if monitor is not None:
+            self.monitor = monitor
+
+    def get_input(self, batch, k):
+        x = batch[k]
+        if self.image_key == 'feature':
+            x = x.permute(0, 2, 1)
+        elif self.image_key == 'image':
+            x = x.unsqueeze(1)
+        x = x.to(memory_format=torch.contiguous_format)
+        return x.float()
+
+    def forward(self, input):
+        if self.image_key == 'image':
+            input = input.squeeze(1)
+        quant, diff, _ = self.encode(input)
+        dec = self.decode(quant)
+        if self.image_key == 'image':
+            dec = dec.unsqueeze(1)
+        return dec, diff
+
+    def log_images(self, batch, **kwargs):
+        if self.image_key == 'image':
+            log = dict()
+            x = self.get_input(batch, self.image_key)
+            x = x.to(self.device)
+            xrec, _ = self(x)
+            if x.shape[1] > 3:
+                # colorize with random projection
+                assert xrec.shape[1] > 3
+                x = self.to_rgb(x)
+                xrec = self.to_rgb(xrec)
+            log['inputs'] = x
+            log['reconstructions'] = xrec
+            return log
+        else:
+            raise NotImplementedError('1d input should be treated differently')
+
+    def to_rgb(self, batch, **kwargs):
+        raise NotImplementedError('1d input should be treated differently')
+
+
+class VQSegmentationModel(VQModel):
+    def __init__(self, n_labels, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.register_buffer("colorize", torch.randn(3, n_labels, 1, 1))
+
+    def configure_optimizers(self):
+        lr = self.learning_rate
+        opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
+                                  list(self.decoder.parameters())+
+                                  list(self.quantize.parameters())+
+                                  list(self.quant_conv.parameters())+
+                                  list(self.post_quant_conv.parameters()),
+                                  lr=lr, betas=(0.5, 0.9))
+        return opt_ae
+
+    def training_step(self, batch, batch_idx):
+        x = self.get_input(batch, self.image_key)
+        xrec, qloss = self(x)
+        aeloss, log_dict_ae = self.loss(qloss, x, xrec, split="train")
+        self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
+        return aeloss
+
+    def validation_step(self, batch, batch_idx):
+        x = self.get_input(batch, self.image_key)
+        xrec, qloss = self(x)
+        aeloss, log_dict_ae = self.loss(qloss, x, xrec, split="val")
+        self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
+        total_loss = log_dict_ae["val/total_loss"]
+        self.log("val/total_loss", total_loss,
+                 prog_bar=True, logger=True, on_step=True, on_epoch=True, sync_dist=True)
+        return aeloss
+
+    @torch.no_grad()
+    def log_images(self, batch, **kwargs):
+        log = dict()
+        x = self.get_input(batch, self.image_key)
+        x = x.to(self.device)
+        xrec, _ = self(x)
+        if x.shape[1] > 3:
+            # colorize with random projection
+            assert xrec.shape[1] > 3
+            # convert logits to indices
+            xrec = torch.argmax(xrec, dim=1, keepdim=True)
+            xrec = F.one_hot(xrec, num_classes=x.shape[1])
+            xrec = xrec.squeeze(1).permute(0, 3, 1, 2).float()
+            x = self.to_rgb(x)
+            xrec = self.to_rgb(xrec)
+        log["inputs"] = x
+        log["reconstructions"] = xrec
+        return log
+
+
+class VQNoDiscModel(VQModel):
+    def __init__(self,
+                 ddconfig,
+                 lossconfig,
+                 n_embed,
+                 embed_dim,
+                 ckpt_path=None,
+                 ignore_keys=[],
+                 image_key="image",
+                 colorize_nlabels=None
+                 ):
+        super().__init__(ddconfig=ddconfig, lossconfig=lossconfig, n_embed=n_embed, embed_dim=embed_dim,
+                         ckpt_path=ckpt_path, ignore_keys=ignore_keys, image_key=image_key,
+                         colorize_nlabels=colorize_nlabels)
+
+    def training_step(self, batch, batch_idx):
+        x = self.get_input(batch, self.image_key)
+        xrec, qloss = self(x)
+        # autoencode
+        aeloss, log_dict_ae = self.loss(qloss, x, xrec, self.global_step, split="train")
+        output = pl.TrainResult(minimize=aeloss)
+        output.log("train/aeloss", aeloss,
+                   prog_bar=True, logger=True, on_step=True, on_epoch=True)
+        output.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
+        return output
+
+    def validation_step(self, batch, batch_idx):
+        x = self.get_input(batch, self.image_key)
+        xrec, qloss = self(x)
+        aeloss, log_dict_ae = self.loss(qloss, x, xrec, self.global_step, split="val")
+        rec_loss = log_dict_ae["val/rec_loss"]
+        output = pl.EvalResult(checkpoint_on=rec_loss)
+        output.log("val/rec_loss", rec_loss,
+                   prog_bar=True, logger=True, on_step=True, on_epoch=True)
+        output.log("val/aeloss", aeloss,
+                   prog_bar=True, logger=True, on_step=True, on_epoch=True)
+        output.log_dict(log_dict_ae)
+
+        return output
+
+    def configure_optimizers(self):
+        optimizer = torch.optim.Adam(list(self.encoder.parameters()) +
+                                     list(self.decoder.parameters()) +
+                                     list(self.quantize.parameters()) +
+                                     list(self.quant_conv.parameters()) +
+                                     list(self.post_quant_conv.parameters()),
+                                     lr=self.learning_rate, betas=(0.5, 0.9))
+        return optimizer
+
+
+if __name__ == '__main__':
+    from omegaconf import OmegaConf
+    from train import instantiate_from_config
+
+    image_key = 'image'
+    cfg_audio = OmegaConf.load('./configs/vggsound_codebook.yaml')
+    model = VQModel(cfg_audio.model.params.ddconfig,
+                    cfg_audio.model.params.lossconfig,
+                    cfg_audio.model.params.n_embed,
+                    cfg_audio.model.params.embed_dim,
+                    image_key='image')
+    batch = {
+        'image': torch.rand((4, 80, 848)),
+        'file_path_': ['data/vggsound/mel123.npy', 'data/vggsound/mel123.npy', 'data/vggsound/mel123.npy'],
+        'class': [1, 1, 1],
+    }
+    xrec, qloss = model(model.get_input(batch, image_key))
+    print(xrec.shape, qloss.shape)
diff --git a/specvqgan/modules/autoencoder/lpaps/train_means_stds_melspec_10s_22050hz.txt b/specvqgan/modules/autoencoder/lpaps/train_means_stds_melspec_10s_22050hz.txt
new file mode 100644
index 0000000..5338cad
--- /dev/null
+++ b/specvqgan/modules/autoencoder/lpaps/train_means_stds_melspec_10s_22050hz.txt
@@ -0,0 +1,80 @@
+0.51234712 0.08187601
+0.52630101 0.08393201
+0.52002938 0.08533191
+0.51831866 0.08651366
+0.52457265 0.08795700
+0.51129235 0.08924046
+0.51403755 0.09011565
+0.51189406 0.09138965
+0.50142221 0.09215379
+0.50632402 0.09251092
+0.49724399 0.09356590
+0.49062130 0.09333057
+0.49971113 0.09446411
+0.48442903 0.09400197
+0.48598301 0.09477853
+0.48681630 0.09487848
+0.47436119 0.09424447
+0.48031359 0.09417475
+0.47422810 0.09498061
+0.46369397 0.09362415
+0.47413055 0.09453825
+0.46062686 0.09481226
+0.45677793 0.09359464
+0.46135474 0.09437913
+0.45246800 0.09384014
+0.45232766 0.09438247
+0.45208386 0.09419720
+0.44351671 0.09340880
+0.44667316 0.09423184
+0.44447692 0.09344461
+0.43676363 0.09265266
+0.44002381 0.09307925
+0.43772414 0.09299363
+0.43061019 0.09236166
+0.43120828 0.09156491
+0.42603271 0.09161403
+0.42863234 0.09150530
+0.42296206 0.09102536
+0.41733331 0.09048366
+0.41804121 0.09025013
+0.41605068 0.09078869
+0.40875265 0.08985338
+0.40666997 0.08877566
+0.40407463 0.08961667
+0.40353311 0.08859275
+0.39708031 0.08827818
+0.39375066 0.08833999
+0.39301091 0.08760654
+0.39047117 0.08812327
+0.38461680 0.08782288
+0.38145284 0.08645484
+0.37985209 0.08718211
+0.37419526 0.08644421
+0.37080597 0.08532454
+0.36786535 0.08592822
+0.36569049 0.08452069
+0.36336079 0.08474272
+0.35775191 0.08476392
+0.35504801 0.08334654
+0.35284816 0.08412110
+0.34594865 0.08367254
+0.34112312 0.08252251
+0.33784886 0.08320975
+0.33095703 0.08257768
+0.32559461 0.08171253
+0.32003106 0.08204872
+0.31506222 0.08098545
+0.31138077 0.08152917
+0.30403516 0.08209135
+0.29969540 0.08073266
+0.29578024 0.08225822
+0.28861871 0.08324076
+0.28581686 0.08058489
+0.27922253 0.08515350
+0.27444035 0.08355056
+0.27339468 0.08067638
+0.26571759 0.08536921
+0.26280864 0.08107620
+0.25664202 0.08357468
+0.24853513 0.08556041
diff --git a/specvqgan/modules/diffusionmodules/model.py b/specvqgan/modules/diffusionmodules/model.py
new file mode 100644
index 0000000..9a1ceb0
--- /dev/null
+++ b/specvqgan/modules/diffusionmodules/model.py
@@ -0,0 +1,999 @@
+# pytorch_diffusion + derived encoder decoder
+import math
+import torch
+import torch.nn as nn
+import numpy as np
+
+
+def get_timestep_embedding(timesteps, embedding_dim):
+    """
+    This matches the implementation in Denoising Diffusion Probabilistic Models:
+    From Fairseq.
+    Build sinusoidal embeddings.
+    This matches the implementation in tensor2tensor, but differs slightly
+    from the description in Section 3.5 of "Attention Is All You Need".
+    """
+    assert len(timesteps.shape) == 1
+
+    half_dim = embedding_dim // 2
+    emb = math.log(10000) / (half_dim - 1)
+    emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
+    emb = emb.to(device=timesteps.device)
+    emb = timesteps.float()[:, None] * emb[None, :]
+    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
+    if embedding_dim % 2 == 1:  # zero pad
+        emb = torch.nn.functional.pad(emb, (0,1,0,0))
+    return emb
+
+
+def nonlinearity(x):
+    # swish
+    return x*torch.sigmoid(x)
+
+
+def Normalize(in_channels):
+    return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
+
+class Upsample(nn.Module):
+    def __init__(self, in_channels, with_conv):
+        super().__init__()
+        self.with_conv = with_conv
+        if self.with_conv:
+            self.conv = torch.nn.Conv2d(in_channels,
+                                        in_channels,
+                                        kernel_size=3,
+                                        stride=1,
+                                        padding=1)
+
+    def forward(self, x):
+        x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
+        if self.with_conv:
+            x = self.conv(x)
+        return x
+
+class Upsample1d(Upsample):
+    def __init__(self, in_channels, with_conv):
+        super().__init__(in_channels, with_conv)
+        if self.with_conv:
+            self.conv = torch.nn.Conv1d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
+
+class Downsample(nn.Module):
+    def __init__(self, in_channels, with_conv):
+        super().__init__()
+        self.with_conv = with_conv
+        if self.with_conv:
+            # no asymmetric padding in torch conv, must do it ourselves
+            self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0)
+            self.pad = (0, 1, 0, 1)
+        else:
+            self.avg_pool = nn.AvgPool2d(kernel_size=2, stride=2)
+
+    def forward(self, x):
+        if self.with_conv:  # bp: check self.avgpool and self.pad
+            x = torch.nn.functional.pad(x, self.pad, mode="constant", value=0)
+            x = self.conv(x)
+        else:
+            x = self.avg_pool(x)
+        return x
+
+class Downsample1d(Downsample):
+
+    def __init__(self, in_channels, with_conv):
+        super().__init__(in_channels, with_conv)
+        if self.with_conv:
+            # no asymmetric padding in torch conv, must do it ourselves
+            # TODO: can we replace it just with conv2d with padding 1?
+            self.conv = torch.nn.Conv1d(in_channels, in_channels, kernel_size=3, stride=2, padding=0)
+            self.pad = (1, 1)
+        else:
+            self.avg_pool = nn.AvgPool1d(kernel_size=2, stride=2)
+
+
+class ResnetBlock(nn.Module):
+    def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
+                 dropout, temb_channels=512):
+        super().__init__()
+        self.in_channels = in_channels
+        out_channels = in_channels if out_channels is None else out_channels
+        self.out_channels = out_channels
+        self.use_conv_shortcut = conv_shortcut
+
+        self.norm1 = Normalize(in_channels)
+        self.conv1 = torch.nn.Conv2d(in_channels,
+                                     out_channels,
+                                     kernel_size=3,
+                                     stride=1,
+                                     padding=1)
+        if temb_channels > 0:
+            self.temb_proj = torch.nn.Linear(temb_channels,
+                                             out_channels)
+        self.norm2 = Normalize(out_channels)
+        self.dropout = torch.nn.Dropout(dropout)
+        self.conv2 = torch.nn.Conv2d(out_channels,
+                                     out_channels,
+                                     kernel_size=3,
+                                     stride=1,
+                                     padding=1)
+        if self.in_channels != self.out_channels:
+            if self.use_conv_shortcut:
+                self.conv_shortcut = torch.nn.Conv2d(in_channels,
+                                                     out_channels,
+                                                     kernel_size=3,
+                                                     stride=1,
+                                                     padding=1)
+            else:
+                self.nin_shortcut = torch.nn.Conv2d(in_channels,
+                                                    out_channels,
+                                                    kernel_size=1,
+                                                    stride=1,
+                                                    padding=0)
+
+    def forward(self, x, temb):
+        h = x
+        h = self.norm1(h)
+        h = nonlinearity(h)
+        h = self.conv1(h)
+
+        if temb is not None:
+            h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]
+
+        h = self.norm2(h)
+        h = nonlinearity(h)
+        h = self.dropout(h)
+        h = self.conv2(h)
+
+        if self.in_channels != self.out_channels:
+            if self.use_conv_shortcut:
+                x = self.conv_shortcut(x)
+            else:
+                x = self.nin_shortcut(x)
+
+        return x+h
+
+class ResnetBlock1d(ResnetBlock):
+    def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
+                 dropout, temb_channels=512):
+        super().__init__(in_channels=in_channels, out_channels=out_channels,
+                         conv_shortcut=conv_shortcut, dropout=dropout, temb_channels=temb_channels)
+        # redefining different elements (forward is goint to be the same as in RenetBlock)
+        if temb_channels > 0:
+            raise NotImplementedError('go to ResnetBlock and figure out how to deal with it in forward')
+            self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
+
+        self.conv1 = torch.nn.Conv1d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
+        self.conv2 = torch.nn.Conv1d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
+        if self.in_channels != self.out_channels:
+            if self.use_conv_shortcut:
+                self.conv_shortcut = torch.nn.Conv1d(in_channels, out_channels, kernel_size=3,
+                                                     stride=1, padding=1)
+            else:
+                self.nin_shortcut = torch.nn.Conv1d(in_channels, out_channels, kernel_size=1,
+                                                    stride=1, padding=0)
+
+
+class AttnBlock(nn.Module):
+    def __init__(self, in_channels):
+        super().__init__()
+        self.in_channels = in_channels
+
+        self.norm = Normalize(in_channels)
+        self.q = torch.nn.Conv2d(in_channels,
+                                 in_channels,
+                                 kernel_size=1,
+                                 stride=1,
+                                 padding=0)
+        self.k = torch.nn.Conv2d(in_channels,
+                                 in_channels,
+                                 kernel_size=1,
+                                 stride=1,
+                                 padding=0)
+        self.v = torch.nn.Conv2d(in_channels,
+                                 in_channels,
+                                 kernel_size=1,
+                                 stride=1,
+                                 padding=0)
+        self.proj_out = torch.nn.Conv2d(in_channels,
+                                        in_channels,
+                                        kernel_size=1,
+                                        stride=1,
+                                        padding=0)
+
+
+    def forward(self, x):
+        h_ = x
+        h_ = self.norm(h_)
+        q = self.q(h_)
+        k = self.k(h_)
+        v = self.v(h_)
+
+        # compute attention
+        b,c,h,w = q.shape
+        q = q.reshape(b,c,h*w)
+        q = q.permute(0,2,1)   # b,hw,c
+        k = k.reshape(b,c,h*w) # b,c,hw
+        w_ = torch.bmm(q,k)     # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
+        w_ = w_ * (int(c)**(-0.5))
+        w_ = torch.nn.functional.softmax(w_, dim=2)
+
+        # attend to values
+        v = v.reshape(b,c,h*w)
+        w_ = w_.permute(0,2,1)   # b,hw,hw (first hw of k, second of q)
+        h_ = torch.bmm(v,w_)     # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
+        h_ = h_.reshape(b,c,h,w)
+
+        h_ = self.proj_out(h_)
+
+        return x+h_
+
+class AttnBlock1d(nn.Module):
+
+    def __init__(self, in_channels):
+        super().__init__()
+        self.in_channels = in_channels
+
+        self.norm = Normalize(in_channels)
+        self.q = torch.nn.Conv1d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
+        self.k = torch.nn.Conv1d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
+        self.v = torch.nn.Conv1d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
+        self.proj_out = torch.nn.Conv1d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
+
+    def forward(self, x):
+        h_ = x
+        h_ = self.norm(h_)
+        q = self.q(h_)
+        k = self.k(h_)
+        v = self.v(h_)
+
+        # compute attention
+        b, c, t = q.shape
+        q = q.permute(0, 2, 1)   # b,t,c
+        w_ = torch.bmm(q, k)     # b,t,t    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
+        w_ = w_ * (int(c) ** (-0.5))
+        w_ = torch.nn.functional.softmax(w_, dim=2)
+
+        # attend to values
+        w_ = w_.permute(0, 2, 1)  # b,t,t (first t of k, second of q)
+        h_ = torch.bmm(v, w_)  # b,c,t (t of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
+
+        h_ = self.proj_out(h_)
+
+        return x + h_
+
+
+class Model(nn.Module):
+    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
+                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
+                 resolution, use_timestep=True):
+        super().__init__()
+        self.ch = ch
+        self.temb_ch = self.ch*4
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.resolution = resolution
+        self.in_channels = in_channels
+
+        self.use_timestep = use_timestep
+        if self.use_timestep:
+            # timestep embedding
+            self.temb = nn.Module()
+            self.temb.dense = nn.ModuleList([
+                torch.nn.Linear(self.ch,
+                                self.temb_ch),
+                torch.nn.Linear(self.temb_ch,
+                                self.temb_ch),
+            ])
+
+        # downsampling
+        self.conv_in = torch.nn.Conv2d(in_channels,
+                                       self.ch,
+                                       kernel_size=3,
+                                       stride=1,
+                                       padding=1)
+
+        curr_res = resolution
+        in_ch_mult = (1,)+tuple(ch_mult)
+        self.down = nn.ModuleList()
+        for i_level in range(self.num_resolutions):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_in = ch*in_ch_mult[i_level]
+            block_out = ch*ch_mult[i_level]
+            for i_block in range(self.num_res_blocks):
+                block.append(ResnetBlock(in_channels=block_in,
+                                         out_channels=block_out,
+                                         temb_channels=self.temb_ch,
+                                         dropout=dropout))
+                block_in = block_out
+                if curr_res in attn_resolutions:
+                    attn.append(AttnBlock(block_in))
+            down = nn.Module()
+            down.block = block
+            down.attn = attn
+            if i_level != self.num_resolutions-1:
+                down.downsample = Downsample(block_in, resamp_with_conv)
+                curr_res = curr_res // 2
+            self.down.append(down)
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = ResnetBlock(in_channels=block_in,
+                                       out_channels=block_in,
+                                       temb_channels=self.temb_ch,
+                                       dropout=dropout)
+        self.mid.attn_1 = AttnBlock(block_in)
+        self.mid.block_2 = ResnetBlock(in_channels=block_in,
+                                       out_channels=block_in,
+                                       temb_channels=self.temb_ch,
+                                       dropout=dropout)
+
+        # upsampling
+        self.up = nn.ModuleList()
+        for i_level in reversed(range(self.num_resolutions)):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_out = ch*ch_mult[i_level]
+            skip_in = ch*ch_mult[i_level]
+            for i_block in range(self.num_res_blocks+1):
+                if i_block == self.num_res_blocks:
+                    skip_in = ch*in_ch_mult[i_level]
+                block.append(ResnetBlock(in_channels=block_in+skip_in,
+                                         out_channels=block_out,
+                                         temb_channels=self.temb_ch,
+                                         dropout=dropout))
+                block_in = block_out
+                if curr_res in attn_resolutions:
+                    attn.append(AttnBlock(block_in))
+            up = nn.Module()
+            up.block = block
+            up.attn = attn
+            if i_level != 0:
+                up.upsample = Upsample(block_in, resamp_with_conv)
+                curr_res = curr_res * 2
+            self.up.insert(0, up) # prepend to get consistent order
+
+        # end
+        self.norm_out = Normalize(block_in)
+        self.conv_out = torch.nn.Conv2d(block_in,
+                                        out_ch,
+                                        kernel_size=3,
+                                        stride=1,
+                                        padding=1)
+
+
+    def forward(self, x, t=None):
+        #assert x.shape[2] == x.shape[3] == self.resolution
+
+        if self.use_timestep:
+            # timestep embedding
+            assert t is not None
+            temb = get_timestep_embedding(t, self.ch)
+            temb = self.temb.dense[0](temb)
+            temb = nonlinearity(temb)
+            temb = self.temb.dense[1](temb)
+        else:
+            temb = None
+
+        # downsampling
+        hs = [self.conv_in(x)]
+        for i_level in range(self.num_resolutions):
+            for i_block in range(self.num_res_blocks):
+                h = self.down[i_level].block[i_block](hs[-1], temb)
+                if len(self.down[i_level].attn) > 0:
+                    h = self.down[i_level].attn[i_block](h)
+                hs.append(h)
+            if i_level != self.num_resolutions-1:
+                hs.append(self.down[i_level].downsample(hs[-1]))
+
+        # middle
+        h = hs[-1]
+        h = self.mid.block_1(h, temb)
+        h = self.mid.attn_1(h)
+        h = self.mid.block_2(h, temb)
+
+        # upsampling
+        for i_level in reversed(range(self.num_resolutions)):
+            for i_block in range(self.num_res_blocks+1):
+                h = self.up[i_level].block[i_block](
+                    torch.cat([h, hs.pop()], dim=1), temb)
+                if len(self.up[i_level].attn) > 0:
+                    h = self.up[i_level].attn[i_block](h)
+            if i_level != 0:
+                h = self.up[i_level].upsample(h)
+
+        # end
+        h = self.norm_out(h)
+        h = nonlinearity(h)
+        h = self.conv_out(h)
+        return h
+
+
+class Encoder(nn.Module):
+    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
+                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
+                 resolution, z_channels, double_z=True, **ignore_kwargs):
+        super().__init__()
+        self.ch = ch
+        self.temb_ch = 0
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.resolution = resolution
+        self.in_channels = in_channels
+
+        # downsampling
+        self.conv_in = torch.nn.Conv2d(in_channels,
+                                       self.ch,
+                                       kernel_size=3,
+                                       stride=1,
+                                       padding=1)
+
+        curr_res = resolution
+        in_ch_mult = (1,)+tuple(ch_mult)
+        self.down = nn.ModuleList()
+        for i_level in range(self.num_resolutions):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_in = ch*in_ch_mult[i_level]
+            block_out = ch*ch_mult[i_level]
+            for i_block in range(self.num_res_blocks):
+                block.append(ResnetBlock(in_channels=block_in,
+                                         out_channels=block_out,
+                                         temb_channels=self.temb_ch,
+                                         dropout=dropout))
+                block_in = block_out
+                if curr_res in attn_resolutions:
+                    attn.append(AttnBlock(block_in))
+            down = nn.Module()
+            down.block = block
+            down.attn = attn
+            if i_level != self.num_resolutions-1:
+                down.downsample = Downsample(block_in, resamp_with_conv)
+                curr_res = curr_res // 2
+            self.down.append(down)
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = ResnetBlock(in_channels=block_in,
+                                       out_channels=block_in,
+                                       temb_channels=self.temb_ch,
+                                       dropout=dropout)
+        self.mid.attn_1 = AttnBlock(block_in)
+        self.mid.block_2 = ResnetBlock(in_channels=block_in,
+                                       out_channels=block_in,
+                                       temb_channels=self.temb_ch,
+                                       dropout=dropout)
+
+        # end
+        self.norm_out = Normalize(block_in)
+        self.conv_out = torch.nn.Conv2d(block_in,
+                                        2*z_channels if double_z else z_channels,
+                                        kernel_size=3,
+                                        stride=1,
+                                        padding=1)
+
+
+    def forward(self, x):
+        #assert x.shape[2] == x.shape[3] == self.resolution, "{}, {}, {}".format(x.shape[2], x.shape[3], self.resolution)
+
+        # timestep embedding
+        temb = None
+
+        # downsampling
+        hs = [self.conv_in(x)]
+        for i_level in range(self.num_resolutions):
+            for i_block in range(self.num_res_blocks):
+                h = self.down[i_level].block[i_block](hs[-1], temb)
+                if len(self.down[i_level].attn) > 0:
+                    h = self.down[i_level].attn[i_block](h)
+                hs.append(h)
+            if i_level != self.num_resolutions-1:
+                hs.append(self.down[i_level].downsample(hs[-1]))
+
+        # middle
+        h = hs[-1]
+        h = self.mid.block_1(h, temb)
+        h = self.mid.attn_1(h)
+        h = self.mid.block_2(h, temb)
+
+        # end
+        h = self.norm_out(h)
+        h = nonlinearity(h)
+        h = self.conv_out(h)
+        return h
+
+class Encoder1d(Encoder):
+    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
+                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
+                 resolution, z_channels, double_z=True, **ignore_kwargs):
+        super().__init__(ch=ch, out_ch=out_ch, ch_mult=ch_mult, num_res_blocks=num_res_blocks,
+                         attn_resolutions=attn_resolutions, dropout=dropout,
+                         resamp_with_conv=resamp_with_conv,
+                         in_channels=in_channels, resolution=resolution, z_channels=z_channels,
+                         double_z=double_z, **ignore_kwargs)
+        self.ch = ch
+        self.temb_ch = 0
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.resolution = resolution
+        self.in_channels = in_channels
+
+        # downsampling
+        self.conv_in = torch.nn.Conv1d(in_channels,
+                                       self.ch,
+                                       kernel_size=3,
+                                       stride=1,
+                                       padding=1)
+
+        curr_res = resolution
+        in_ch_mult = (1,)+tuple(ch_mult)
+        self.down = nn.ModuleList()
+        for i_level in range(self.num_resolutions):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_in = ch*in_ch_mult[i_level]
+            block_out = ch*ch_mult[i_level]
+            for i_block in range(self.num_res_blocks):
+                block.append(ResnetBlock1d(in_channels=block_in,
+                                           out_channels=block_out,
+                                           temb_channels=self.temb_ch,
+                                           dropout=dropout))
+                block_in = block_out
+                if curr_res in attn_resolutions:
+                    attn.append(AttnBlock1d(block_in))
+            down = nn.Module()
+            down.block = block
+            down.attn = attn
+            if i_level != self.num_resolutions-1:
+                down.downsample = Downsample1d(block_in, resamp_with_conv)
+                curr_res = curr_res // 2
+            self.down.append(down)
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = ResnetBlock1d(in_channels=block_in,
+                                         out_channels=block_in,
+                                         temb_channels=self.temb_ch,
+                                         dropout=dropout)
+        self.mid.attn_1 = AttnBlock1d(block_in)
+        self.mid.block_2 = ResnetBlock1d(in_channels=block_in,
+                                         out_channels=block_in,
+                                         temb_channels=self.temb_ch,
+                                         dropout=dropout)
+
+        # end
+        self.norm_out = Normalize(block_in)
+        self.conv_out = torch.nn.Conv1d(block_in,
+                                        2*z_channels if double_z else z_channels,
+                                        kernel_size=3,
+                                        stride=1,
+                                        padding=1)
+
+
+class Decoder(nn.Module):
+    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
+                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
+                 resolution, z_channels, give_pre_end=False, **ignorekwargs):
+        super().__init__()
+        self.ch = ch
+        self.temb_ch = 0
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.resolution = resolution
+        self.in_channels = in_channels
+        self.give_pre_end = give_pre_end
+
+        # compute in_ch_mult, block_in and curr_res at lowest res
+        in_ch_mult = (1,)+tuple(ch_mult)
+        block_in = ch*ch_mult[self.num_resolutions-1]
+        curr_res = resolution // 2**(self.num_resolutions-1)
+        # self.z_shape = (1,z_channels,curr_res,curr_res)
+        # print("Working with z of shape {} = {} dimensions.".format(
+        #     self.z_shape, np.prod(self.z_shape)))
+
+        # z to block_in
+        self.conv_in = torch.nn.Conv2d(z_channels,
+                                       block_in,
+                                       kernel_size=3,
+                                       stride=1,
+                                       padding=1)
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = ResnetBlock(in_channels=block_in,
+                                       out_channels=block_in,
+                                       temb_channels=self.temb_ch,
+                                       dropout=dropout)
+        self.mid.attn_1 = AttnBlock(block_in)
+        self.mid.block_2 = ResnetBlock(in_channels=block_in,
+                                       out_channels=block_in,
+                                       temb_channels=self.temb_ch,
+                                       dropout=dropout)
+
+        # upsampling
+        self.up = nn.ModuleList()
+        for i_level in reversed(range(self.num_resolutions)):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_out = ch*ch_mult[i_level]
+            for i_block in range(self.num_res_blocks+1):
+                block.append(ResnetBlock(in_channels=block_in,
+                                         out_channels=block_out,
+                                         temb_channels=self.temb_ch,
+                                         dropout=dropout))
+                block_in = block_out
+                if curr_res in attn_resolutions:
+                    attn.append(AttnBlock(block_in))
+            up = nn.Module()
+            up.block = block
+            up.attn = attn
+            if i_level != 0:
+                up.upsample = Upsample(block_in, resamp_with_conv)
+                curr_res = curr_res * 2
+            self.up.insert(0, up) # prepend to get consistent order
+
+        # end
+        self.norm_out = Normalize(block_in)
+        self.conv_out = torch.nn.Conv2d(block_in,
+                                        out_ch,
+                                        kernel_size=3,
+                                        stride=1,
+                                        padding=1)
+
+    def forward(self, z):
+        #assert z.shape[1:] == self.z_shape[1:]
+        self.last_z_shape = z.shape
+
+        # timestep embedding
+        temb = None
+
+        # z to block_in
+        h = self.conv_in(z)
+
+        # middle
+        h = self.mid.block_1(h, temb)
+        h = self.mid.attn_1(h)
+        h = self.mid.block_2(h, temb)
+
+        # upsampling
+        for i_level in reversed(range(self.num_resolutions)):
+            for i_block in range(self.num_res_blocks+1):
+                h = self.up[i_level].block[i_block](h, temb)
+                if len(self.up[i_level].attn) > 0:
+                    h = self.up[i_level].attn[i_block](h)
+            if i_level != 0:
+                h = self.up[i_level].upsample(h)
+
+        # end
+        if self.give_pre_end:
+            return h
+
+        h = self.norm_out(h)
+        h = nonlinearity(h)
+        h = self.conv_out(h)
+        return h
+
+class Decoder1d(Decoder):
+    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
+                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
+                 resolution, z_channels, give_pre_end=False, **ignorekwargs):
+        super().__init__(ch=ch, out_ch=out_ch, ch_mult=ch_mult, num_res_blocks=num_res_blocks,
+                         attn_resolutions=attn_resolutions, dropout=dropout,
+                         resamp_with_conv=resamp_with_conv,
+                         in_channels=in_channels, resolution=resolution, z_channels=z_channels,
+                         give_pre_end=give_pre_end, **ignorekwargs)
+        self.ch = ch
+        self.temb_ch = 0
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.resolution = resolution
+        self.in_channels = in_channels
+        self.give_pre_end = give_pre_end
+
+        # compute in_ch_mult, block_in and curr_res at lowest res
+        in_ch_mult = (1,) + tuple(ch_mult)
+        block_in = ch * ch_mult[self.num_resolutions-1]
+        curr_res = resolution // 2**(self.num_resolutions-1)
+        # self.z_shape = (1,z_channels,curr_res,curr_res)
+        # print("Working with z of shape {} = {} dimensions.".format(
+        #     self.z_shape, np.prod(self.z_shape)))
+
+        # z to block_in
+        self.conv_in = torch.nn.Conv1d(z_channels, block_in, kernel_size=3, stride=1, padding=1)
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = ResnetBlock1d(in_channels=block_in, out_channels=block_in,
+                                         temb_channels=self.temb_ch, dropout=dropout)
+        self.mid.attn_1 = AttnBlock1d(block_in)
+        self.mid.block_2 = ResnetBlock1d(in_channels=block_in, out_channels=block_in,
+                                         temb_channels=self.temb_ch, dropout=dropout)
+
+        # upsampling
+        self.up = nn.ModuleList()
+        for i_level in reversed(range(self.num_resolutions)):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_out = ch * ch_mult[i_level]
+            for i_block in range(self.num_res_blocks+1):
+                block.append(ResnetBlock1d(in_channels=block_in, out_channels=block_out,
+                                           temb_channels=self.temb_ch, dropout=dropout))
+                block_in = block_out
+                if curr_res in attn_resolutions:
+                    attn.append(AttnBlock1d(block_in))
+            up = nn.Module()
+            up.block = block
+            up.attn = attn
+            if i_level != 0:
+                up.upsample = Upsample1d(block_in, resamp_with_conv)
+                curr_res = curr_res * 2
+            self.up.insert(0, up)  # prepend to get consistent order
+
+        # end
+        self.norm_out = Normalize(block_in)
+        self.conv_out = torch.nn.Conv1d(block_in, out_ch, kernel_size=3, stride=1, padding=1)
+
+
+class VUNet(nn.Module):
+    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
+                 attn_resolutions, dropout=0.0, resamp_with_conv=True,
+                 in_channels, c_channels,
+                 resolution, z_channels, use_timestep=False, **ignore_kwargs):
+        super().__init__()
+        self.ch = ch
+        self.temb_ch = self.ch*4
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.resolution = resolution
+
+        self.use_timestep = use_timestep
+        if self.use_timestep:
+            # timestep embedding
+            self.temb = nn.Module()
+            self.temb.dense = nn.ModuleList([
+                torch.nn.Linear(self.ch,
+                                self.temb_ch),
+                torch.nn.Linear(self.temb_ch,
+                                self.temb_ch),
+            ])
+
+        # downsampling
+        self.conv_in = torch.nn.Conv2d(c_channels,
+                                       self.ch,
+                                       kernel_size=3,
+                                       stride=1,
+                                       padding=1)
+
+        curr_res = resolution
+        in_ch_mult = (1,)+tuple(ch_mult)
+        self.down = nn.ModuleList()
+        for i_level in range(self.num_resolutions):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_in = ch*in_ch_mult[i_level]
+            block_out = ch*ch_mult[i_level]
+            for i_block in range(self.num_res_blocks):
+                block.append(ResnetBlock(in_channels=block_in,
+                                         out_channels=block_out,
+                                         temb_channels=self.temb_ch,
+                                         dropout=dropout))
+                block_in = block_out
+                if curr_res in attn_resolutions:
+                    attn.append(AttnBlock(block_in))
+            down = nn.Module()
+            down.block = block
+            down.attn = attn
+            if i_level != self.num_resolutions-1:
+                down.downsample = Downsample(block_in, resamp_with_conv)
+                curr_res = curr_res // 2
+            self.down.append(down)
+
+        self.z_in = torch.nn.Conv2d(z_channels,
+                                    block_in,
+                                    kernel_size=1,
+                                    stride=1,
+                                    padding=0)
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = ResnetBlock(in_channels=2*block_in,
+                                       out_channels=block_in,
+                                       temb_channels=self.temb_ch,
+                                       dropout=dropout)
+        self.mid.attn_1 = AttnBlock(block_in)
+        self.mid.block_2 = ResnetBlock(in_channels=block_in,
+                                       out_channels=block_in,
+                                       temb_channels=self.temb_ch,
+                                       dropout=dropout)
+
+        # upsampling
+        self.up = nn.ModuleList()
+        for i_level in reversed(range(self.num_resolutions)):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_out = ch*ch_mult[i_level]
+            skip_in = ch*ch_mult[i_level]
+            for i_block in range(self.num_res_blocks+1):
+                if i_block == self.num_res_blocks:
+                    skip_in = ch*in_ch_mult[i_level]
+                block.append(ResnetBlock(in_channels=block_in+skip_in,
+                                         out_channels=block_out,
+                                         temb_channels=self.temb_ch,
+                                         dropout=dropout))
+                block_in = block_out
+                if curr_res in attn_resolutions:
+                    attn.append(AttnBlock(block_in))
+            up = nn.Module()
+            up.block = block
+            up.attn = attn
+            if i_level != 0:
+                up.upsample = Upsample(block_in, resamp_with_conv)
+                curr_res = curr_res * 2
+            self.up.insert(0, up) # prepend to get consistent order
+
+        # end
+        self.norm_out = Normalize(block_in)
+        self.conv_out = torch.nn.Conv2d(block_in,
+                                        out_ch,
+                                        kernel_size=3,
+                                        stride=1,
+                                        padding=1)
+
+
+    def forward(self, x, z):
+        #assert x.shape[2] == x.shape[3] == self.resolution
+
+        if self.use_timestep:
+            # timestep embedding
+            assert t is not None
+            temb = get_timestep_embedding(t, self.ch)
+            temb = self.temb.dense[0](temb)
+            temb = nonlinearity(temb)
+            temb = self.temb.dense[1](temb)
+        else:
+            temb = None
+
+        # downsampling
+        hs = [self.conv_in(x)]
+        for i_level in range(self.num_resolutions):
+            for i_block in range(self.num_res_blocks):
+                h = self.down[i_level].block[i_block](hs[-1], temb)
+                if len(self.down[i_level].attn) > 0:
+                    h = self.down[i_level].attn[i_block](h)
+                hs.append(h)
+            if i_level != self.num_resolutions-1:
+                hs.append(self.down[i_level].downsample(hs[-1]))
+
+        # middle
+        h = hs[-1]
+        z = self.z_in(z)
+        h = torch.cat((h,z),dim=1)
+        h = self.mid.block_1(h, temb)
+        h = self.mid.attn_1(h)
+        h = self.mid.block_2(h, temb)
+
+        # upsampling
+        for i_level in reversed(range(self.num_resolutions)):
+            for i_block in range(self.num_res_blocks+1):
+                h = self.up[i_level].block[i_block](
+                    torch.cat([h, hs.pop()], dim=1), temb)
+                if len(self.up[i_level].attn) > 0:
+                    h = self.up[i_level].attn[i_block](h)
+            if i_level != 0:
+                h = self.up[i_level].upsample(h)
+
+        # end
+        h = self.norm_out(h)
+        h = nonlinearity(h)
+        h = self.conv_out(h)
+        return h
+
+
+class SimpleDecoder(nn.Module):
+    def __init__(self, in_channels, out_channels, *args, **kwargs):
+        super().__init__()
+        self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1),
+                                     ResnetBlock(in_channels=in_channels,
+                                                 out_channels=2 * in_channels,
+                                                 temb_channels=0, dropout=0.0),
+                                     ResnetBlock(in_channels=2 * in_channels,
+                                                out_channels=4 * in_channels,
+                                                temb_channels=0, dropout=0.0),
+                                     ResnetBlock(in_channels=4 * in_channels,
+                                                out_channels=2 * in_channels,
+                                                temb_channels=0, dropout=0.0),
+                                     nn.Conv2d(2*in_channels, in_channels, 1),
+                                     Upsample(in_channels, with_conv=True)])
+        # end
+        self.norm_out = Normalize(in_channels)
+        self.conv_out = torch.nn.Conv2d(in_channels,
+                                        out_channels,
+                                        kernel_size=3,
+                                        stride=1,
+                                        padding=1)
+
+    def forward(self, x):
+        for i, layer in enumerate(self.model):
+            if i in [1,2,3]:
+                x = layer(x, None)
+            else:
+                x = layer(x)
+
+        h = self.norm_out(x)
+        h = nonlinearity(h)
+        x = self.conv_out(h)
+        return x
+
+
+class UpsampleDecoder(nn.Module):
+    def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution,
+                 ch_mult=(2,2), dropout=0.0):
+        super().__init__()
+        # upsampling
+        self.temb_ch = 0
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        block_in = in_channels
+        curr_res = resolution // 2 ** (self.num_resolutions - 1)
+        self.res_blocks = nn.ModuleList()
+        self.upsample_blocks = nn.ModuleList()
+        for i_level in range(self.num_resolutions):
+            res_block = []
+            block_out = ch * ch_mult[i_level]
+            for i_block in range(self.num_res_blocks + 1):
+                res_block.append(ResnetBlock(in_channels=block_in,
+                                         out_channels=block_out,
+                                         temb_channels=self.temb_ch,
+                                         dropout=dropout))
+                block_in = block_out
+            self.res_blocks.append(nn.ModuleList(res_block))
+            if i_level != self.num_resolutions - 1:
+                self.upsample_blocks.append(Upsample(block_in, True))
+                curr_res = curr_res * 2
+
+        # end
+        self.norm_out = Normalize(block_in)
+        self.conv_out = torch.nn.Conv2d(block_in,
+                                        out_channels,
+                                        kernel_size=3,
+                                        stride=1,
+                                        padding=1)
+
+    def forward(self, x):
+        # upsampling
+        h = x
+        for k, i_level in enumerate(range(self.num_resolutions)):
+            for i_block in range(self.num_res_blocks + 1):
+                h = self.res_blocks[i_level][i_block](h, None)
+            if i_level != self.num_resolutions - 1:
+                h = self.upsample_blocks[k](h)
+        h = self.norm_out(h)
+        h = nonlinearity(h)
+        h = self.conv_out(h)
+        return h
+
+
+if __name__ == '__main__':
+    ddconfig = {
+        'ch': 128,
+        'num_res_blocks': 2,
+        'dropout': 0.0,
+        'z_channels': 256,
+        'double_z': False,
+    }
+
+    # Audio example ##
+    ddconfig['in_channels'] = 1
+    ddconfig['resolution'] = 848
+    ddconfig['attn_resolutions'] = [53]
+    ddconfig['ch_mult'] = [1, 1, 2, 2, 4]
+    ddconfig['out_ch'] = 1
+    # input
+    inputs = torch.rand(4, 1, 80, 848)
+    print('Input:', inputs.shape)
+    # Encoder
+    encoder = Encoder(**ddconfig)
+    enc_outs = encoder(inputs)
+    print('Encoder out:', enc_outs.shape)
+    # Decoder
+    decoder = Decoder(**ddconfig)
+    quant_outs = torch.rand(4, 256, 5, 53)
+    dec_outs = decoder(quant_outs)
+    print('Decoder out:', dec_outs.shape)
diff --git a/specvqgan/modules/discriminator/model.py b/specvqgan/modules/discriminator/model.py
new file mode 100644
index 0000000..5263368
--- /dev/null
+++ b/specvqgan/modules/discriminator/model.py
@@ -0,0 +1,295 @@
+import functools
+import torch.nn as nn
+
+
+class ActNorm(nn.Module):
+    def __init__(self, num_features, logdet=False, affine=True,
+                 allow_reverse_init=False):
+        assert affine
+        super().__init__()
+        self.logdet = logdet
+        self.loc = nn.Parameter(torch.zeros(1, num_features, 1, 1))
+        self.scale = nn.Parameter(torch.ones(1, num_features, 1, 1))
+        self.allow_reverse_init = allow_reverse_init
+
+        self.register_buffer('initialized', torch.tensor(0, dtype=torch.uint8))
+
+    def initialize(self, input):
+        with torch.no_grad():
+            flatten = input.permute(1, 0, 2, 3).contiguous().view(input.shape[1], -1)
+            mean = (
+                flatten.mean(1)
+                .unsqueeze(1)
+                .unsqueeze(2)
+                .unsqueeze(3)
+                .permute(1, 0, 2, 3)
+            )
+            std = (
+                flatten.std(1)
+                .unsqueeze(1)
+                .unsqueeze(2)
+                .unsqueeze(3)
+                .permute(1, 0, 2, 3)
+            )
+
+            self.loc.data.copy_(-mean)
+            self.scale.data.copy_(1 / (std + 1e-6))
+
+    def forward(self, input, reverse=False):
+        if reverse:
+            return self.reverse(input)
+        if len(input.shape) == 2:
+            input = input[:, :, None, None]
+            squeeze = True
+        else:
+            squeeze = False
+
+        _, _, height, width = input.shape
+
+        if self.training and self.initialized.item() == 0:
+            self.initialize(input)
+            self.initialized.fill_(1)
+
+        h = self.scale * (input + self.loc)
+
+        if squeeze:
+            h = h.squeeze(-1).squeeze(-1)
+
+        if self.logdet:
+            log_abs = torch.log(torch.abs(self.scale))
+            logdet = height * width * torch.sum(log_abs)
+            logdet = logdet * torch.ones(input.shape[0]).to(input)
+            return h, logdet
+
+        return h
+
+    def reverse(self, output):
+        if self.training and self.initialized.item() == 0:
+            if not self.allow_reverse_init:
+                raise RuntimeError(
+                    "Initializing ActNorm in reverse direction is "
+                    "disabled by default. Use allow_reverse_init=True to enable."
+                )
+            else:
+                self.initialize(output)
+                self.initialized.fill_(1)
+
+        if len(output.shape) == 2:
+            output = output[:, :, None, None]
+            squeeze = True
+        else:
+            squeeze = False
+
+        h = output / self.scale - self.loc
+
+        if squeeze:
+            h = h.squeeze(-1).squeeze(-1)
+        return h
+
+def weights_init(m):
+    classname = m.__class__.__name__
+    if classname.find('Conv') != -1:
+        nn.init.normal_(m.weight.data, 0.0, 0.02)
+    elif classname.find('BatchNorm') != -1:
+        nn.init.normal_(m.weight.data, 1.0, 0.02)
+        nn.init.constant_(m.bias.data, 0)
+
+
+class NLayerDiscriminator(nn.Module):
+    """Defines a PatchGAN discriminator as in Pix2Pix
+        --> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py
+    """
+    def __init__(self, input_nc=3, ndf=64, n_layers=3, use_actnorm=False):
+        """Construct a PatchGAN discriminator
+        Parameters:
+            input_nc (int)  -- the number of channels in input images
+            ndf (int)       -- the number of filters in the last conv layer
+            n_layers (int)  -- the number of conv layers in the discriminator
+            norm_layer      -- normalization layer
+        """
+        super(NLayerDiscriminator, self).__init__()
+        if not use_actnorm:
+            norm_layer = nn.BatchNorm2d
+        else:
+            norm_layer = ActNorm
+        if type(norm_layer) == functools.partial:  # no need to use bias as BatchNorm2d has affine parameters
+            use_bias = norm_layer.func != nn.BatchNorm2d
+        else:
+            use_bias = norm_layer != nn.BatchNorm2d
+
+        kw = 4
+        padw = 1
+        sequence = [nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)]
+        nf_mult = 1
+        nf_mult_prev = 1
+        for n in range(1, n_layers):  # gradually increase the number of filters
+            nf_mult_prev = nf_mult
+            nf_mult = min(2 ** n, 8)
+            sequence += [
+                nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias),
+                norm_layer(ndf * nf_mult),
+                nn.LeakyReLU(0.2, True)
+            ]
+
+        nf_mult_prev = nf_mult
+        nf_mult = min(2 ** n_layers, 8)
+        sequence += [
+            nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias),
+            norm_layer(ndf * nf_mult),
+            nn.LeakyReLU(0.2, True)
+        ]
+        # output 1 channel prediction map
+        sequence += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)]
+        self.main = nn.Sequential(*sequence)
+
+    def forward(self, input):
+        """Standard forward."""
+        return self.main(input)
+
+class NLayerDiscriminator1dFeats(NLayerDiscriminator):
+    """Defines a PatchGAN discriminator as in Pix2Pix
+        --> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py
+    """
+    def __init__(self, input_nc=3, ndf=64, n_layers=3, use_actnorm=False):
+        """Construct a PatchGAN discriminator
+        Parameters:
+            input_nc (int)  -- the number of channels in input feats
+            ndf (int)       -- the number of filters in the last conv layer
+            n_layers (int)  -- the number of conv layers in the discriminator
+            norm_layer      -- normalization layer
+        """
+        super().__init__(input_nc=input_nc, ndf=64, n_layers=n_layers, use_actnorm=use_actnorm)
+
+        if not use_actnorm:
+            norm_layer = nn.BatchNorm1d
+        else:
+            norm_layer = ActNorm
+        if type(norm_layer) == functools.partial:  # no need to use bias as BatchNorm has affine parameters
+            use_bias = norm_layer.func != nn.BatchNorm1d
+        else:
+            use_bias = norm_layer != nn.BatchNorm1d
+
+        kw = 4
+        padw = 1
+        sequence = [nn.Conv1d(input_nc, input_nc//2, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)]
+        nf_mult = input_nc//2
+        nf_mult_prev = 1
+        for n in range(1, n_layers):  # gradually decrease the number of filters
+            nf_mult_prev = nf_mult
+            nf_mult = max(nf_mult_prev // (2 ** n), 8)
+            sequence += [
+                nn.Conv1d(nf_mult_prev, nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias),
+                norm_layer(nf_mult),
+                nn.LeakyReLU(0.2, True)
+            ]
+
+        nf_mult_prev = nf_mult
+        nf_mult = max(nf_mult_prev // (2 ** n), 8)
+        sequence += [
+            nn.Conv1d(nf_mult_prev, nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias),
+            norm_layer(nf_mult),
+            nn.LeakyReLU(0.2, True)
+        ]
+        nf_mult_prev = nf_mult
+        nf_mult = max(nf_mult_prev // (2 ** n), 8)
+        sequence += [
+            nn.Conv1d(nf_mult_prev, nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias),
+            norm_layer(nf_mult),
+            nn.LeakyReLU(0.2, True)
+        ]
+        # output 1 channel prediction map
+        sequence += [nn.Conv1d(nf_mult, 1, kernel_size=kw, stride=1, padding=padw)]
+        self.main = nn.Sequential(*sequence)
+
+
+class NLayerDiscriminator1dSpecs(NLayerDiscriminator):
+    """Defines a PatchGAN discriminator as in Pix2Pix
+        --> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py
+    """
+    def __init__(self, input_nc=80, ndf=64, n_layers=3, use_actnorm=False):
+        """Construct a PatchGAN discriminator
+        Parameters:
+            input_nc (int)  -- the number of channels in input specs
+            ndf (int)       -- the number of filters in the last conv layer
+            n_layers (int)  -- the number of conv layers in the discriminator
+            norm_layer      -- normalization layer
+        """
+        super().__init__(input_nc=input_nc, ndf=64, n_layers=n_layers, use_actnorm=use_actnorm)
+
+        if not use_actnorm:
+            norm_layer = nn.BatchNorm1d
+        else:
+            norm_layer = ActNorm
+        if type(norm_layer) == functools.partial:  # no need to use bias as BatchNorm has affine parameters
+            use_bias = norm_layer.func != nn.BatchNorm1d
+        else:
+            use_bias = norm_layer != nn.BatchNorm1d
+
+        kw = 4
+        padw = 1
+        sequence = [nn.Conv1d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)]
+        nf_mult = 1
+        nf_mult_prev = 1
+        for n in range(1, n_layers):  # gradually decrease the number of filters
+            nf_mult_prev = nf_mult
+            nf_mult = min(2 ** n, 8)
+            sequence += [
+                nn.Conv1d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias),
+                norm_layer(ndf * nf_mult),
+                nn.LeakyReLU(0.2, True)
+            ]
+
+        nf_mult_prev = nf_mult
+        nf_mult = min(2 ** n_layers, 8)
+        sequence += [
+            nn.Conv1d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias),
+            norm_layer(ndf * nf_mult),
+            nn.LeakyReLU(0.2, True)
+        ]
+        # output 1 channel prediction map
+        sequence += [nn.Conv1d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)]
+        self.main = nn.Sequential(*sequence)
+
+    def forward(self, input):
+        """Standard forward."""
+        # (B, C, L)
+        input = input.squeeze(1)
+        input = self.main(input)
+        return input
+
+
+if __name__ == '__main__':
+    import torch
+
+    ## FEATURES
+    disc_in_channels = 2048
+    disc_num_layers = 2
+    use_actnorm = False
+    disc_ndf = 64
+    discriminator = NLayerDiscriminator1dFeats(input_nc=disc_in_channels, n_layers=disc_num_layers,
+                                            use_actnorm=use_actnorm, ndf=disc_ndf).apply(weights_init)
+    inputs = torch.rand((6, 2048, 212))
+    outputs = discriminator(inputs)
+    print(outputs.shape)
+
+    ## AUDIO
+    disc_in_channels = 1
+    disc_num_layers = 3
+    use_actnorm = False
+    disc_ndf = 64
+    discriminator = NLayerDiscriminator(input_nc=disc_in_channels, n_layers=disc_num_layers,
+                                        use_actnorm=use_actnorm, ndf=disc_ndf).apply(weights_init)
+    inputs = torch.rand((6, 1, 80, 848))
+    outputs = discriminator(inputs)
+    print(outputs.shape)
+
+    ## IMAGE
+    disc_in_channels = 3
+    disc_num_layers = 3
+    use_actnorm = False
+    disc_ndf = 64
+    discriminator = NLayerDiscriminator(input_nc=disc_in_channels, n_layers=disc_num_layers,
+                                        use_actnorm=use_actnorm, ndf=disc_ndf).apply(weights_init)
+    inputs = torch.rand((6, 3, 256, 256))
+    outputs = discriminator(inputs)
+    print(outputs.shape)
diff --git a/specvqgan/modules/losses/__init__.py b/specvqgan/modules/losses/__init__.py
new file mode 100644
index 0000000..b7367e9
--- /dev/null
+++ b/specvqgan/modules/losses/__init__.py
@@ -0,0 +1,7 @@
+from specvqgan.modules.losses.vqperceptual import DummyLoss
+
+# relative imports pain
+import os
+import sys
+path = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'vggishish')
+sys.path.append(path)
diff --git a/specvqgan/modules/losses/lpaps.py b/specvqgan/modules/losses/lpaps.py
new file mode 100644
index 0000000..d4b48de
--- /dev/null
+++ b/specvqgan/modules/losses/lpaps.py
@@ -0,0 +1,152 @@
+"""
+    Based on https://github.com/CompVis/taming-transformers/blob/52720829/taming/modules/losses/lpips.py
+    Adapted for spectrograms by Vladimir Iashin (v-iashin)
+"""
+from collections import namedtuple
+
+import numpy as np
+import torch
+import torch.nn as nn
+
+import sys
+sys.path.insert(0, '.')  # nopep8
+from specvqgan.modules.losses.vggishish.model import VGGishish
+from specvqgan.util import get_ckpt_path
+
+
+class LPAPS(nn.Module):
+    # Learned perceptual metric
+    def __init__(self, use_dropout=True):
+        super().__init__()
+        self.scaling_layer = ScalingLayer()
+        self.chns = [64, 128, 256, 512, 512]  # vggish16 features
+        self.net = vggishish16(pretrained=True, requires_grad=False)
+        self.lin0 = NetLinLayer(self.chns[0], use_dropout=use_dropout)
+        self.lin1 = NetLinLayer(self.chns[1], use_dropout=use_dropout)
+        self.lin2 = NetLinLayer(self.chns[2], use_dropout=use_dropout)
+        self.lin3 = NetLinLayer(self.chns[3], use_dropout=use_dropout)
+        self.lin4 = NetLinLayer(self.chns[4], use_dropout=use_dropout)
+        self.load_from_pretrained()
+        for param in self.parameters():
+            param.requires_grad = False
+
+    def load_from_pretrained(self, name="vggishish_lpaps"):
+        ckpt = get_ckpt_path(name, "specvqgan/modules/autoencoder/lpaps")
+        self.load_state_dict(torch.load(ckpt, map_location=torch.device("cpu")), strict=False)
+        print("loaded pretrained LPAPS loss from {}".format(ckpt))
+
+    @classmethod
+    def from_pretrained(cls, name="vggishish_lpaps"):
+        if name != "vggishish_lpaps":
+            raise NotImplementedError
+        model = cls()
+        ckpt = get_ckpt_path(name)
+        model.load_state_dict(torch.load(ckpt, map_location=torch.device("cpu")), strict=False)
+        return model
+
+    def forward(self, input, target):
+        in0_input, in1_input = (self.scaling_layer(input), self.scaling_layer(target))
+        outs0, outs1 = self.net(in0_input), self.net(in1_input)
+        feats0, feats1, diffs = {}, {}, {}
+        lins = [self.lin0, self.lin1, self.lin2, self.lin3, self.lin4]
+        for kk in range(len(self.chns)):
+            feats0[kk], feats1[kk] = normalize_tensor(outs0[kk]), normalize_tensor(outs1[kk])
+            diffs[kk] = (feats0[kk] - feats1[kk]) ** 2
+
+        res = [spatial_average(lins[kk].model(diffs[kk]), keepdim=True) for kk in range(len(self.chns))]
+        val = res[0]
+        for l in range(1, len(self.chns)):
+            val += res[l]
+        return val
+
+class ScalingLayer(nn.Module):
+    def __init__(self):
+        super(ScalingLayer, self).__init__()
+        # we are gonna use get_ckpt_path to donwload the stats as well
+        stat_path = get_ckpt_path('vggishish_mean_std_melspec_10s_22050hz', 'specvqgan/modules/autoencoder/lpaps')
+        # if for images we normalize on the channel dim, in spectrogram we will norm on frequency dimension
+        means, stds = np.loadtxt(stat_path, dtype=np.float32).T
+        # the normalization in means and stds are given for [0, 1], but specvqgan expects [-1, 1]:
+        means = 2 * means - 1
+        stds = 2 * stds
+        # input is expected to be (B, 1, F, T)
+        self.register_buffer('shift', torch.from_numpy(means)[None, None, :, None])
+        self.register_buffer('scale', torch.from_numpy(stds)[None, None, :, None])
+
+    def forward(self, inp):
+        return (inp - self.shift) / self.scale
+
+
+class NetLinLayer(nn.Module):
+    """ A single linear layer which does a 1x1 conv """
+    def __init__(self, chn_in, chn_out=1, use_dropout=False):
+        super(NetLinLayer, self).__init__()
+        layers = [nn.Dropout(), ] if (use_dropout) else []
+        layers += [nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False), ]
+        self.model = nn.Sequential(*layers)
+
+class vggishish16(torch.nn.Module):
+    def __init__(self, requires_grad=False, pretrained=True):
+        super().__init__()
+        vgg_pretrained_features = self.vggishish16(pretrained=pretrained).features
+        self.slice1 = torch.nn.Sequential()
+        self.slice2 = torch.nn.Sequential()
+        self.slice3 = torch.nn.Sequential()
+        self.slice4 = torch.nn.Sequential()
+        self.slice5 = torch.nn.Sequential()
+        self.N_slices = 5
+        for x in range(4):
+            self.slice1.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(4, 9):
+            self.slice2.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(9, 16):
+            self.slice3.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(16, 23):
+            self.slice4.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(23, 30):
+            self.slice5.add_module(str(x), vgg_pretrained_features[x])
+        if not requires_grad:
+            for param in self.parameters():
+                param.requires_grad = False
+
+    def forward(self, X):
+        h = self.slice1(X)
+        h_relu1_2 = h
+        h = self.slice2(h)
+        h_relu2_2 = h
+        h = self.slice3(h)
+        h_relu3_3 = h
+        h = self.slice4(h)
+        h_relu4_3 = h
+        h = self.slice5(h)
+        h_relu5_3 = h
+        vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3', 'relu5_3'])
+        out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3)
+        return out
+
+    def vggishish16(self, pretrained: bool = True) -> VGGishish:
+        # loading vggishish pretrained on vggsound
+        num_classes_vggsound = 309
+        conv_layers = [64, 64, 'MP', 128, 128, 'MP', 256, 256, 256, 'MP', 512, 512, 512, 'MP', 512, 512, 512]
+        model = VGGishish(conv_layers, use_bn=False, num_classes=num_classes_vggsound)
+        if pretrained:
+            ckpt_path = get_ckpt_path('vggishish_lpaps', "specvqgan/modules/autoencoder/lpaps")
+            ckpt = torch.load(ckpt_path, map_location=torch.device("cpu"))
+            model.load_state_dict(ckpt, strict=False)
+        return model
+
+def normalize_tensor(x, eps=1e-10):
+    norm_factor = torch.sqrt(torch.sum(x**2, dim=1, keepdim=True))
+    return x / (norm_factor+eps)
+
+def spatial_average(x, keepdim=True):
+    return x.mean([2, 3], keepdim=keepdim)
+
+
+if __name__ == '__main__':
+    inputs = torch.rand((16, 1, 80, 848))
+    reconstructions = torch.rand((16, 1, 80, 848))
+    lpips = LPAPS().eval()
+    loss_p = lpips(inputs.contiguous(), reconstructions.contiguous())
+    # (16, 1, 1, 1)
+    print(loss_p.shape)
diff --git a/specvqgan/modules/losses/vggishish/configs/melception.yaml b/specvqgan/modules/losses/vggishish/configs/melception.yaml
new file mode 100644
index 0000000..4c03169
--- /dev/null
+++ b/specvqgan/modules/losses/vggishish/configs/melception.yaml
@@ -0,0 +1,24 @@
+seed: 1337
+log_code_state: True
+# patterns to ignore when backing up the code folder
+patterns_to_ignore: ['logs', '.git', '__pycache__', 'data', 'checkpoints', '*.pt']
+
+# data:
+mels_path: '/home/nvme/data/vggsound/features/melspec_10s_22050hz/'
+spec_shape: [80, 860]
+cropped_size: [80, 848]
+random_crop: False
+
+# train:
+device: 'cuda:0'
+batch_size: 8
+num_workers: 0
+optimizer: adam
+betas: [0.9, 0.999]
+momentum: 0.9
+learning_rate: 3e-4
+weight_decay: 0
+num_epochs: 100
+patience: 3
+logdir: './logs'
+cls_weights_in_loss: False
diff --git a/specvqgan/modules/losses/vggishish/configs/vggish.yaml b/specvqgan/modules/losses/vggishish/configs/vggish.yaml
new file mode 100644
index 0000000..f973596
--- /dev/null
+++ b/specvqgan/modules/losses/vggishish/configs/vggish.yaml
@@ -0,0 +1,34 @@
+seed: 1337
+log_code_state: True
+# patterns to ignore when backing up the code folder
+patterns_to_ignore: ['logs', '.git', '__pycache__']
+
+# data:
+mels_path: '/home/nvme/data/vggsound/features/melspec_10s_22050hz/'
+spec_shape: [80, 860]
+cropped_size: [80, 848]
+random_crop: False
+
+# model:
+# original vgg family except for MP is missing at the end
+# 'vggish': [64, 'MP', 128, 'MP', 256, 256, 'MP', 512, 512]
+# 'vgg11': [64, 'MP', 128, 'MP', 256, 256, 'MP', 512, 512, 'MP', 512, 512],
+# 'vgg13': [64, 64, 'MP', 128, 128, 'MP', 256, 256, 'MP', 512, 512, 'MP', 512, 512],
+# 'vgg16': [64, 64, 'MP', 128, 128, 'MP', 256, 256, 256, 'MP', 512, 512, 512, 'MP', 512, 512, 512],
+# 'vgg19': [64, 64, 'MP', 128, 128, 'MP', 256, 256, 256, 256, 'MP', 512, 512, 512, 512, 'MP', 512, 512, 512, 512],
+conv_layers: [64, 64, 'MP', 128, 128, 'MP', 256, 256, 256, 'MP', 512, 512, 512, 'MP', 512, 512, 512]
+use_bn: False
+
+# train:
+device: 'cuda:0'
+batch_size: 32
+num_workers: 0
+optimizer: adam
+betas: [0.9, 0.999]
+momentum: 0.9
+learning_rate: 3e-4
+weight_decay: 0.0001
+num_epochs: 100
+patience: 3
+logdir: './logs'
+cls_weights_in_loss: False
diff --git a/specvqgan/modules/losses/vggishish/data/train_means_stds_melspec_10s_22050hz.txt b/specvqgan/modules/losses/vggishish/data/train_means_stds_melspec_10s_22050hz.txt
new file mode 100644
index 0000000..5338cad
--- /dev/null
+++ b/specvqgan/modules/losses/vggishish/data/train_means_stds_melspec_10s_22050hz.txt
@@ -0,0 +1,80 @@
+0.51234712 0.08187601
+0.52630101 0.08393201
+0.52002938 0.08533191
+0.51831866 0.08651366
+0.52457265 0.08795700
+0.51129235 0.08924046
+0.51403755 0.09011565
+0.51189406 0.09138965
+0.50142221 0.09215379
+0.50632402 0.09251092
+0.49724399 0.09356590
+0.49062130 0.09333057
+0.49971113 0.09446411
+0.48442903 0.09400197
+0.48598301 0.09477853
+0.48681630 0.09487848
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diff --git a/specvqgan/modules/losses/vggishish/data/vggsound.csv b/specvqgan/modules/losses/vggishish/data/vggsound.csv
new file mode 100644
index 0000000..dbcbf5b
--- /dev/null
+++ b/specvqgan/modules/losses/vggishish/data/vggsound.csv
@@ -0,0 +1,199176 @@
+---g-f_I2yQ,1,people marching,test
+--0PQM4-hqg,30,waterfall burbling,train
+--56QUhyDQM,185,playing tennis,train
+--5OkAjCI7g,40,people belly laughing,train
+--8puiAGLhs,30,car engine starting,train
+--96EN9NUQM,242,alarm clock ringing,train
+--9O4XZOge4,30,"female speech, woman speaking",train
+--Aa4M484QM,0,cricket chirping,train
+--Bu2xe4OSo,430,wind noise,train
+--CC5pH97q4,4,foghorn,train
+--CC5pH97q4,33,foghorn,train
+--CudrykwoE,67,cricket chirping,train
+--CudrykwoE,78,cricket chirping,train
+--DTR0_mIGI,11,people battle cry,train
+--DTR0_mIGI,32,people battle cry,train
+--EquRwM9IQ,8,playing volleyball,train
+--FenyW2i_4,0,female singing,train
+--G-wKyj6JQ,30,playing harpsichord,train
+--HuBNHXu9I,30,"male speech, man speaking",train
+--I3Bjp_ptc,254,playing bassoon,train
+--IOqc-XgYo,0,playing piano,train
+--L3BCCcGEw,400,people clapping,train
+--Lj4Y_96f0,120,"bee, wasp, etc. buzzing",train
+--Nrb6rtheE,10,baby babbling,train
+--PlJNEnf-s,288,"bee, wasp, etc. buzzing",train
+--Q8wkZvDZE,150,people whispering,train
+--QVnZXkb_Y,74,coyote howling,train
+--QVnZXkb_Y,98,coyote howling,train
+--R3QLObQ5I,319,metronome,train
+--SQyOb8eS0,30,playing harp,train
+--SvivLlKLU,137,airplane,train
+--SvivLlKLU,662,airplane,train
+--TF_YkxfvQ,1,rope skipping,train
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+--TKJIv9aY4,210,ambulance siren,train
+--TKJIv9aY4,282,ambulance siren,train
+--U7joUcTCo,0,people coughing,test
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+--UQXuZfbFY,44,bird wings flapping,train
+--UQXuZfbFY,59,bird wings flapping,train
+--UgnJTCTqg,0,people battle cry,train
+--UgnJTCTqg,101,people battle cry,train
+--XInAaMS6k,61,cap gun shooting,train
+--XInAaMS6k,72,cap gun shooting,train
+--Z4X0SQxBc,290,people clapping,train
+--ZHUMfueO0,30,female singing,train
+--_K9nQrmDU,30,child singing,train
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+-0VKonoRviE,209,church bell ringing,train
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+-0qyuT918Jc,75,planing timber,train
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+-0u9E3dETG0,199,"bee, wasp, etc. buzzing",train
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+-1B4--7U1xY,64,"vehicle horn, car horn, honking",train
+-1BLaahvBSM,266,child singing,train
+-1BLaahvBSM,1048,child singing,train
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+-1C5GLtgcbY,30,playing washboard,train
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+-2JAK9CdWvY,435,slot machine,train
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+-2qJQqXbtZs,42,vacuum cleaner cleaning floors,train
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+-40DA8RPuq0,216,train horning,train
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+_Pm4A9bNmXk,22,"alligators, crocodiles hissing",train
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diff --git a/specvqgan/modules/losses/vggishish/data/vggsound_test.txt b/specvqgan/modules/losses/vggishish/data/vggsound_test.txt
new file mode 100644
index 0000000..227999d
--- /dev/null
+++ b/specvqgan/modules/losses/vggishish/data/vggsound_test.txt
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diff --git a/specvqgan/modules/losses/vggishish/data/vggsound_train.txt b/specvqgan/modules/losses/vggishish/data/vggsound_train.txt
new file mode 100644
index 0000000..a956df6
--- /dev/null
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diff --git a/specvqgan/modules/losses/vggishish/data/vggsound_valid.txt b/specvqgan/modules/losses/vggishish/data/vggsound_valid.txt
new file mode 100644
index 0000000..8291426
--- /dev/null
+++ b/specvqgan/modules/losses/vggishish/data/vggsound_valid.txt
@@ -0,0 +1,19056 @@
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diff --git a/specvqgan/modules/losses/vggishish/dataset.py b/specvqgan/modules/losses/vggishish/dataset.py
new file mode 100644
index 0000000..4451946
--- /dev/null
+++ b/specvqgan/modules/losses/vggishish/dataset.py
@@ -0,0 +1,146 @@
+import collections
+import csv
+import logging
+import os
+import random
+from glob import glob
+from pathlib import Path
+
+import numpy as np
+import torch
+import torchvision
+
+logger = logging.getLogger(f'main.{__name__}')
+
+
+class VGGSound(torch.utils.data.Dataset):
+
+    def __init__(self, split, specs_dir, transforms=None, splits_path='./data', meta_path='./data/vggsound.csv'):
+        super().__init__()
+        self.split = split
+        self.specs_dir = specs_dir
+        self.transforms = transforms
+        self.splits_path = splits_path
+        self.meta_path = meta_path
+
+        vggsound_meta = list(csv.reader(open(meta_path), quotechar='"'))
+        unique_classes = sorted(list(set(row[2] for row in vggsound_meta)))
+        self.label2target = {label: target for target, label in enumerate(unique_classes)}
+        self.target2label = {target: label for label, target in self.label2target.items()}
+        self.video2target = {row[0]: self.label2target[row[2]] for row in vggsound_meta}
+
+        split_clip_ids_path = os.path.join(splits_path, f'vggsound_{split}.txt')
+        if not os.path.exists(split_clip_ids_path):
+            self.make_split_files()
+        clip_ids_with_timestamp = open(split_clip_ids_path).read().splitlines()
+        clip_paths = [os.path.join(specs_dir, v + '_mel.npy') for v in clip_ids_with_timestamp]
+        self.dataset = clip_paths
+        # self.dataset = clip_paths[:10000]  # overfit one batch
+
+        # 'zyTX_1BXKDE_16000_26000'[:11] -> 'zyTX_1BXKDE'
+        vid_classes = [self.video2target[Path(path).stem[:11]] for path in self.dataset]
+        class2count = collections.Counter(vid_classes)
+        self.class_counts = torch.tensor([class2count[cls] for cls in range(len(class2count))])
+        # self.sample_weights = [len(self.dataset) / class2count[self.video2target[Path(path).stem[:11]]] for path in self.dataset]
+
+    def __getitem__(self, idx):
+        item = {}
+
+        spec_path = self.dataset[idx]
+        # 'zyTX_1BXKDE_16000_26000' -> 'zyTX_1BXKDE'
+        video_name = Path(spec_path).stem[:11]
+
+        item['input'] = np.load(spec_path)
+        item['input_path'] = spec_path
+
+        # if self.split in ['train', 'valid']:
+        item['target'] = self.video2target[video_name]
+        item['label'] = self.target2label[item['target']]
+
+        if self.transforms is not None:
+            item = self.transforms(item)
+
+        return item
+
+    def __len__(self):
+        return len(self.dataset)
+
+    def make_split_files(self):
+        random.seed(1337)
+        logger.info(f'The split files do not exist @ {self.splits_path}. Calculating the new ones.')
+        # The downloaded videos (some went missing on YouTube and no longer available)
+        available_vid_paths = sorted(glob(os.path.join(self.specs_dir, '*_mel.npy')))
+        logger.info(f'The number of clips available after download: {len(available_vid_paths)}')
+
+        # original (full) train and test sets
+        vggsound_meta = list(csv.reader(open(self.meta_path), quotechar='"'))
+        train_vids = {row[0] for row in vggsound_meta if row[3] == 'train'}
+        test_vids = {row[0] for row in vggsound_meta if row[3] == 'test'}
+        logger.info(f'The number of videos in vggsound train set: {len(train_vids)}')
+        logger.info(f'The number of videos in vggsound test set: {len(test_vids)}')
+
+        # class counts in test set. We would like to have the same distribution in valid
+        unique_classes = sorted(list(set(row[2] for row in vggsound_meta)))
+        label2target = {label: target for target, label in enumerate(unique_classes)}
+        video2target = {row[0]: label2target[row[2]] for row in vggsound_meta}
+        test_vid_classes = [video2target[vid] for vid in test_vids]
+        test_target2count = collections.Counter(test_vid_classes)
+
+        # now given the counts from test set, sample the same count for validation and the rest leave in train
+        train_vids_wo_valid, valid_vids = set(), set()
+        for target, label in enumerate(label2target.keys()):
+            class_train_vids = [vid for vid in train_vids if video2target[vid] == target]
+            random.shuffle(class_train_vids)
+            count = test_target2count[target]
+            valid_vids.update(class_train_vids[:count])
+            train_vids_wo_valid.update(class_train_vids[count:])
+
+        # make file with a list of available test videos (each video should contain timestamps as well)
+        train_i = valid_i = test_i = 0
+        with open(os.path.join(self.splits_path, 'vggsound_train.txt'), 'w') as train_file, \
+             open(os.path.join(self.splits_path, 'vggsound_valid.txt'), 'w') as valid_file, \
+             open(os.path.join(self.splits_path, 'vggsound_test.txt'), 'w') as test_file:
+            for path in available_vid_paths:
+                path = path.replace('_mel.npy', '')
+                vid_name = Path(path).name
+                # 'zyTX_1BXKDE_16000_26000'[:11] -> 'zyTX_1BXKDE'
+                if vid_name[:11] in train_vids_wo_valid:
+                    train_file.write(vid_name + '\n')
+                    train_i += 1
+                elif vid_name[:11] in valid_vids:
+                    valid_file.write(vid_name + '\n')
+                    valid_i += 1
+                elif vid_name[:11] in test_vids:
+                    test_file.write(vid_name + '\n')
+                    test_i += 1
+                else:
+                    raise Exception(f'Clip {vid_name} is neither in train, valid nor test. Strange.')
+
+        logger.info(f'Put {train_i} clips to the train set and saved it to ./data/vggsound_train.txt')
+        logger.info(f'Put {valid_i} clips to the valid set and saved it to ./data/vggsound_valid.txt')
+        logger.info(f'Put {test_i} clips to the test set and saved it to ./data/vggsound_test.txt')
+
+
+if __name__ == '__main__':
+    from transforms import Crop, StandardNormalizeAudio, ToTensor
+    specs_path = '/home/nvme/data/vggsound/features/melspec_10s_22050hz/'
+
+    transforms = torchvision.transforms.transforms.Compose([
+        StandardNormalizeAudio(specs_path),
+        ToTensor(),
+        Crop([80, 848]),
+    ])
+
+    datasets = {
+        'train': VGGSound('train', specs_path, transforms),
+        'valid': VGGSound('valid', specs_path, transforms),
+        'test': VGGSound('test', specs_path, transforms),
+    }
+
+    print(datasets['train'][0])
+    print(datasets['valid'][0])
+    print(datasets['test'][0])
+
+    print(datasets['train'].class_counts)
+    print(datasets['valid'].class_counts)
+    print(datasets['test'].class_counts)
diff --git a/specvqgan/modules/losses/vggishish/logger.py b/specvqgan/modules/losses/vggishish/logger.py
new file mode 100644
index 0000000..c9737cc
--- /dev/null
+++ b/specvqgan/modules/losses/vggishish/logger.py
@@ -0,0 +1,87 @@
+import logging
+import os
+import time
+from shutil import copytree, ignore_patterns
+
+import torch
+from omegaconf import OmegaConf
+from torch.utils.tensorboard import SummaryWriter, summary
+
+
+class LoggerWithTBoard(SummaryWriter):
+
+    def __init__(self, cfg):
+        # current time stamp and experiment log directory
+        self.start_time = time.strftime('%y-%m-%dT%H-%M-%S', time.localtime())
+        self.logdir = os.path.join(cfg.logdir, self.start_time)
+        # init tboard
+        super().__init__(self.logdir)
+        # backup the cfg
+        OmegaConf.save(cfg, os.path.join(self.log_dir, 'cfg.yaml'))
+        # backup the code state
+        if cfg.log_code_state:
+            dest_dir = os.path.join(self.logdir, 'code')
+            copytree(os.getcwd(), dest_dir, ignore=ignore_patterns(*cfg.patterns_to_ignore))
+
+        # init logger which handles printing and logging mostly same things to the log file
+        self.print_logger = logging.getLogger('main')
+        self.print_logger.setLevel(logging.INFO)
+        msgfmt = '[%(levelname)s] %(asctime)s - %(name)s \n    %(message)s'
+        datefmt = '%d %b %Y %H:%M:%S'
+        formatter = logging.Formatter(msgfmt, datefmt)
+        # stdout
+        sh = logging.StreamHandler()
+        sh.setLevel(logging.DEBUG)
+        sh.setFormatter(formatter)
+        self.print_logger.addHandler(sh)
+        # log file
+        fh = logging.FileHandler(os.path.join(self.log_dir, 'log.txt'))
+        fh.setLevel(logging.INFO)
+        fh.setFormatter(formatter)
+        self.print_logger.addHandler(fh)
+
+        self.print_logger.info(f'Saving logs and checkpoints @ {self.logdir}')
+
+    def log_param_num(self, model):
+        param_num = sum(p.numel() for p in model.parameters() if p.requires_grad)
+        self.print_logger.info(f'The number of parameters: {param_num/1e+6:.3f} mil')
+        self.add_scalar('num_params', param_num, 0)
+        return param_num
+
+    def log_iter_loss(self, loss, iter, phase):
+        self.add_scalar(f'{phase}/loss_iter', loss, iter)
+
+    def log_epoch_loss(self, loss, epoch, phase):
+        self.add_scalar(f'{phase}/loss', loss, epoch)
+        self.print_logger.info(f'{phase} ({epoch}): loss {loss:.3f};')
+
+    def log_epoch_metrics(self, metrics_dict, epoch, phase):
+        for metric, val in metrics_dict.items():
+            self.add_scalar(f'{phase}/{metric}', val, epoch)
+        metrics_dict = {k: round(v, 4) for k, v in metrics_dict.items()}
+        self.print_logger.info(f'{phase} ({epoch}) metrics: {metrics_dict};')
+
+    def log_test_metrics(self, metrics_dict, hparams_dict, best_epoch):
+        allowed_types = (int, float, str, bool, torch.Tensor)
+        hparams_dict = {k: v for k, v in hparams_dict.items() if isinstance(v, allowed_types)}
+        metrics_dict = {f'test/{k}': round(v, 4) for k, v in metrics_dict.items()}
+        exp, ssi, sei = summary.hparams(hparams_dict, metrics_dict)
+        self.file_writer.add_summary(exp)
+        self.file_writer.add_summary(ssi)
+        self.file_writer.add_summary(sei)
+        for k, v in metrics_dict.items():
+            self.add_scalar(k, v, best_epoch)
+        self.print_logger.info(f'test ({best_epoch}) metrics: {metrics_dict};')
+
+    def log_best_model(self, model, loss, epoch, optimizer, metrics_dict):
+        model_name = model.__class__.__name__
+        self.best_model_path = os.path.join(self.logdir, f'{model_name}-{self.start_time}.pt')
+        checkpoint = {
+            'loss': loss,
+            'metrics': metrics_dict,
+            'epoch': epoch,
+            'optimizer': optimizer.state_dict(),
+            'model': model.state_dict(),
+        }
+        torch.save(checkpoint, self.best_model_path)
+        self.print_logger.info(f'Saved model in {self.best_model_path}')
diff --git a/specvqgan/modules/losses/vggishish/loss.py b/specvqgan/modules/losses/vggishish/loss.py
new file mode 100644
index 0000000..bae7657
--- /dev/null
+++ b/specvqgan/modules/losses/vggishish/loss.py
@@ -0,0 +1,41 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+
+class WeightedCrossEntropy(nn.CrossEntropyLoss):
+
+    def __init__(self, weights, **pytorch_ce_loss_args) -> None:
+        super().__init__(reduction='none', **pytorch_ce_loss_args)
+        self.weights = weights
+
+    def __call__(self, outputs, targets, to_weight=True):
+        loss = super().__call__(outputs, targets)
+        if to_weight:
+            return (loss * self.weights[targets]).sum() / self.weights[targets].sum()
+        else:
+            return loss.mean()
+
+
+if __name__ == '__main__':
+    x = torch.randn(10, 5)
+    target = torch.randint(0, 5, (10,))
+    weights = torch.tensor([1., 2., 3., 4., 5.])
+
+    # criterion_weighted = nn.CrossEntropyLoss(weight=weights)
+    # loss_weighted = criterion_weighted(x, target)
+
+    # criterion_weighted_manual = nn.CrossEntropyLoss(reduction='none')
+    # loss_weighted_manual = criterion_weighted_manual(x, target)
+    # print(loss_weighted, loss_weighted_manual.mean())
+    # loss_weighted_manual = (loss_weighted_manual * weights[target]).sum() / weights[target].sum()
+    # print(loss_weighted, loss_weighted_manual)
+    # print(torch.allclose(loss_weighted, loss_weighted_manual))
+
+    pytorch_weighted = nn.CrossEntropyLoss(weight=weights)
+    pytorch_unweighted = nn.CrossEntropyLoss()
+    custom = WeightedCrossEntropy(weights)
+
+    assert torch.allclose(pytorch_weighted(x, target), custom(x, target, to_weight=True))
+    assert torch.allclose(pytorch_unweighted(x, target), custom(x, target, to_weight=False))
+    print(custom(x, target, to_weight=True), custom(x, target, to_weight=False))
diff --git a/specvqgan/modules/losses/vggishish/metrics.py b/specvqgan/modules/losses/vggishish/metrics.py
new file mode 100644
index 0000000..1690522
--- /dev/null
+++ b/specvqgan/modules/losses/vggishish/metrics.py
@@ -0,0 +1,69 @@
+import logging
+
+import numpy as np
+import scipy
+import torch
+from sklearn.metrics import average_precision_score, roc_auc_score
+
+logger = logging.getLogger(f'main.{__name__}')
+
+def metrics(targets, outputs, topk=(1, 5)):
+    """
+    Adapted from https://github.com/hche11/VGGSound/blob/master/utils.py
+
+    Calculate statistics including mAP, AUC, and d-prime.
+        Args:
+            output: 2d tensors, (dataset_size, classes_num) - before softmax
+            target: 1d tensors, (dataset_size, )
+            topk: tuple
+        Returns:
+            metric_dict: a dict of metrics
+    """
+    metrics_dict = dict()
+
+    num_cls = outputs.shape[-1]
+
+    # accuracy@k
+    _, preds = torch.topk(outputs, k=max(topk), dim=1)
+    correct_for_maxtopk = preds == targets.view(-1, 1).expand_as(preds)
+    for k in topk:
+        metrics_dict[f'accuracy_{k}'] = float(correct_for_maxtopk[:, :k].sum() / correct_for_maxtopk.shape[0])
+
+    # avg precision, average roc_auc, and dprime
+    targets = torch.nn.functional.one_hot(targets, num_classes=num_cls)
+
+    # ids of the predicted classes (same as softmax)
+    targets_pred = torch.softmax(outputs, dim=1)
+
+    targets = targets.numpy()
+    targets_pred = targets_pred.numpy()
+
+    # one-vs-rest
+    avg_p = [average_precision_score(targets[:, c], targets_pred[:, c], average=None) for c in range(num_cls)]
+    try:
+        roc_aucs = [roc_auc_score(targets[:, c], targets_pred[:, c], average=None) for c in range(num_cls)]
+    except ValueError:
+        logger.warning('Weird... Some classes never occured in targets. Do not trust the metrics.')
+        roc_aucs = np.array([0.5])
+        avg_p = np.array([0])
+
+    metrics_dict['mAP'] = np.mean(avg_p)
+    metrics_dict['mROCAUC'] = np.mean(roc_aucs)
+    # Percent point function (ppf) (inverse of cdf — percentiles).
+    metrics_dict['dprime'] = scipy.stats.norm().ppf(metrics_dict['mROCAUC']) * np.sqrt(2)
+
+    return metrics_dict
+
+
+if __name__ == '__main__':
+    targets = torch.tensor([3, 3, 1, 2, 1, 0])
+    outputs = torch.tensor([
+        [1.2, 1.3, 1.1, 1.5],
+        [1.3, 1.4, 1.0, 1.1],
+        [1.5, 1.1, 1.4, 1.3],
+        [1.0, 1.2, 1.4, 1.5],
+        [1.2, 1.3, 1.1, 1.1],
+        [1.2, 1.1, 1.1, 1.1],
+    ]).float()
+    metrics_dict = metrics(targets, outputs, topk=(1, 3))
+    print(metrics_dict)
diff --git a/specvqgan/modules/losses/vggishish/model.py b/specvqgan/modules/losses/vggishish/model.py
new file mode 100644
index 0000000..d5069ba
--- /dev/null
+++ b/specvqgan/modules/losses/vggishish/model.py
@@ -0,0 +1,77 @@
+import torch
+import torch.nn as nn
+
+
+class VGGishish(nn.Module):
+
+    def __init__(self, conv_layers, use_bn, num_classes):
+        '''
+        Mostly from
+            https://pytorch.org/vision/0.8/_modules/torchvision/models/vgg.html
+        '''
+        super().__init__()
+        layers = []
+        in_channels = 1
+
+        # a list of channels with 'MP' (maxpool) from config
+        for v in conv_layers:
+            if v == 'MP':
+                layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
+            else:
+                conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1, stride=1)
+                if use_bn:
+                    layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
+                else:
+                    layers += [conv2d, nn.ReLU(inplace=True)]
+                in_channels = v
+        self.features = nn.Sequential(*layers)
+
+        self.avgpool = nn.AdaptiveAvgPool2d((5, 10))
+
+        self.flatten = nn.Flatten()
+        self.classifier = nn.Sequential(
+            nn.Linear(512 * 5 * 10, 4096),
+            nn.ReLU(True),
+            nn.Linear(4096, 4096),
+            nn.ReLU(True),
+            nn.Linear(4096, num_classes)
+        )
+
+        # weight init
+        self.reset_parameters()
+
+    def forward(self, x):
+        # adding channel dim for conv2d (B, 1, F, T) <-
+        x = x.unsqueeze(1)
+        # backbone (B, 1, 5, 53) <- (B, 1, 80, 860)
+        x = self.features(x)
+        # adaptive avg pooling (B, 1, 5, 10) <- (B, 1, 5, 53) – if no MP is used as the end of VGG
+        x = self.avgpool(x)
+        # flatten
+        x = self.flatten(x)
+        # classify
+        x = self.classifier(x)
+        return x
+
+    def reset_parameters(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, 0, 0.01)
+                nn.init.constant_(m.bias, 0)
+
+
+if __name__ == '__main__':
+    num_classes = 309
+    inputs = torch.rand(3, 80, 848)
+    conv_layers = [64, 64, 'MP', 128, 128, 'MP', 256, 256, 256, 'MP', 512, 512, 512, 'MP', 512, 512, 512]
+    # conv_layers = [64, 'MP', 128, 'MP', 256, 256, 'MP', 512, 512, 'MP']
+    model = VGGishish(conv_layers, use_bn=False, num_classes=num_classes)
+    outputs = model(inputs)
+    print(outputs.shape)
diff --git a/specvqgan/modules/losses/vggishish/predict.py b/specvqgan/modules/losses/vggishish/predict.py
new file mode 100644
index 0000000..e9d13f3
--- /dev/null
+++ b/specvqgan/modules/losses/vggishish/predict.py
@@ -0,0 +1,90 @@
+import os
+from torch.utils.data import DataLoader
+import torchvision
+from tqdm import tqdm
+from dataset import VGGSound
+import torch
+import torch.nn as nn
+from metrics import metrics
+from omegaconf import OmegaConf
+from model import VGGishish
+from transforms import Crop, StandardNormalizeAudio, ToTensor
+
+
+if __name__ == '__main__':
+    cfg_cli = OmegaConf.from_cli()
+    print(cfg_cli.config)
+    cfg_yml = OmegaConf.load(cfg_cli.config)
+    # the latter arguments are prioritized
+    cfg = OmegaConf.merge(cfg_yml, cfg_cli)
+    OmegaConf.set_readonly(cfg, True)
+    print(OmegaConf.to_yaml(cfg))
+
+    # logger = LoggerWithTBoard(cfg)
+    transforms = [
+        StandardNormalizeAudio(cfg.mels_path),
+        ToTensor(),
+    ]
+    if cfg.cropped_size not in [None, 'None', 'none']:
+        transforms.append(Crop(cfg.cropped_size))
+    transforms = torchvision.transforms.transforms.Compose(transforms)
+
+    datasets = {
+        'test': VGGSound('test', cfg.mels_path, transforms),
+    }
+
+    loaders = {
+        'test': DataLoader(datasets['test'], batch_size=cfg.batch_size,
+                           num_workers=cfg.num_workers, pin_memory=True)
+    }
+
+    device = torch.device(cfg.device if torch.cuda.is_available() else 'cpu')
+    model = VGGishish(cfg.conv_layers, cfg.use_bn, num_classes=len(datasets['test'].target2label))
+    model = model.to(device)
+
+    optimizer = torch.optim.Adam(model.parameters(), lr=cfg.learning_rate)
+    criterion = nn.CrossEntropyLoss()
+
+    # loading the best model
+    folder_name = os.path.split(cfg.config)[0].split('/')[-1]
+    print(folder_name)
+    ckpt = torch.load(f'./logs/{folder_name}/vggishish-{folder_name}.pt', map_location='cpu')
+    model.load_state_dict(ckpt['model'])
+    print((f'The model was trained for {ckpt["epoch"]} epochs. Loss: {ckpt["loss"]:.4f}'))
+
+    # Testing the model
+    model.eval()
+    running_loss = 0
+    preds_from_each_batch = []
+    targets_from_each_batch = []
+
+    for i, batch in enumerate(tqdm(loaders['test'])):
+        inputs = batch['input'].to(device)
+        targets = batch['target'].to(device)
+
+        # zero the parameter gradients
+        optimizer.zero_grad()
+
+        # forward + backward + optimize
+        with torch.set_grad_enabled(False):
+            outputs = model(inputs)
+            loss = criterion(outputs, targets)
+
+        # loss
+        running_loss += loss.item()
+
+        # for metrics calculation later on
+        preds_from_each_batch += [outputs.detach().cpu()]
+        targets_from_each_batch += [targets.cpu()]
+
+    # logging metrics
+    preds_from_each_batch = torch.cat(preds_from_each_batch)
+    targets_from_each_batch = torch.cat(targets_from_each_batch)
+    test_metrics_dict = metrics(targets_from_each_batch, preds_from_each_batch)
+    test_metrics_dict['avg_loss'] = running_loss / len(loaders['test'])
+    test_metrics_dict['param_num'] = sum(p.numel() for p in model.parameters() if p.requires_grad)
+
+    # TODO: I have no idea why tboard doesn't keep metrics (hparams) in a tensorboard when
+    # I run this experiment from cli: `python main.py config=./configs/vggish.yaml`
+    # while when I run it in vscode debugger the metrics are present in the tboard (weird)
+    print(test_metrics_dict)
diff --git a/specvqgan/modules/losses/vggishish/train_melception.py b/specvqgan/modules/losses/vggishish/train_melception.py
new file mode 100644
index 0000000..8adc5aa
--- /dev/null
+++ b/specvqgan/modules/losses/vggishish/train_melception.py
@@ -0,0 +1,241 @@
+import random
+
+import numpy as np
+import torch
+import torchvision
+from omegaconf import OmegaConf
+from torch.utils.data.dataloader import DataLoader
+from torchvision.models.inception import BasicConv2d, Inception3
+from tqdm import tqdm
+
+from dataset import VGGSound
+from logger import LoggerWithTBoard
+from loss import WeightedCrossEntropy
+from metrics import metrics
+from transforms import Crop, StandardNormalizeAudio, ToTensor
+
+
+# TODO: refactor  ./evaluation/feature_extractors/melception.py to handle this class as well.
+# So far couldn't do it because of the difference in outputs
+class Melception(Inception3):
+
+    def __init__(self, num_classes, **kwargs):
+        # inception = Melception(num_classes=309)
+        super().__init__(num_classes=num_classes, **kwargs)
+        # the same as https://github.com/pytorch/vision/blob/5339e63148/torchvision/models/inception.py#L95
+        # but for 1-channel input instead of RGB.
+        self.Conv2d_1a_3x3 = BasicConv2d(1, 32, kernel_size=3, stride=2)
+        # also the 'hight' of the mel spec is 80 (vs 299 in RGB) we remove all max pool from Inception
+        self.maxpool1 = torch.nn.Identity()
+        self.maxpool2 = torch.nn.Identity()
+
+    def forward(self, x):
+        x = x.unsqueeze(1)
+        return super().forward(x)
+
+def train_inception_scorer(cfg):
+    logger = LoggerWithTBoard(cfg)
+
+    random.seed(cfg.seed)
+    np.random.seed(cfg.seed)
+    torch.manual_seed(cfg.seed)
+    torch.cuda.manual_seed_all(cfg.seed)
+    # makes iterations faster (in this case 30%) if your inputs are of a fixed size
+    # https://discuss.pytorch.org/t/what-does-torch-backends-cudnn-benchmark-do/5936/3
+    torch.backends.cudnn.benchmark = True
+
+    meta_path = './data/vggsound.csv'
+    train_ids_path = './data/vggsound_train.txt'
+    cache_path = './data/'
+    splits_path = cache_path
+
+    transforms = [
+        StandardNormalizeAudio(cfg.mels_path, train_ids_path, cache_path),
+    ]
+    if cfg.cropped_size not in [None, 'None', 'none']:
+        logger.print_logger.info(f'Using cropping {cfg.cropped_size}')
+        transforms.append(Crop(cfg.cropped_size))
+    transforms.append(ToTensor())
+    transforms = torchvision.transforms.transforms.Compose(transforms)
+
+    datasets = {
+        'train': VGGSound('train', cfg.mels_path, transforms, splits_path, meta_path),
+        'valid': VGGSound('valid', cfg.mels_path, transforms, splits_path, meta_path),
+        'test': VGGSound('test', cfg.mels_path, transforms, splits_path, meta_path),
+    }
+
+    loaders = {
+        'train': DataLoader(datasets['train'], batch_size=cfg.batch_size, shuffle=True, drop_last=True,
+                            num_workers=cfg.num_workers, pin_memory=True),
+        'valid': DataLoader(datasets['valid'], batch_size=cfg.batch_size,
+                            num_workers=cfg.num_workers, pin_memory=True),
+        'test': DataLoader(datasets['test'], batch_size=cfg.batch_size,
+                           num_workers=cfg.num_workers, pin_memory=True),
+    }
+
+    device = torch.device(cfg.device if torch.cuda.is_available() else 'cpu')
+
+    model = Melception(num_classes=len(datasets['train'].target2label))
+    model = model.to(device)
+    param_num = logger.log_param_num(model)
+
+    if cfg.optimizer == 'adam':
+        optimizer = torch.optim.Adam(
+            model.parameters(), lr=cfg.learning_rate, betas=cfg.betas, weight_decay=cfg.weight_decay)
+    elif cfg.optimizer == 'sgd':
+        optimizer = torch.optim.SGD(
+            model.parameters(), lr=cfg.learning_rate, momentum=cfg.momentum, weight_decay=cfg.weight_decay)
+    else:
+        raise NotImplementedError
+
+    if cfg.cls_weights_in_loss:
+        weights = 1 / datasets['train'].class_counts
+    else:
+        weights = torch.ones(len(datasets['train'].target2label))
+    criterion = WeightedCrossEntropy(weights.to(device))
+
+    # loop over the train and validation multiple times (typical PT boilerplate)
+    no_change_epochs = 0
+    best_valid_loss = float('inf')
+    early_stop_triggered = False
+
+    for epoch in range(cfg.num_epochs):
+
+        for phase in ['train', 'valid']:
+            if phase == 'train':
+                model.train()
+            else:
+                model.eval()
+
+            running_loss = 0
+            preds_from_each_batch = []
+            targets_from_each_batch = []
+
+            prog_bar = tqdm(loaders[phase], f'{phase} ({epoch})', ncols=0)
+            for i, batch in enumerate(prog_bar):
+                inputs = batch['input'].to(device)
+                targets = batch['target'].to(device)
+
+                # zero the parameter gradients
+                optimizer.zero_grad()
+
+                # forward + backward + optimize
+                with torch.set_grad_enabled(phase == 'train'):
+                    # inception v3
+                    if phase == 'train':
+                        outputs, aux_outputs = model(inputs)
+                        loss1 = criterion(outputs, targets)
+                        loss2 = criterion(aux_outputs, targets)
+                        loss = loss1 + 0.4*loss2
+                        loss = criterion(outputs, targets, to_weight=True)
+                    else:
+                        outputs = model(inputs)
+                        loss = criterion(outputs, targets, to_weight=False)
+
+                if phase == 'train':
+                    loss.backward()
+                    optimizer.step()
+
+                # loss
+                running_loss += loss.item()
+
+                # for metrics calculation later on
+                preds_from_each_batch += [outputs.detach().cpu()]
+                targets_from_each_batch += [targets.cpu()]
+
+                # iter logging
+                if i % 50 == 0:
+                    logger.log_iter_loss(loss.item(), epoch*len(loaders[phase])+i, phase)
+                    # tracks loss in the tqdm progress bar
+                    prog_bar.set_postfix(loss=loss.item())
+
+            # logging loss
+            epoch_loss = running_loss / len(loaders[phase])
+            logger.log_epoch_loss(epoch_loss, epoch, phase)
+
+            # logging metrics
+            preds_from_each_batch = torch.cat(preds_from_each_batch)
+            targets_from_each_batch = torch.cat(targets_from_each_batch)
+            metrics_dict = metrics(targets_from_each_batch, preds_from_each_batch)
+            logger.log_epoch_metrics(metrics_dict, epoch, phase)
+
+            # Early stopping
+            if phase == 'valid':
+                if epoch_loss < best_valid_loss:
+                    no_change_epochs = 0
+                    best_valid_loss = epoch_loss
+                    logger.log_best_model(model, epoch_loss, epoch, optimizer, metrics_dict)
+                else:
+                    no_change_epochs += 1
+                    logger.print_logger.info(
+                        f'Valid loss hasnt changed for {no_change_epochs} patience: {cfg.patience}'
+                    )
+                    if no_change_epochs >= cfg.patience:
+                        early_stop_triggered = True
+
+        if early_stop_triggered:
+            logger.print_logger.info(f'Training is early stopped @ {epoch}')
+            break
+
+    logger.print_logger.info('Finished Training')
+
+    # loading the best model
+    ckpt = torch.load(logger.best_model_path)
+    model.load_state_dict(ckpt['model'])
+    logger.print_logger.info(f'Loading the best model from {logger.best_model_path}')
+    logger.print_logger.info((f'The model was trained for {ckpt["epoch"]} epochs. Loss: {ckpt["loss"]:.4f}'))
+
+    # Testing the model
+    model.eval()
+    running_loss = 0
+    preds_from_each_batch = []
+    targets_from_each_batch = []
+
+    for i, batch in enumerate(loaders['test']):
+        inputs = batch['input'].to(device)
+        targets = batch['target'].to(device)
+
+        # zero the parameter gradients
+        optimizer.zero_grad()
+
+        # forward + backward + optimize
+        with torch.set_grad_enabled(False):
+            outputs = model(inputs)
+            loss = criterion(outputs, targets, to_weight=False)
+
+        # loss
+        running_loss += loss.item()
+
+        # for metrics calculation later on
+        preds_from_each_batch += [outputs.detach().cpu()]
+        targets_from_each_batch += [targets.cpu()]
+
+    # logging metrics
+    preds_from_each_batch = torch.cat(preds_from_each_batch)
+    targets_from_each_batch = torch.cat(targets_from_each_batch)
+    test_metrics_dict = metrics(targets_from_each_batch, preds_from_each_batch)
+    test_metrics_dict['avg_loss'] = running_loss / len(loaders['test'])
+    test_metrics_dict['param_num'] = param_num
+    # TODO: I have no idea why tboard doesn't keep metrics (hparams) when
+    # I run this experiment from cli: `python train_melception.py config=./configs/vggish.yaml`
+    # while when I run it in vscode debugger the metrics are logger (wtf)
+    logger.log_test_metrics(test_metrics_dict, dict(cfg), ckpt['epoch'])
+
+    logger.print_logger.info('Finished the experiment')
+
+
+if __name__ == '__main__':
+    # input = torch.rand(16, 1, 80, 848)
+    # output, aux = inception(input)
+    # print(output.shape, aux.shape)
+    # Expected input size: (3, 299, 299) in RGB -> (1, 80, 848) in Mel Spec
+    # train_inception_scorer()
+
+    cfg_cli = OmegaConf.from_cli()
+    cfg_yml = OmegaConf.load(cfg_cli.config)
+    # the latter arguments are prioritized
+    cfg = OmegaConf.merge(cfg_yml, cfg_cli)
+    OmegaConf.set_readonly(cfg, True)
+    print(OmegaConf.to_yaml(cfg))
+
+    train_inception_scorer(cfg)
diff --git a/specvqgan/modules/losses/vggishish/train_vggishish.py b/specvqgan/modules/losses/vggishish/train_vggishish.py
new file mode 100644
index 0000000..2056682
--- /dev/null
+++ b/specvqgan/modules/losses/vggishish/train_vggishish.py
@@ -0,0 +1,199 @@
+from loss import WeightedCrossEntropy
+import random
+
+import numpy as np
+import torch
+import torchvision
+from omegaconf import OmegaConf
+from torch.utils.data.dataloader import DataLoader
+from tqdm import tqdm
+
+from dataset import VGGSound
+from transforms import Crop, StandardNormalizeAudio, ToTensor
+from logger import LoggerWithTBoard
+from metrics import metrics
+from model import VGGishish
+
+if __name__ == "__main__":
+    cfg_cli = OmegaConf.from_cli()
+    cfg_yml = OmegaConf.load(cfg_cli.config)
+    # the latter arguments are prioritized
+    cfg = OmegaConf.merge(cfg_yml, cfg_cli)
+    OmegaConf.set_readonly(cfg, True)
+    print(OmegaConf.to_yaml(cfg))
+
+    logger = LoggerWithTBoard(cfg)
+
+    random.seed(cfg.seed)
+    np.random.seed(cfg.seed)
+    torch.manual_seed(cfg.seed)
+    torch.cuda.manual_seed_all(cfg.seed)
+    # makes iterations faster (in this case 30%) if your inputs are of a fixed size
+    # https://discuss.pytorch.org/t/what-does-torch-backends-cudnn-benchmark-do/5936/3
+    torch.backends.cudnn.benchmark = True
+
+    transforms = [
+        StandardNormalizeAudio(cfg.mels_path),
+    ]
+    if cfg.cropped_size not in [None, 'None', 'none']:
+        logger.print_logger.info(f'Using cropping {cfg.cropped_size}')
+        transforms.append(Crop(cfg.cropped_size))
+    transforms.append(ToTensor())
+    transforms = torchvision.transforms.transforms.Compose(transforms)
+
+    datasets = {
+        'train': VGGSound('train', cfg.mels_path, transforms),
+        'valid': VGGSound('valid', cfg.mels_path, transforms),
+        'test': VGGSound('test', cfg.mels_path, transforms),
+    }
+
+    loaders = {
+        'train': DataLoader(datasets['train'], batch_size=cfg.batch_size, shuffle=True, drop_last=True,
+                            num_workers=cfg.num_workers, pin_memory=True),
+        'valid': DataLoader(datasets['valid'], batch_size=cfg.batch_size,
+                            num_workers=cfg.num_workers, pin_memory=True),
+        'test': DataLoader(datasets['test'], batch_size=cfg.batch_size,
+                           num_workers=cfg.num_workers, pin_memory=True),
+    }
+
+    device = torch.device(cfg.device if torch.cuda.is_available() else 'cpu')
+
+    model = VGGishish(cfg.conv_layers, cfg.use_bn, num_classes=len(datasets['train'].target2label))
+    model = model.to(device)
+    param_num = logger.log_param_num(model)
+
+    if cfg.optimizer == 'adam':
+        optimizer = torch.optim.Adam(
+            model.parameters(), lr=cfg.learning_rate, betas=cfg.betas, weight_decay=cfg.weight_decay)
+    elif cfg.optimizer == 'sgd':
+        optimizer = torch.optim.SGD(
+            model.parameters(), lr=cfg.learning_rate, momentum=cfg.momentum, weight_decay=cfg.weight_decay)
+    else:
+        raise NotImplementedError
+
+    if cfg.cls_weights_in_loss:
+        weights = 1 / datasets['train'].class_counts
+    else:
+        weights = torch.ones(len(datasets['train'].target2label))
+    criterion = WeightedCrossEntropy(weights.to(device))
+
+    # loop over the train and validation multiple times (typical PT boilerplate)
+    no_change_epochs = 0
+    best_valid_loss = float('inf')
+    early_stop_triggered = False
+
+    for epoch in range(cfg.num_epochs):
+
+        for phase in ['train', 'valid']:
+            if phase == 'train':
+                model.train()
+            else:
+                model.eval()
+
+            running_loss = 0
+            preds_from_each_batch = []
+            targets_from_each_batch = []
+
+            prog_bar = tqdm(loaders[phase], f'{phase} ({epoch})', ncols=0)
+            for i, batch in enumerate(prog_bar):
+                inputs = batch['input'].to(device)
+                targets = batch['target'].to(device)
+
+                # zero the parameter gradients
+                optimizer.zero_grad()
+
+                # forward + backward + optimize
+                with torch.set_grad_enabled(phase == 'train'):
+                    outputs = model(inputs)
+                    loss = criterion(outputs, targets, to_weight=phase == 'train')
+
+                if phase == 'train':
+                    loss.backward()
+                    optimizer.step()
+
+                # loss
+                running_loss += loss.item()
+
+                # for metrics calculation later on
+                preds_from_each_batch += [outputs.detach().cpu()]
+                targets_from_each_batch += [targets.cpu()]
+
+                # iter logging
+                if i % 50 == 0:
+                    logger.log_iter_loss(loss.item(), epoch*len(loaders[phase])+i, phase)
+                    # tracks loss in the tqdm progress bar
+                    prog_bar.set_postfix(loss=loss.item())
+
+            # logging loss
+            epoch_loss = running_loss / len(loaders[phase])
+            logger.log_epoch_loss(epoch_loss, epoch, phase)
+
+            # logging metrics
+            preds_from_each_batch = torch.cat(preds_from_each_batch)
+            targets_from_each_batch = torch.cat(targets_from_each_batch)
+            metrics_dict = metrics(targets_from_each_batch, preds_from_each_batch)
+            logger.log_epoch_metrics(metrics_dict, epoch, phase)
+
+            # Early stopping
+            if phase == 'valid':
+                if epoch_loss < best_valid_loss:
+                    no_change_epochs = 0
+                    best_valid_loss = epoch_loss
+                    logger.log_best_model(model, epoch_loss, epoch, optimizer, metrics_dict)
+                else:
+                    no_change_epochs += 1
+                    logger.print_logger.info(
+                        f'Valid loss hasnt changed for {no_change_epochs} patience: {cfg.patience}'
+                    )
+                    if no_change_epochs >= cfg.patience:
+                        early_stop_triggered = True
+
+        if early_stop_triggered:
+            logger.print_logger.info(f'Training is early stopped @ {epoch}')
+            break
+
+    logger.print_logger.info('Finished Training')
+
+    # loading the best model
+    ckpt = torch.load(logger.best_model_path)
+    model.load_state_dict(ckpt['model'])
+    logger.print_logger.info(f'Loading the best model from {logger.best_model_path}')
+    logger.print_logger.info((f'The model was trained for {ckpt["epoch"]} epochs. Loss: {ckpt["loss"]:.4f}'))
+
+    # Testing the model
+    model.eval()
+    running_loss = 0
+    preds_from_each_batch = []
+    targets_from_each_batch = []
+
+    for i, batch in enumerate(loaders['test']):
+        inputs = batch['input'].to(device)
+        targets = batch['target'].to(device)
+
+        # zero the parameter gradients
+        optimizer.zero_grad()
+
+        # forward + backward + optimize
+        with torch.set_grad_enabled(False):
+            outputs = model(inputs)
+            loss = criterion(outputs, targets, to_weight=False)
+
+        # loss
+        running_loss += loss.item()
+
+        # for metrics calculation later on
+        preds_from_each_batch += [outputs.detach().cpu()]
+        targets_from_each_batch += [targets.cpu()]
+
+    # logging metrics
+    preds_from_each_batch = torch.cat(preds_from_each_batch)
+    targets_from_each_batch = torch.cat(targets_from_each_batch)
+    test_metrics_dict = metrics(targets_from_each_batch, preds_from_each_batch)
+    test_metrics_dict['avg_loss'] = running_loss / len(loaders['test'])
+    test_metrics_dict['param_num'] = param_num
+    # TODO: I have no idea why tboard doesn't keep metrics (hparams) when
+    # I run this experiment from cli: `python train_vggishish.py config=./configs/vggish.yaml`
+    # while when I run it in vscode debugger the metrics are logger (wtf)
+    logger.log_test_metrics(test_metrics_dict, dict(cfg), ckpt['epoch'])
+
+    logger.print_logger.info('Finished the experiment')
diff --git a/specvqgan/modules/losses/vggishish/transforms.py b/specvqgan/modules/losses/vggishish/transforms.py
new file mode 100644
index 0000000..bdab7eb
--- /dev/null
+++ b/specvqgan/modules/losses/vggishish/transforms.py
@@ -0,0 +1,98 @@
+import logging
+import os
+from pathlib import Path
+
+import albumentations
+import numpy as np
+import torch
+from tqdm import tqdm
+
+logger = logging.getLogger(f'main.{__name__}')
+
+
+class StandardNormalizeAudio(object):
+    '''
+        Frequency-wise normalization
+    '''
+    def __init__(self, specs_dir, train_ids_path='./data/vggsound_train.txt', cache_path='./data/'):
+        self.specs_dir = specs_dir
+        self.train_ids_path = train_ids_path
+        # making the stats filename to match the specs dir name
+        self.cache_path = os.path.join(cache_path, f'train_means_stds_{Path(specs_dir).stem}.txt')
+        logger.info('Assuming that the input stats are calculated using preprocessed spectrograms (log)')
+        self.train_stats = self.calculate_or_load_stats()
+
+    def __call__(self, item):
+        # just to generalizat the input handling. Useful for FID, IS eval and training other staff
+        if isinstance(item, dict):
+            if 'input' in item:
+                input_key = 'input'
+            elif 'image' in item:
+                input_key = 'image'
+            else:
+                raise NotImplementedError
+            item[input_key] = (item[input_key] - self.train_stats['means']) / self.train_stats['stds']
+        elif isinstance(item, torch.Tensor):
+            # broadcasts np.ndarray (80, 1) to (1, 80, 1) because item is torch.Tensor (B, 80, T)
+            item = (item - self.train_stats['means']) / self.train_stats['stds']
+        else:
+            raise NotImplementedError
+        return item
+
+    def calculate_or_load_stats(self):
+        try:
+            # (F, 2)
+            train_stats = np.loadtxt(self.cache_path)
+            means, stds = train_stats.T
+            logger.info('Trying to load train stats for Standard Normalization of inputs')
+        except OSError:
+            logger.info('Could not find the precalculated stats for Standard Normalization. Calculating...')
+            train_vid_ids = open(self.train_ids_path)
+            specs_paths = [os.path.join(self.specs_dir, f'{i.rstrip()}_mel.npy') for i in train_vid_ids]
+            means = [None] * len(specs_paths)
+            stds = [None] * len(specs_paths)
+            for i, path in enumerate(tqdm(specs_paths)):
+                spec = np.load(path)
+                means[i] = spec.mean(axis=1)
+                stds[i] = spec.std(axis=1)
+            # (F) <- (num_files, F)
+            means = np.array(means).mean(axis=0)
+            stds = np.array(stds).mean(axis=0)
+            # saving in two columns
+            np.savetxt(self.cache_path, np.vstack([means, stds]).T, fmt='%0.8f')
+        means = means.reshape(-1, 1)
+        stds = stds.reshape(-1, 1)
+        return {'means': means, 'stds': stds}
+
+class ToTensor(object):
+
+    def __call__(self, item):
+        item['input'] = torch.from_numpy(item['input']).float()
+        # if 'target' in item:
+        item['target'] = torch.tensor(item['target'])
+        return item
+
+class Crop(object):
+
+    def __init__(self, cropped_shape=None, random_crop=False):
+        self.cropped_shape = cropped_shape
+        if cropped_shape is not None:
+            mel_num, spec_len = cropped_shape
+            if random_crop:
+                self.cropper = albumentations.RandomCrop
+            else:
+                self.cropper = albumentations.CenterCrop
+            self.preprocessor = albumentations.Compose([self.cropper(mel_num, spec_len)])
+        else:
+            self.preprocessor = lambda **kwargs: kwargs
+
+    def __call__(self, item):
+        item['input'] = self.preprocessor(image=item['input'])['image']
+        return item
+
+
+if __name__ == '__main__':
+    cropper = Crop([80, 848])
+    item = {'input': torch.rand([80, 860])}
+    outputs = cropper(item)
+    print(outputs['input'].shape)
diff --git a/specvqgan/modules/losses/vqperceptual.py b/specvqgan/modules/losses/vqperceptual.py
new file mode 100644
index 0000000..f0ceaf2
--- /dev/null
+++ b/specvqgan/modules/losses/vqperceptual.py
@@ -0,0 +1,209 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import sys
+
+sys.path.insert(0, '.')  # nopep8
+from specvqgan.modules.discriminator.model import (NLayerDiscriminator, NLayerDiscriminator1dFeats,
+                                                   NLayerDiscriminator1dSpecs,
+                                                   weights_init)
+from specvqgan.modules.losses.lpaps import LPAPS
+
+
+class DummyLoss(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+
+def adopt_weight(weight, global_step, threshold=0, value=0.):
+    if global_step < threshold:
+        weight = value
+    return weight
+
+
+def hinge_d_loss(logits_real, logits_fake):
+    loss_real = torch.mean(F.relu(1. - logits_real))
+    loss_fake = torch.mean(F.relu(1. + logits_fake))
+    d_loss = 0.5 * (loss_real + loss_fake)
+    return d_loss
+
+
+def vanilla_d_loss(logits_real, logits_fake):
+    d_loss = 0.5 * (
+        torch.mean(torch.nn.functional.softplus(-logits_real)) +
+        torch.mean(torch.nn.functional.softplus(logits_fake)))
+    return d_loss
+
+
+class VQLPAPSWithDiscriminator(nn.Module):
+    def __init__(self, disc_start, codebook_weight=1.0, pixelloss_weight=1.0,
+                 disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0,
+                 perceptual_weight=1.0, use_actnorm=False, disc_conditional=False,
+                 disc_ndf=64, disc_loss="hinge", min_adapt_weight=0.0, max_adapt_weight=1e4):
+        super().__init__()
+        assert disc_loss in ["hinge", "vanilla"]
+        self.codebook_weight = codebook_weight
+        self.pixel_weight = pixelloss_weight
+        self.perceptual_loss = LPAPS().eval()
+        self.perceptual_weight = perceptual_weight
+
+        self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels,
+                                                 n_layers=disc_num_layers,
+                                                 use_actnorm=use_actnorm,
+                                                 ndf=disc_ndf
+                                                 ).apply(weights_init)
+        self.discriminator_iter_start = disc_start
+        if disc_loss == "hinge":
+            self.disc_loss = hinge_d_loss
+        elif disc_loss == "vanilla":
+            self.disc_loss = vanilla_d_loss
+        else:
+            raise ValueError(f"Unknown GAN loss '{disc_loss}'.")
+        print(f"VQLPAPSWithDiscriminator running with {disc_loss} loss.")
+        self.disc_factor = disc_factor
+        self.discriminator_weight = disc_weight
+        self.disc_conditional = disc_conditional
+        self.min_adapt_weight = min_adapt_weight
+        self.max_adapt_weight = max_adapt_weight
+
+    def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
+        if last_layer is not None:
+            nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]
+            g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
+        else:
+            nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0]
+            g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0]
+
+        d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
+        d_weight = torch.clamp(d_weight, self.min_adapt_weight, self.max_adapt_weight).detach()
+        d_weight = d_weight * self.discriminator_weight
+        return d_weight
+
+    def forward(self, codebook_loss, inputs, reconstructions, optimizer_idx,
+                global_step, last_layer=None, cond=None, split="train"):
+        rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
+        if self.perceptual_weight > 0:
+            p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous())
+            rec_loss = rec_loss + self.perceptual_weight * p_loss
+        else:
+            p_loss = torch.tensor([0.0])
+
+        nll_loss = rec_loss
+        # nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
+        nll_loss = torch.mean(nll_loss)
+
+        # now the GAN part
+        if optimizer_idx == 0:
+            # generator update
+            if cond is None:
+                assert not self.disc_conditional
+                logits_fake = self.discriminator(reconstructions.contiguous())
+            else:
+                assert self.disc_conditional
+                logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1))
+            g_loss = -torch.mean(logits_fake)
+
+            try:
+                d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer)
+            except RuntimeError:
+                assert not self.training
+                d_weight = torch.tensor(0.0)
+
+            disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
+            loss = nll_loss + d_weight * disc_factor * g_loss + self.codebook_weight * codebook_loss.mean()
+
+            log = {"{}/total_loss".format(split): loss.clone().detach().mean(),
+                   "{}/quant_loss".format(split): codebook_loss.detach().mean(),
+                   "{}/nll_loss".format(split): nll_loss.detach().mean(),
+                   "{}/rec_loss".format(split): rec_loss.detach().mean(),
+                   "{}/p_loss".format(split): p_loss.detach().mean(),
+                   "{}/d_weight".format(split): d_weight.detach(),
+                   "{}/disc_factor".format(split): torch.tensor(disc_factor),
+                   "{}/g_loss".format(split): g_loss.detach().mean(),
+                   }
+            return loss, log
+
+        if optimizer_idx == 1:
+            # second pass for discriminator update
+            if cond is None:
+                logits_real = self.discriminator(inputs.contiguous().detach())
+                logits_fake = self.discriminator(reconstructions.contiguous().detach())
+            else:
+                logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1))
+                logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1))
+
+            disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
+            d_loss = disc_factor * self.disc_loss(logits_real, logits_fake)
+
+            log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(),
+                   "{}/logits_real".format(split): logits_real.detach().mean(),
+                   "{}/logits_fake".format(split): logits_fake.detach().mean()
+                   }
+            return d_loss, log
+
+
+class VQLPAPSWithDiscriminator1dFeats(VQLPAPSWithDiscriminator):
+    def __init__(self, disc_start, codebook_weight=1.0, pixelloss_weight=1.0,
+                 disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0,
+                 perceptual_weight=1.0, use_actnorm=False, disc_conditional=False,
+                 disc_ndf=64, disc_loss="hinge", min_adapt_weight=0.0, max_adapt_weight=1e4):
+        super().__init__(disc_start=disc_start, codebook_weight=codebook_weight,
+                         pixelloss_weight=pixelloss_weight, disc_num_layers=disc_num_layers,
+                         disc_in_channels=disc_in_channels, disc_factor=disc_factor, disc_weight=disc_weight,
+                         perceptual_weight=perceptual_weight, use_actnorm=use_actnorm,
+                         disc_conditional=disc_conditional, disc_ndf=disc_ndf, disc_loss=disc_loss,
+                         min_adapt_weight=min_adapt_weight, max_adapt_weight=max_adapt_weight)
+
+        self.discriminator = NLayerDiscriminator1dFeats(input_nc=disc_in_channels, n_layers=disc_num_layers,
+                                                   use_actnorm=use_actnorm, ndf=disc_ndf).apply(weights_init)
+
+class VQLPAPSWithDiscriminator1dSpecs(VQLPAPSWithDiscriminator):
+    def __init__(self, disc_start, codebook_weight=1.0, pixelloss_weight=1.0,
+                 disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0,
+                 perceptual_weight=1.0, use_actnorm=False, disc_conditional=False,
+                 disc_ndf=64, disc_loss="hinge", min_adapt_weight=0.0, max_adapt_weight=1e4):
+        super().__init__(disc_start=disc_start, codebook_weight=codebook_weight,
+                         pixelloss_weight=pixelloss_weight, disc_num_layers=disc_num_layers,
+                         disc_in_channels=disc_in_channels, disc_factor=disc_factor, disc_weight=disc_weight,
+                         perceptual_weight=perceptual_weight, use_actnorm=use_actnorm,
+                         disc_conditional=disc_conditional, disc_ndf=disc_ndf, disc_loss=disc_loss,
+                         min_adapt_weight=min_adapt_weight, max_adapt_weight=max_adapt_weight)
+
+        self.discriminator = NLayerDiscriminator1dSpecs(input_nc=disc_in_channels, n_layers=disc_num_layers,
+                                                   use_actnorm=use_actnorm, ndf=disc_ndf).apply(weights_init)
+
+
+if __name__ == '__main__':
+    from specvqgan.modules.diffusionmodules.model import Decoder, Decoder1d
+
+    optimizer_idx = 0
+    loss_config = {
+        'disc_conditional': False,
+        'disc_start': 30001,
+        'disc_weight': 0.8,
+        'codebook_weight': 1.0,
+    }
+    ddconfig = {
+        'ch': 128,
+        'num_res_blocks': 2,
+        'dropout': 0.0,
+        'z_channels': 256,
+        'double_z': False,
+    }
+    qloss = torch.rand(1, requires_grad=True)
+
+    ## AUDIO
+    loss_config['disc_in_channels'] = 1
+    ddconfig['in_channels'] = 1
+    ddconfig['resolution'] = 848
+    ddconfig['attn_resolutions'] = [53]
+    ddconfig['out_ch'] = 1
+    ddconfig['ch_mult'] = [1, 1, 2, 2, 4]
+    decoder = Decoder(**ddconfig)
+    loss = VQLPAPSWithDiscriminator(**loss_config)
+    x = torch.rand(16, 1, 80, 848)
+    # subtracting something which uses dec_conv_out so that it will be in a graph
+    xrec = torch.rand(16, 1, 80, 848) - decoder.conv_out(torch.rand(16, 128, 80, 848)).mean()
+    aeloss, log_dict_ae = loss(qloss, x, xrec, optimizer_idx, global_step=0,last_layer=decoder.conv_out.weight)
+    print(aeloss)
+    print(log_dict_ae)
diff --git a/specvqgan/modules/misc/class_cond.py b/specvqgan/modules/misc/class_cond.py
new file mode 100644
index 0000000..e704457
--- /dev/null
+++ b/specvqgan/modules/misc/class_cond.py
@@ -0,0 +1,21 @@
+import torch
+
+class ClassOnlyStage(object):
+    def __init__(self):
+        pass
+
+    def eval(self):
+        return self
+
+    def encode(self, c):
+        """fake vqmodel interface because self.cond_stage_model should have something
+        similar to coord.py but even more `dummy`"""
+        # assert 0.0 <= c.min() and c.max() <= 1.0
+        info = None, None, c
+        return c, None, info
+
+    def decode(self, c):
+        return c
+
+    def get_input(self, batch, k):
+        return batch[k].unsqueeze(1).to(memory_format=torch.contiguous_format)
diff --git a/specvqgan/modules/misc/coord.py b/specvqgan/modules/misc/coord.py
new file mode 100644
index 0000000..ee69b0c
--- /dev/null
+++ b/specvqgan/modules/misc/coord.py
@@ -0,0 +1,31 @@
+import torch
+
+class CoordStage(object):
+    def __init__(self, n_embed, down_factor):
+        self.n_embed = n_embed
+        self.down_factor = down_factor
+
+    def eval(self):
+        return self
+
+    def encode(self, c):
+        """fake vqmodel interface"""
+        assert 0.0 <= c.min() and c.max() <= 1.0
+        b,ch,h,w = c.shape
+        assert ch == 1
+
+        c = torch.nn.functional.interpolate(c, scale_factor=1/self.down_factor,
+                                            mode="area")
+        c = c.clamp(0.0, 1.0)
+        c = self.n_embed*c
+        c_quant = c.round()
+        c_ind = c_quant.to(dtype=torch.long)
+
+        info = None, None, c_ind
+        return c_quant, None, info
+
+    def decode(self, c):
+        c = c/self.n_embed
+        c = torch.nn.functional.interpolate(c, scale_factor=self.down_factor,
+                                            mode="nearest")
+        return c
diff --git a/specvqgan/modules/misc/feat_cluster.py b/specvqgan/modules/misc/feat_cluster.py
new file mode 100644
index 0000000..47b0527
--- /dev/null
+++ b/specvqgan/modules/misc/feat_cluster.py
@@ -0,0 +1,83 @@
+import os
+from glob import glob
+
+import joblib
+import numpy as np
+import torch
+from sklearn.cluster import MiniBatchKMeans
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+from train import instantiate_from_config
+
+
+class FeatClusterStage(object):
+
+    def __init__(self, num_clusters=None, cached_kmeans_path=None, feats_dataset_config=None, num_workers=None):
+        if cached_kmeans_path is not None and os.path.exists(cached_kmeans_path):
+            print(f'Precalculated Clusterer already exists, loading from {cached_kmeans_path}')
+            self.clusterer = joblib.load(cached_kmeans_path)
+        elif feats_dataset_config is not None:
+            self.clusterer = self.load_or_precalculate_kmeans(num_clusters, feats_dataset_config, num_workers)
+        else:
+            raise Exception('Neither `feats_dataset_config` nor `cached_kmeans_path` are defined')
+
+    def eval(self):
+        return self
+
+    def encode(self, c):
+        # c_quant: cluster centers, c_ind: cluster index
+
+        B, D, T = c.shape
+        # (B*T, D) <- (B, T, D) <- (B, D, T)
+        c_flat = c.permute(0, 2, 1).view(B*T, D).cpu().numpy()
+
+        c_ind = self.clusterer.predict(c_flat)
+        c_quant = self.clusterer.cluster_centers_[c_ind]
+
+        c_ind = torch.from_numpy(c_ind).to(c.device)
+        c_quant = torch.from_numpy(c_quant).to(c.device)
+
+        c_ind = c_ind.long().unsqueeze(-1)
+        c_quant = c_quant.view(B, T, D).permute(0, 2, 1)
+
+        info = None, None, c_ind
+        # (B, D, T), (), ((), (768, 1024), (768, 1))
+        return c_quant, None, info
+
+    def decode(self, c):
+        return c
+
+    def get_input(self, batch, k):
+        x = batch[k]
+        x = x.permute(0, 2, 1).to(memory_format=torch.contiguous_format)
+        return x.float()
+
+    def load_or_precalculate_kmeans(self, num_clusters, dataset_cfg, num_workers):
+        print(f'Calculating clustering K={num_clusters}')
+        batch_size = 64
+        dataset_name = dataset_cfg.target.split('.')[-1]
+        cached_path = os.path.join('./specvqgan/modules/misc/', f'kmeans_K{num_clusters}_{dataset_name}.sklearn')
+        feat_depth = dataset_cfg.params.condition_dataset_cfg.feat_depth
+        feat_crop_len = dataset_cfg.params.condition_dataset_cfg.feat_crop_len
+
+        feat_loading_dset = instantiate_from_config(dataset_cfg)
+        feat_loading_dset = DataLoader(feat_loading_dset, batch_size, num_workers=num_workers, shuffle=True)
+
+        clusterer = MiniBatchKMeans(num_clusters, batch_size=batch_size*feat_crop_len, random_state=0)
+
+        for item in tqdm(feat_loading_dset):
+            batch = item['feature'].reshape(-1, feat_depth).float().numpy()
+            clusterer.partial_fit(batch)
+
+        joblib.dump(clusterer, cached_path)
+        print(f'Saved the calculated Clusterer @ {cached_path}')
+        return clusterer
+
+
+if __name__ == '__main__':
+    from omegaconf import OmegaConf
+
+    config = OmegaConf.load('./configs/vggsound_featcluster_transformer.yaml')
+    config.model.params.first_stage_config.params.ckpt_path = './logs/2021-05-19T22-16-54_vggsound_specs_vqgan/checkpoints/epoch_39.ckpt'
+    model = instantiate_from_config(config.model.params.cond_stage_config)
+    print(model)
diff --git a/specvqgan/modules/misc/feats_class.py b/specvqgan/modules/misc/feats_class.py
new file mode 100644
index 0000000..7298097
--- /dev/null
+++ b/specvqgan/modules/misc/feats_class.py
@@ -0,0 +1,28 @@
+import torch
+
+class FeatsClassStage(object):
+    def __init__(self):
+        pass
+
+    def eval(self):
+        return self
+
+    def encode(self, c):
+        """fake vqmodel interface because self.cond_stage_model should have something
+        similar to coord.py but even more `dummy`"""
+        # assert 0.0 <= c.min() and c.max() <= 1.0
+        info = None, None, c
+        return c, None, info
+
+    def decode(self, c):
+        return c
+
+    def get_input(self, batch: dict, keys: dict) -> dict:
+        out = {}
+        for k in keys:
+            if k == 'target':
+                out[k] = batch[k].unsqueeze(1)
+            elif k == 'feature':
+                out[k] = batch[k].float().permute(0, 2, 1)
+            out[k] = out[k].to(memory_format=torch.contiguous_format)
+        return out
diff --git a/specvqgan/modules/misc/kmeans_K1024_VGGSoundFeatsTrain.sklearn b/specvqgan/modules/misc/kmeans_K1024_VGGSoundFeatsTrain.sklearn
new file mode 100644
index 0000000..651bd08
Binary files /dev/null and b/specvqgan/modules/misc/kmeans_K1024_VGGSoundFeatsTrain.sklearn differ
diff --git a/specvqgan/modules/misc/raw_feats.py b/specvqgan/modules/misc/raw_feats.py
new file mode 100644
index 0000000..96b13f2
--- /dev/null
+++ b/specvqgan/modules/misc/raw_feats.py
@@ -0,0 +1,23 @@
+import torch
+
+class RawFeatsStage(object):
+    def __init__(self):
+        pass
+
+    def eval(self):
+        return self
+
+    def encode(self, c):
+        """fake vqmodel interface because self.cond_stage_model should have something
+        similar to coord.py but even more `dummy`"""
+        # assert 0.0 <= c.min() and c.max() <= 1.0
+        info = None, None, c
+        return c, None, info
+
+    def decode(self, c):
+        return c
+
+    def get_input(self, batch, k):
+        x = batch[k]
+        x = x.permute(0, 2, 1).to(memory_format=torch.contiguous_format)
+        return x.float()
diff --git a/specvqgan/modules/transformer/mingpt.py b/specvqgan/modules/transformer/mingpt.py
new file mode 100644
index 0000000..1b83aa7
--- /dev/null
+++ b/specvqgan/modules/transformer/mingpt.py
@@ -0,0 +1,478 @@
+"""
+taken from: https://github.com/karpathy/minGPT/
+GPT model:
+- the initial stem consists of a combination of token encoding and a positional encoding
+- the meat of it is a uniform sequence of Transformer blocks
+    - each Transformer is a sequential combination of a 1-hidden-layer MLP block and a self-attention block
+    - all blocks feed into a central residual pathway similar to resnets
+- the final decoder is a linear projection into a vanilla Softmax classifier
+"""
+
+import math
+import logging
+
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+import sys
+sys.path.insert(0, '.')  # nopep8
+from train import instantiate_from_config
+
+logger = logging.getLogger(__name__)
+
+
+class GPTConfig:
+    """ base GPT config, params common to all GPT versions """
+    embd_pdrop = 0.1
+    resid_pdrop = 0.1
+    attn_pdrop = 0.1
+
+    def __init__(self, vocab_size, block_size, **kwargs):
+        self.vocab_size = vocab_size
+        self.block_size = block_size
+        for k,v in kwargs.items():
+            setattr(self, k, v)
+
+
+class GPT1Config(GPTConfig):
+    """ GPT-1 like network roughly 125M params """
+    n_layer = 12
+    n_head = 12
+    n_embd = 768
+
+
+class GPT2Config(GPTConfig):
+    """ GPT-2 like network roughly 1.5B params """
+    # TODO
+
+
+class CausalSelfAttention(nn.Module):
+    """
+    A vanilla multi-head masked self-attention layer with a projection at the end.
+    It is possible to use torch.nn.MultiheadAttention here but I am including an
+    explicit implementation here to show that there is nothing too scary here.
+    """
+
+    def __init__(self, config):
+        super().__init__()
+        assert config.n_embd % config.n_head == 0
+        # key, query, value projections for all heads
+        self.key = nn.Linear(config.n_embd, config.n_embd)
+        self.query = nn.Linear(config.n_embd, config.n_embd)
+        self.value = nn.Linear(config.n_embd, config.n_embd)
+        # regularization
+        self.attn_drop = nn.Dropout(config.attn_pdrop)
+        self.resid_drop = nn.Dropout(config.resid_pdrop)
+        # output projection
+        self.proj = nn.Linear(config.n_embd, config.n_embd)
+        # causal mask to ensure that attention is only applied to the left in the input sequence
+        mask = torch.tril(torch.ones(config.block_size,
+                                     config.block_size))
+        if hasattr(config, "n_unmasked"):
+            mask[:config.n_unmasked, :config.n_unmasked] = 1
+        self.register_buffer("mask", mask.view(1, 1, config.block_size, config.block_size))
+        self.n_head = config.n_head
+
+    def forward(self, x, layer_past=None):
+        B, T, C = x.size()
+
+        # calculate query, key, values for all heads in batch and move head forward to be the batch dim
+        k = self.key(x).view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
+        q = self.query(x).view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
+        v = self.value(x).view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
+
+        # causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T)
+        att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
+        att = att.masked_fill(self.mask[:,:,:T,:T] == 0, float('-inf'))
+        att = F.softmax(att, dim=-1)
+        y = self.attn_drop(att) @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
+        y = y.transpose(1, 2).contiguous().view(B, T, C) # re-assemble all head outputs side by side
+
+        # output projection
+        y = self.resid_drop(self.proj(y))
+
+        return y, att
+
+
+class Block(nn.Module):
+    """ an unassuming Transformer block """
+    def __init__(self, config):
+        super().__init__()
+        self.ln1 = nn.LayerNorm(config.n_embd)
+        self.ln2 = nn.LayerNorm(config.n_embd)
+        self.attn = CausalSelfAttention(config)
+        self.mlp = nn.Sequential(
+            nn.Linear(config.n_embd, 4 * config.n_embd),
+            nn.GELU(),  # nice
+            nn.Linear(4 * config.n_embd, config.n_embd),
+            nn.Dropout(config.resid_pdrop),
+        )
+
+    def forward(self, x):
+        # x = x + self.attn(self.ln1(x))
+
+        # x is a tuple (x, attention)
+        x, _ = x
+        res = x
+        x = self.ln1(x)
+        x, att = self.attn(x)
+        x = res + x
+
+        x = x + self.mlp(self.ln2(x))
+
+        return x, att
+
+
+class GPT(nn.Module):
+    """  the full GPT language model, with a context size of block_size """
+    def __init__(self, vocab_size, block_size, n_layer=12, n_head=8, n_embd=256,
+                 embd_pdrop=0., resid_pdrop=0., attn_pdrop=0., n_unmasked=0):
+        super().__init__()
+        config = GPTConfig(vocab_size=vocab_size, block_size=block_size,
+                           embd_pdrop=embd_pdrop, resid_pdrop=resid_pdrop, attn_pdrop=attn_pdrop,
+                           n_layer=n_layer, n_head=n_head, n_embd=n_embd,
+                           n_unmasked=n_unmasked)
+        # input embedding stem
+        self.tok_emb = nn.Embedding(config.vocab_size, config.n_embd)
+        self.pos_emb = nn.Parameter(torch.zeros(1, config.block_size, config.n_embd))
+        self.drop = nn.Dropout(config.embd_pdrop)
+        # transformer
+        self.blocks = nn.Sequential(*[Block(config) for _ in range(config.n_layer)])
+        # decoder head
+        self.ln_f = nn.LayerNorm(config.n_embd)
+        self.head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+        self.block_size = config.block_size
+        self.apply(self._init_weights)
+        self.config = config
+        logger.info("number of parameters: %e", sum(p.numel() for p in self.parameters()))
+
+    def get_block_size(self):
+        return self.block_size
+
+    def _init_weights(self, module):
+        if isinstance(module, (nn.Linear, nn.Embedding)):
+            module.weight.data.normal_(mean=0.0, std=0.02)
+            if isinstance(module, nn.Linear) and module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+
+    def forward(self, idx, embeddings=None, targets=None):
+        # forward the GPT model
+        token_embeddings = self.tok_emb(idx)  # each index maps to a (learnable) vector
+
+        if embeddings is not None:  # prepend explicit embeddings
+            token_embeddings = torch.cat((embeddings, token_embeddings), dim=1)
+
+        t = token_embeddings.shape[1]
+        assert t <= self.block_size, "Cannot forward, model block size is exhausted."
+        position_embeddings = self.pos_emb[:, :t, :]  # each position maps to a (learnable) vector
+        x = self.drop(token_embeddings + position_embeddings)
+
+        # returns only last layer attention
+        # giving tuple (x, None) just because Sequential takes a single input but outputs two (x, atttention).
+        # att is (B, H, T, T)
+        x, att = self.blocks((x, None))
+        x = self.ln_f(x)
+        logits = self.head(x)
+
+        # if we are given some desired targets also calculate the loss
+        loss = None
+        if targets is not None:
+            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
+
+        return logits, loss, att
+
+
+class DummyGPT(nn.Module):
+    # for debugging
+    def __init__(self, add_value=1):
+        super().__init__()
+        self.add_value = add_value
+
+    def forward(self, idx):
+        raise NotImplementedError('Model should output attention')
+        return idx + self.add_value, None
+
+
+class CodeGPT(nn.Module):
+    """Takes in semi-embeddings"""
+    def __init__(self, vocab_size, block_size, in_channels, n_layer=12, n_head=8, n_embd=256,
+                 embd_pdrop=0., resid_pdrop=0., attn_pdrop=0., n_unmasked=0):
+        super().__init__()
+        config = GPTConfig(vocab_size=vocab_size, block_size=block_size,
+                           embd_pdrop=embd_pdrop, resid_pdrop=resid_pdrop, attn_pdrop=attn_pdrop,
+                           n_layer=n_layer, n_head=n_head, n_embd=n_embd,
+                           n_unmasked=n_unmasked)
+        # input embedding stem
+        self.tok_emb = nn.Linear(in_channels, config.n_embd)
+        self.pos_emb = nn.Parameter(torch.zeros(1, config.block_size, config.n_embd))
+        self.drop = nn.Dropout(config.embd_pdrop)
+        # transformer
+        self.blocks = nn.Sequential(*[Block(config) for _ in range(config.n_layer)])
+        # decoder head
+        self.ln_f = nn.LayerNorm(config.n_embd)
+        self.head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+        self.block_size = config.block_size
+        self.apply(self._init_weights)
+        self.config = config
+        logger.info("number of parameters: %e", sum(p.numel() for p in self.parameters()))
+
+    def get_block_size(self):
+        return self.block_size
+
+    def _init_weights(self, module):
+        if isinstance(module, (nn.Linear, nn.Embedding)):
+            module.weight.data.normal_(mean=0.0, std=0.02)
+            if isinstance(module, nn.Linear) and module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+        elif isinstance(module, (nn.Conv1d, nn.Conv2d)):
+            torch.nn.init.xavier_uniform(module.weight)
+            if module.bias is not None:
+                module.bias.data.fill_(0.01)
+
+    def forward(self, idx, embeddings=None, targets=None):
+        raise NotImplementedError('Model should output attention')
+        # forward the GPT model
+        token_embeddings = self.tok_emb(idx) # each index maps to a (learnable) vector
+
+        if embeddings is not None: # prepend explicit embeddings
+            token_embeddings = torch.cat((embeddings, token_embeddings), dim=1)
+
+        t = token_embeddings.shape[1]
+        assert t <= self.block_size, "Cannot forward, model block size is exhausted."
+        position_embeddings = self.pos_emb[:, :t, :] # each position maps to a (learnable) vector
+        x = self.drop(token_embeddings + position_embeddings)
+        x = self.blocks(x)
+        x = self.ln_f(x)
+        logits = self.head(x)
+
+        # if we are given some desired targets also calculate the loss
+        loss = None
+        if targets is not None:
+            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
+
+        return logits, loss
+
+class GPTFeats(GPT):
+
+    def __init__(self, feat_embedding_config, GPT_config):
+        super().__init__(**GPT_config)
+        # patching the config by removing the default parameters for Conv1d
+        if feat_embedding_config.target.split('.')[-1] in ['LSTM', 'GRU']:
+            for p in ['in_channels', 'out_channels', 'padding', 'kernel_size']:
+                if p in feat_embedding_config.params:
+                    feat_embedding_config.params.pop(p)
+        self.embedder = instantiate_from_config(config=feat_embedding_config)
+        if isinstance(self.embedder, nn.Linear):
+            print('Checkout cond_transformer.configure_optimizers. Make sure not to use decay with Linear')
+
+    def forward(self, idx, feats):
+        if isinstance(self.embedder, nn.Linear):
+            feats = feats.permute(0, 2, 1)
+            feats = self.embedder(feats)
+        elif isinstance(self.embedder, (nn.LSTM, nn.GRU)):
+            feats = feats.permute(0, 2, 1)
+            feats, _ = self.embedder(feats)
+        elif isinstance(self.embedder, (nn.Conv1d, nn.Identity)):
+            # (B, D', T) <- (B, D, T)
+            feats = self.embedder(feats)
+            # (B, T, D') <- (B, T, D)
+            feats = feats.permute(0, 2, 1)
+        else:
+            raise NotImplementedError
+        # calling forward from super
+        return super().forward(idx, embeddings=feats)
+
+class GPTClass(GPT):
+
+    def __init__(self, token_embedding_config, GPT_config):
+        super().__init__(**GPT_config)
+        self.embedder = instantiate_from_config(config=token_embedding_config)
+
+    def forward(self, idx, token):
+        token = self.embedder(token)
+        # calling forward from super
+        return super().forward(idx, embeddings=token)
+
+class GPTFeatsClass(GPT):
+
+    def __init__(self, feat_embedding_config, token_embedding_config, GPT_config):
+        super().__init__(**GPT_config)
+
+        # patching the config by removing the default parameters for Conv1d
+        if feat_embedding_config.target.split('.')[-1] in ['LSTM', 'GRU']:
+            for p in ['in_channels', 'out_channels', 'padding', 'kernel_size']:
+                if p in feat_embedding_config.params:
+                    feat_embedding_config.params.pop(p)
+
+        self.feat_embedder = instantiate_from_config(config=feat_embedding_config)
+        self.cls_embedder = instantiate_from_config(config=token_embedding_config)
+
+        if isinstance(self.feat_embedder, nn.Linear):
+            print('Checkout cond_transformer.configure_optimizers. Make sure not to use decay with Linear')
+
+    def forward(self, idx, feats_token_dict: dict):
+        feats = feats_token_dict['feature']
+        token = feats_token_dict['target']
+
+        # Features. Output size: (B, T, D')
+        if isinstance(self.feat_embedder, nn.Linear):
+            feats = feats.permute(0, 2, 1)
+            feats = self.feat_embedder(feats)
+        elif isinstance(self.feat_embedder, (nn.LSTM, nn.GRU)):
+            feats = feats.permute(0, 2, 1)
+            feats, _ = self.feat_embedder(feats)
+        elif isinstance(self.feat_embedder, (nn.Conv1d, nn.Identity)):
+            # (B, D', T) <- (B, D, T)
+            feats = self.feat_embedder(feats)
+            # (B, T, D') <- (B, T, D)
+            feats = feats.permute(0, 2, 1)
+        else:
+            raise NotImplementedError
+
+        # Class. Output size: (B, 1, D')
+        token = self.cls_embedder(token)
+
+        # Concat
+        condition_emb = torch.cat([feats, token], dim=1)
+
+        # calling forward from super
+        return super().forward(idx, embeddings=condition_emb)
+
+
+#### sampling utils
+
+def top_k_logits(logits, k):
+    v, ix = torch.topk(logits, k)
+    out = logits.clone()
+    out[out < v[:, [-1]]] = -float('Inf')
+    return out
+
+@torch.no_grad()
+def sample(model, x, steps, temperature=1.0, sample=False, top_k=None):
+    """
+    take a conditioning sequence of indices in x (of shape (b,t)) and predict the next token in
+    the sequence, feeding the predictions back into the model each time. Clearly the sampling
+    has quadratic complexity unlike an RNN that is only linear, and has a finite context window
+    of block_size, unlike an RNN that has an infinite context window.
+    """
+    block_size = model.get_block_size()
+    model.eval()
+    for k in range(steps):
+        x_cond = x if x.size(1) <= block_size else x[:, -block_size:]  # crop context if needed
+        raise NotImplementedError('v-iashin: the model outputs (logits, loss, attention)')
+        logits, _ = model(x_cond)
+        # pluck the logits at the final step and scale by temperature
+        logits = logits[:, -1, :] / temperature
+        # optionally crop probabilities to only the top k options
+        if top_k is not None:
+            logits = top_k_logits(logits, top_k)
+        # apply softmax to convert to probabilities
+        probs = F.softmax(logits, dim=-1)
+        # sample from the distribution or take the most likely
+        if sample:
+            ix = torch.multinomial(probs, num_samples=1)
+        else:
+            _, ix = torch.topk(probs, k=1, dim=-1)
+        # append to the sequence and continue
+        x = torch.cat((x, ix), dim=1)
+
+    return x
+
+
+
+#### clustering utils
+
+class KMeans(nn.Module):
+    def __init__(self, ncluster=512, nc=3, niter=10):
+        super().__init__()
+        self.ncluster = ncluster
+        self.nc = nc
+        self.niter = niter
+        self.shape = (3,32,32)
+        self.register_buffer("C", torch.zeros(self.ncluster,nc))
+        self.register_buffer('initialized', torch.tensor(0, dtype=torch.uint8))
+
+    def is_initialized(self):
+        return self.initialized.item() == 1
+
+    @torch.no_grad()
+    def initialize(self, x):
+        N, D = x.shape
+        assert D == self.nc, D
+        c = x[torch.randperm(N)[:self.ncluster]] # init clusters at random
+        for i in range(self.niter):
+            # assign all pixels to the closest codebook element
+            a = ((x[:, None, :] - c[None, :, :])**2).sum(-1).argmin(1)
+            # move each codebook element to be the mean of the pixels that assigned to it
+            c = torch.stack([x[a==k].mean(0) for k in range(self.ncluster)])
+            # re-assign any poorly positioned codebook elements
+            nanix = torch.any(torch.isnan(c), dim=1)
+            ndead = nanix.sum().item()
+            print('done step %d/%d, re-initialized %d dead clusters' % (i+1, self.niter, ndead))
+            c[nanix] = x[torch.randperm(N)[:ndead]] # re-init dead clusters
+
+        self.C.copy_(c)
+        self.initialized.fill_(1)
+
+
+    def forward(self, x, reverse=False, shape=None):
+        if not reverse:
+            # flatten
+            bs,c,h,w = x.shape
+            assert c == self.nc
+            x = x.reshape(bs,c,h*w,1)
+            C = self.C.permute(1,0)
+            C = C.reshape(1,c,1,self.ncluster)
+            a = ((x-C)**2).sum(1).argmin(-1) # bs, h*w indices
+            return a
+        else:
+            # flatten
+            bs, HW = x.shape
+            """
+            c = self.C.reshape( 1, self.nc,  1, self.ncluster)
+            c = c[bs*[0],:,:,:]
+            c = c[:,:,HW*[0],:]
+            x =      x.reshape(bs,       1, HW,             1)
+            x = x[:,3*[0],:,:]
+            x = torch.gather(c, dim=3, index=x)
+            """
+            x = self.C[x]
+            x = x.permute(0,2,1)
+            shape = shape if shape is not None else self.shape
+            x = x.reshape(bs, *shape)
+
+            return x
+
+
+if __name__ == '__main__':
+    import torch
+    from omegaconf import OmegaConf
+    import numpy as np
+    from tqdm import tqdm
+
+    device = torch.device('cuda:2')
+    torch.cuda.set_device(device)
+
+    cfg = OmegaConf.load('./configs/vggsound_transformer.yaml')
+
+    model = instantiate_from_config(cfg.model.params.transformer_config)
+    model = model.to(device)
+
+    mel_num = cfg.data.params.mel_num
+    spec_crop_len = cfg.data.params.spec_crop_len
+    feat_depth = cfg.data.params.feat_depth
+    feat_crop_len = cfg.data.params.feat_crop_len
+
+    gcd = np.gcd(mel_num, spec_crop_len)
+    z_idx_size = (2, int(mel_num / gcd) * int(spec_crop_len / gcd))
+
+    for i in tqdm(range(300)):
+        z_indices = torch.randint(0, cfg.model.params.transformer_config.params.GPT_config.vocab_size, z_idx_size).to(device)
+        c = torch.rand(2, feat_depth, feat_crop_len).to(device)
+        logits, loss, att = model(z_indices[:, :-1], feats=c)
diff --git a/specvqgan/modules/transformer/permuter.py b/specvqgan/modules/transformer/permuter.py
new file mode 100644
index 0000000..bee3ab7
--- /dev/null
+++ b/specvqgan/modules/transformer/permuter.py
@@ -0,0 +1,295 @@
+import torch
+import torch.nn as nn
+import numpy as np
+
+TO_WARN_USER_ONCE = True
+
+class AbstractPermuter(nn.Module):
+    def __init__(self, *args, **kwargs):
+        super().__init__()
+    def forward(self, x, reverse=False):
+        raise NotImplementedError
+
+
+class Identity(AbstractPermuter):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x, reverse=False):
+        return x
+
+class ColumnMajor(AbstractPermuter):
+    '''Useful for spectrograms which are from left to right (features, time)'''
+    def __init__(self, H, W):
+        super().__init__()
+        self.H = H
+        self.W = W
+        idx = self.make_idx(H, W)
+        self.register_buffer('forward_shuffle_idx', idx)
+        self.register_buffer('backward_shuffle_idx', torch.argsort(idx))
+
+    def forward(self, x, reverse=False):
+        B, L = x.shape
+        L_idx = len(self.forward_shuffle_idx)
+        if L > L_idx:
+            # an ugly patch for "infinite" sampling because self.*_shuffle_idx are shorter
+            # otherwise even uglier patch in other places. 'if' is triggered only on sampling.
+            assert L % L_idx == 0 and L / L_idx == int(L / L_idx), f'L: {L}, L_idx: {L_idx}'
+            W_scale = L // L_idx
+            print(f'Permuter is making a guess on the temp scale: {W_scale}. Ignore on "infinite" sampling')
+            idx = self.make_idx(self.H, self.W * W_scale)
+            if not reverse:
+                return x[:, idx]
+            else:
+                return x[:, torch.argsort(idx)]
+        else:
+            if not reverse:
+                return x[:, self.forward_shuffle_idx]
+            else:
+                return x[:, self.backward_shuffle_idx]
+
+    def make_idx(self, H, W):
+        idx = np.arange(H * W).reshape(H, W)
+        idx = idx.T
+        idx = torch.tensor(idx.ravel())
+        return idx
+
+class Subsample(AbstractPermuter):
+    def __init__(self, H, W):
+        super().__init__()
+        C = 1
+        indices = np.arange(H*W).reshape(C,H,W)
+        while min(H, W) > 1:
+            indices = indices.reshape(C,H//2,2,W//2,2)
+            indices = indices.transpose(0,2,4,1,3)
+            indices = indices.reshape(C*4,H//2, W//2)
+            H = H//2
+            W = W//2
+            C = C*4
+        assert H == W == 1
+        idx = torch.tensor(indices.ravel())
+        self.register_buffer('forward_shuffle_idx',
+                             nn.Parameter(idx, requires_grad=False))
+        self.register_buffer('backward_shuffle_idx',
+                             nn.Parameter(torch.argsort(idx), requires_grad=False))
+
+    def forward(self, x, reverse=False):
+        if not reverse:
+            return x[:, self.forward_shuffle_idx]
+        else:
+            return x[:, self.backward_shuffle_idx]
+
+
+def mortonify(i, j):
+    """(i,j) index to linear morton code"""
+    i = np.uint64(i)
+    j = np.uint64(j)
+
+    z = np.uint(0)
+
+    for pos in range(32):
+        z = (z |
+             ((j & (np.uint64(1) << np.uint64(pos))) << np.uint64(pos)) |
+             ((i & (np.uint64(1) << np.uint64(pos))) << np.uint64(pos+1))
+             )
+    return z
+
+
+class ZCurve(AbstractPermuter):
+    def __init__(self, H, W):
+        super().__init__()
+        reverseidx = [np.int64(mortonify(i,j)) for i in range(H) for j in range(W)]
+        idx = np.argsort(reverseidx)
+        idx = torch.tensor(idx)
+        reverseidx = torch.tensor(reverseidx)
+        self.register_buffer('forward_shuffle_idx',
+                             idx)
+        self.register_buffer('backward_shuffle_idx',
+                             reverseidx)
+
+    def forward(self, x, reverse=False):
+        if not reverse:
+            return x[:, self.forward_shuffle_idx]
+        else:
+            return x[:, self.backward_shuffle_idx]
+
+
+class SpiralOut(AbstractPermuter):
+    def __init__(self, H, W):
+        super().__init__()
+        assert H == W
+        size = W
+        indices = np.arange(size*size).reshape(size,size)
+
+        i0 = size//2
+        j0 = size//2-1
+
+        i = i0
+        j = j0
+
+        idx = [indices[i0, j0]]
+        step_mult = 0
+        for c in range(1, size//2+1):
+            step_mult += 1
+            # steps left
+            for k in range(step_mult):
+                i = i - 1
+                j = j
+                idx.append(indices[i, j])
+
+            # step down
+            for k in range(step_mult):
+                i = i
+                j = j + 1
+                idx.append(indices[i, j])
+
+            step_mult += 1
+            if c < size//2:
+                # step right
+                for k in range(step_mult):
+                    i = i + 1
+                    j = j
+                    idx.append(indices[i, j])
+
+                # step up
+                for k in range(step_mult):
+                    i = i
+                    j = j - 1
+                    idx.append(indices[i, j])
+            else:
+                # end reached
+                for k in range(step_mult-1):
+                    i = i + 1
+                    idx.append(indices[i, j])
+
+        assert len(idx) == size*size
+        idx = torch.tensor(idx)
+        self.register_buffer('forward_shuffle_idx', idx)
+        self.register_buffer('backward_shuffle_idx', torch.argsort(idx))
+
+    def forward(self, x, reverse=False):
+        if not reverse:
+            return x[:, self.forward_shuffle_idx]
+        else:
+            return x[:, self.backward_shuffle_idx]
+
+
+class SpiralIn(AbstractPermuter):
+    def __init__(self, H, W):
+        super().__init__()
+        assert H == W
+        size = W
+        indices = np.arange(size*size).reshape(size,size)
+
+        i0 = size//2
+        j0 = size//2-1
+
+        i = i0
+        j = j0
+
+        idx = [indices[i0, j0]]
+        step_mult = 0
+        for c in range(1, size//2+1):
+            step_mult += 1
+            # steps left
+            for k in range(step_mult):
+                i = i - 1
+                j = j
+                idx.append(indices[i, j])
+
+            # step down
+            for k in range(step_mult):
+                i = i
+                j = j + 1
+                idx.append(indices[i, j])
+
+            step_mult += 1
+            if c < size//2:
+                # step right
+                for k in range(step_mult):
+                    i = i + 1
+                    j = j
+                    idx.append(indices[i, j])
+
+                # step up
+                for k in range(step_mult):
+                    i = i
+                    j = j - 1
+                    idx.append(indices[i, j])
+            else:
+                # end reached
+                for k in range(step_mult-1):
+                    i = i + 1
+                    idx.append(indices[i, j])
+
+        assert len(idx) == size*size
+        idx = idx[::-1]
+        idx = torch.tensor(idx)
+        self.register_buffer('forward_shuffle_idx', idx)
+        self.register_buffer('backward_shuffle_idx', torch.argsort(idx))
+
+    def forward(self, x, reverse=False):
+        if not reverse:
+            return x[:, self.forward_shuffle_idx]
+        else:
+            return x[:, self.backward_shuffle_idx]
+
+
+class Random(nn.Module):
+    def __init__(self, H, W):
+        super().__init__()
+        indices = np.random.RandomState(1).permutation(H*W)
+        idx = torch.tensor(indices.ravel())
+        self.register_buffer('forward_shuffle_idx', idx)
+        self.register_buffer('backward_shuffle_idx', torch.argsort(idx))
+
+    def forward(self, x, reverse=False):
+        if not reverse:
+            return x[:, self.forward_shuffle_idx]
+        else:
+            return x[:, self.backward_shuffle_idx]
+
+
+class AlternateParsing(AbstractPermuter):
+    def __init__(self, H, W):
+        super().__init__()
+        indices = np.arange(W*H).reshape(H,W)
+        for i in range(1, H, 2):
+            indices[i, :] = indices[i, ::-1]
+        idx = indices.flatten()
+        assert len(idx) == H*W
+        idx = torch.tensor(idx)
+        self.register_buffer('forward_shuffle_idx', idx)
+        self.register_buffer('backward_shuffle_idx', torch.argsort(idx))
+
+    def forward(self, x, reverse=False):
+        if not reverse:
+            return x[:, self.forward_shuffle_idx]
+        else:
+            return x[:, self.backward_shuffle_idx]
+
+
+if __name__ == "__main__":
+    p0 = AlternateParsing(16, 16)
+    print(p0.forward_shuffle_idx)
+    print(p0.backward_shuffle_idx)
+
+    x = torch.randint(0, 768, size=(11, 256))
+    y = p0(x)
+    xre = p0(y, reverse=True)
+    assert torch.equal(x, xre)
+
+    p1 = SpiralOut(2, 2)
+    print(p1.forward_shuffle_idx)
+    print(p1.backward_shuffle_idx)
+    x = torch.randint(0, 768, size=(11, 2*2))
+    y = p1(x)
+    xre = p1(y, reverse=True)
+    assert torch.equal(x, xre)
+
+    p2 = ColumnMajor(5, 53)
+    print(p2.forward_shuffle_idx)
+    print(p2.backward_shuffle_idx)
+    x = torch.randint(0, 768, size=(11, 5*53))
+    xre = p2(p2(x), reverse=True)
+    assert torch.equal(x, xre)
diff --git a/specvqgan/modules/vqvae/quantize.py b/specvqgan/modules/vqvae/quantize.py
new file mode 100644
index 0000000..296df15
--- /dev/null
+++ b/specvqgan/modules/vqvae/quantize.py
@@ -0,0 +1,131 @@
+import torch
+import torch.nn as nn
+
+
+class VectorQuantizer(nn.Module):
+    """
+    see https://github.com/MishaLaskin/vqvae/blob/d761a999e2267766400dc646d82d3ac3657771d4/models/quantizer.py
+    ____________________________________________
+    Discretization bottleneck part of the VQ-VAE.
+    Inputs:
+    - n_e : number of embeddings
+    - e_dim : dimension of embedding
+    - beta : commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2
+    _____________________________________________
+    """
+
+    def __init__(self, n_e, e_dim, beta):
+        super(VectorQuantizer, self).__init__()
+        self.n_e = n_e
+        self.e_dim = e_dim
+        self.beta = beta
+
+        self.embedding = nn.Embedding(self.n_e, self.e_dim)
+        self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)
+
+        # better inheritence properties (so that when VectorQuantizer1d() inherits it, only these will be
+        # changed)
+        self.permute_order_in = [0, 2, 3, 1]
+        self.permute_order_out = [0, 3, 1, 2]
+
+    def forward(self, z):
+        """
+        Inputs the output of the encoder network z and maps it to a discrete
+        one-hot vector that is the index of the closest embedding vector e_j
+        z (continuous) -> z_q (discrete)
+        2d: z.shape = (batch, channel, height, width)
+        1d: z.shape = (batch, channel, time)
+        quantization pipeline:
+            1. get encoder input 2d: (B,C,H,W) or 1d: (B, C, T)
+            2. flatten input to 2d: (B*H*W,C) or 1d: (B*T, C)
+        """
+        # reshape z -> (batch, height, width, channel) or (batch, time, channel) and flatten
+        z = z.permute(self.permute_order_in).contiguous()
+        z_flattened = z.view(-1, self.e_dim)
+        # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
+
+        d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \
+            torch.sum(self.embedding.weight**2, dim=1) - 2 * \
+            torch.matmul(z_flattened, self.embedding.weight.t())
+
+        ## could possible replace this here
+        # #\start...
+        # find closest encodings
+        min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1)
+
+        min_encodings = torch.zeros(min_encoding_indices.shape[0], self.n_e).to(z)
+        min_encodings.scatter_(1, min_encoding_indices, 1)
+
+        # dtype min encodings: torch.float32
+        # min_encodings shape: torch.Size([2048, 512])
+        # min_encoding_indices.shape: torch.Size([2048, 1])
+
+        # get quantized latent vectors
+        z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape)
+        #.........\end
+
+        # with:
+        # .........\start
+        #min_encoding_indices = torch.argmin(d, dim=1)
+        #z_q = self.embedding(min_encoding_indices)
+        # ......\end......... (TODO)
+
+        # compute loss for embedding
+        loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * torch.mean((z_q - z.detach()) ** 2)
+
+        # preserve gradients
+        z_q = z + (z_q - z).detach()
+
+        # perplexity
+        e_mean = torch.mean(min_encodings, dim=0)
+        perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10)))
+
+        # reshape back to match original input shape
+        z_q = z_q.permute(self.permute_order_out).contiguous()
+
+        return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
+
+    def get_codebook_entry(self, indices, shape):
+        # shape specifying (batch, height, width, channel)
+        # TODO: check for more easy handling with nn.Embedding
+        min_encodings = torch.zeros(indices.shape[0], self.n_e).to(indices)
+        min_encodings.scatter_(1, indices[:, None], 1)
+
+        # get quantized latent vectors
+        z_q = torch.matmul(min_encodings.float(), self.embedding.weight)
+
+        if shape is not None:
+            z_q = z_q.view(shape)
+
+            # reshape back to match original input shape
+            z_q = z_q.permute(self.permute_order_out).contiguous()
+
+        return z_q
+
+class VectorQuantizer1d(VectorQuantizer):
+
+    def __init__(self, n_embed, embed_dim, beta=0.25):
+        super().__init__(n_embed, embed_dim, beta)
+        self.permute_order_in = [0, 2, 1]
+        self.permute_order_out = [0, 2, 1]
+
+
+if __name__ == '__main__':
+    quantize = VectorQuantizer1d(n_embed=1024, embed_dim=256, beta=0.25)
+
+    # 1d Input (features)
+    enc_outputs = torch.rand(6, 256, 53)
+    quant, emb_loss, info = quantize(enc_outputs)
+    print(quant.shape)
+
+    quantize = VectorQuantizer(n_e=1024, e_dim=256, beta=0.25)
+
+    # Audio
+    enc_outputs = torch.rand(4, 256, 5, 53)
+    quant, emb_loss, info = quantize(enc_outputs)
+    print(quant.shape)
+
+    # Image
+    enc_outputs = torch.rand(4, 256, 16, 16)
+    quant, emb_loss, info = quantize(enc_outputs)
+    print(quant.shape)
diff --git a/specvqgan/util.py b/specvqgan/util.py
new file mode 100644
index 0000000..deb92db
--- /dev/null
+++ b/specvqgan/util.py
@@ -0,0 +1,150 @@
+import hashlib
+import os
+
+import requests
+from tqdm import tqdm
+
+URL_MAP = {
+    'vggishish_lpaps': 'https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/vggishish16.pt',
+    'vggishish_mean_std_melspec_10s_22050hz': 'https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/train_means_stds_melspec_10s_22050hz.txt',
+    'melception': 'https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/melception-21-05-10T09-28-40.pt',
+}
+
+CKPT_MAP = {
+    'vggishish_lpaps': 'vggishish16.pt',
+    'vggishish_mean_std_melspec_10s_22050hz': 'train_means_stds_melspec_10s_22050hz.txt',
+    'melception': 'melception-21-05-10T09-28-40.pt',
+}
+
+MD5_MAP = {
+    'vggishish_lpaps': '197040c524a07ccacf7715d7080a80bd',
+    'vggishish_mean_std_melspec_10s_22050hz': 'f449c6fd0e248936c16f6d22492bb625',
+    'melception': 'a71a41041e945b457c7d3d814bbcf72d',
+}
+
+
+def download(url, local_path, chunk_size=1024):
+    os.makedirs(os.path.split(local_path)[0], exist_ok=True)
+    with requests.get(url, stream=True) as r:
+        total_size = int(r.headers.get("content-length", 0))
+        with tqdm(total=total_size, unit="B", unit_scale=True) as pbar:
+            with open(local_path, "wb") as f:
+                for data in r.iter_content(chunk_size=chunk_size):
+                    if data:
+                        f.write(data)
+                        pbar.update(chunk_size)
+
+
+def md5_hash(path):
+    with open(path, "rb") as f:
+        content = f.read()
+    return hashlib.md5(content).hexdigest()
+
+
+def get_ckpt_path(name, root, check=False):
+    assert name in URL_MAP
+    path = os.path.join(root, CKPT_MAP[name])
+    if not os.path.exists(path) or (check and not md5_hash(path) == MD5_MAP[name]):
+        print("Downloading {} model from {} to {}".format(name, URL_MAP[name], path))
+        download(URL_MAP[name], path)
+        md5 = md5_hash(path)
+        assert md5 == MD5_MAP[name], md5
+    return path
+
+
+class KeyNotFoundError(Exception):
+    def __init__(self, cause, keys=None, visited=None):
+        self.cause = cause
+        self.keys = keys
+        self.visited = visited
+        messages = list()
+        if keys is not None:
+            messages.append("Key not found: {}".format(keys))
+        if visited is not None:
+            messages.append("Visited: {}".format(visited))
+        messages.append("Cause:\n{}".format(cause))
+        message = "\n".join(messages)
+        super().__init__(message)
+
+
+def retrieve(
+    list_or_dict, key, splitval="/", default=None, expand=True, pass_success=False
+):
+    """Given a nested list or dict return the desired value at key expanding
+    callable nodes if necessary and :attr:`expand` is ``True``. The expansion
+    is done in-place.
+
+    Parameters
+    ----------
+        list_or_dict : list or dict
+            Possibly nested list or dictionary.
+        key : str
+            key/to/value, path like string describing all keys necessary to
+            consider to get to the desired value. List indices can also be
+            passed here.
+        splitval : str
+            String that defines the delimiter between keys of the
+            different depth levels in `key`.
+        default : obj
+            Value returned if :attr:`key` is not found.
+        expand : bool
+            Whether to expand callable nodes on the path or not.
+
+    Returns
+    -------
+        The desired value or if :attr:`default` is not ``None`` and the
+        :attr:`key` is not found returns ``default``.
+
+    Raises
+    ------
+        Exception if ``key`` not in ``list_or_dict`` and :attr:`default` is
+        ``None``.
+    """
+
+    keys = key.split(splitval)
+
+    success = True
+    try:
+        visited = []
+        parent = None
+        last_key = None
+        for key in keys:
+            if callable(list_or_dict):
+                if not expand:
+                    raise KeyNotFoundError(
+                        ValueError(
+                            "Trying to get past callable node with expand=False."
+                        ),
+                        keys=keys,
+                        visited=visited,
+                    )
+                list_or_dict = list_or_dict()
+                parent[last_key] = list_or_dict
+
+            last_key = key
+            parent = list_or_dict
+
+            try:
+                if isinstance(list_or_dict, dict):
+                    list_or_dict = list_or_dict[key]
+                else:
+                    list_or_dict = list_or_dict[int(key)]
+            except (KeyError, IndexError, ValueError) as e:
+                raise KeyNotFoundError(e, keys=keys, visited=visited)
+
+            visited += [key]
+        # final expansion of retrieved value
+        if expand and callable(list_or_dict):
+            list_or_dict = list_or_dict()
+            parent[last_key] = list_or_dict
+    except KeyNotFoundError as e:
+        if default is None:
+            raise e
+        else:
+            list_or_dict = default
+            success = False
+
+    if not pass_success:
+        return list_or_dict
+    else:
+        return list_or_dict, success
diff --git a/train.py b/train.py
new file mode 100644
index 0000000..bedf99c
--- /dev/null
+++ b/train.py
@@ -0,0 +1,764 @@
+import argparse
+import datetime
+import glob
+import importlib
+import os
+import signal
+import sys
+from pathlib import Path
+
+import librosa
+import numpy as np
+import pytorch_lightning as pl
+import soundfile
+import torch
+import torchvision
+import yaml
+from omegaconf import OmegaConf
+from PIL import Image
+from pytorch_lightning import seed_everything
+from pytorch_lightning.callbacks import ModelCheckpoint, Callback, LearningRateMonitor
+from pytorch_lightning.trainer import Trainer
+from pytorch_lightning.utilities.distributed import rank_zero_only
+from torch.utils.data import DataLoader, Dataset
+
+from feature_extraction.extract_mel_spectrogram import inv_transforms
+from vocoder.modules import Generator
+
+
+def get_obj_from_str(string, reload=False):
+    module, cls = string.rsplit('.', 1)
+    if reload:
+        module_imp = importlib.import_module(module)
+        importlib.reload(module_imp)
+    return getattr(importlib.import_module(module, package=None), cls)
+
+
+def get_parser(**parser_kwargs):
+    def str2bool(v):
+        if isinstance(v, bool):
+            return v
+        if v.lower() in ('yes', 'true', 't', 'y', '1'):
+            return True
+        elif v.lower() in ('no', 'false', 'f', 'n', '0'):
+            return False
+        else:
+            raise argparse.ArgumentTypeError('Boolean value expected.')
+
+    parser = argparse.ArgumentParser(**parser_kwargs)
+    parser.add_argument(
+        '-n',
+        '--name',
+        type=str,
+        const=True,
+        default='',
+        nargs='?',
+        help='postfix for logdir',
+    )
+    parser.add_argument(
+        '-r',
+        '--resume',
+        type=str,
+        const=True,
+        default='',
+        nargs='?',
+        help='resume from logdir or checkpoint in logdir',
+    )
+    parser.add_argument(
+        '-b',
+        '--base',
+        nargs='*',
+        metavar='base_config.yaml',
+        help='paths to base configs. Loaded from left-to-right. '
+        'Parameters can be overwritten or added with command-line options of the form `--key value`.',
+        default=list(),
+    )
+    parser.add_argument(
+        '-t',
+        '--train',
+        type=str2bool,
+        const=True,
+        default=False,
+        nargs='?',
+        help='train',
+    )
+    parser.add_argument(
+        '--no-test',
+        type=str2bool,
+        const=True,
+        default=False,
+        nargs='?',
+        help='disable test',
+    )
+    parser.add_argument('-p', '--project', help='name of new or path to existing project')
+    parser.add_argument(
+        '-d',
+        '--debug',
+        type=str2bool,
+        nargs='?',
+        const=True,
+        default=False,
+        help='enable post-mortem debugging',
+    )
+    parser.add_argument(
+        '-s',
+        '--seed',
+        type=int,
+        default=23,
+        help='seed for seed_everything',
+    )
+    parser.add_argument(
+        '-f',
+        '--postfix',
+        type=str,
+        default='',
+        help='post-postfix for default name',
+    )
+
+    return parser
+
+
+def nondefault_trainer_args(opt):
+    parser = argparse.ArgumentParser()
+    parser = Trainer.add_argparse_args(parser)
+    args = parser.parse_args([])
+    return sorted(k for k in vars(args) if getattr(opt, k) != getattr(args, k))
+
+
+def instantiate_from_config(config):
+    if not 'target' in config:
+        raise KeyError('Expected key `target` to instantiate.')
+    return get_obj_from_str(config['target'])(**config.get('params', dict()))
+
+
+class WrappedDataset(Dataset):
+    '''Wraps an arbitrary object with __len__ and __getitem__ into a pytorch dataset'''
+    def __init__(self, dataset):
+        self.data = dataset
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, idx):
+        return self.data[idx]
+
+
+class DataModuleFromConfig(pl.LightningDataModule):
+    def __init__(self, batch_size, train=None, validation=None, test=None,
+                 wrap=False, num_workers=None):
+        super().__init__()
+        self.batch_size = batch_size
+        self.dataset_configs = dict()
+        self.num_workers = num_workers if num_workers is not None else batch_size*2
+        if train is not None:
+            self.dataset_configs['train'] = train
+            self.train_dataloader = self._train_dataloader
+        if validation is not None:
+            self.dataset_configs['validation'] = validation
+            self.val_dataloader = self._val_dataloader
+        if test is not None:
+            self.dataset_configs['test'] = test
+            self.test_dataloader = self._test_dataloader
+        self.wrap = wrap
+
+    def prepare_data(self):
+        for data_cfg in self.dataset_configs.values():
+            instantiate_from_config(data_cfg)
+
+    def setup(self, stage=None):
+        self.datasets = dict(
+            (k, instantiate_from_config(self.dataset_configs[k]))
+            for k in self.dataset_configs)
+        if self.wrap:
+            for k in self.datasets:
+                self.datasets[k] = WrappedDataset(self.datasets[k])
+
+    def _train_dataloader(self):
+        return DataLoader(self.datasets['train'], batch_size=self.batch_size,
+                          num_workers=self.num_workers, worker_init_fn=self.worker_init_fn,
+                          shuffle=True)
+
+    def _val_dataloader(self):
+        return DataLoader(self.datasets['validation'], batch_size=self.batch_size,
+                          num_workers=self.num_workers, worker_init_fn=self.worker_init_fn)
+
+    def _test_dataloader(self):
+        return DataLoader(self.datasets['test'], batch_size=self.batch_size,
+                          num_workers=self.num_workers, worker_init_fn=self.worker_init_fn)
+
+    @staticmethod
+    def worker_init_fn(worker_id):
+        np.random.seed(np.random.get_state()[1][0] + worker_id)
+
+class SpectrogramDataModuleFromConfig(DataModuleFromConfig):
+    '''avoiding duplication of hyper-parameters in the config by gross patching here '''
+    def __init__(self, batch_size, num_workers, spec_dir_path=None,
+                 sample_rate=None, mel_num=None, spec_len=None, spec_crop_len=None,
+                 random_crop=None, train=None, validation=None, test=None, wrap=False):
+        specs_dataset_cfg = {
+            # 'spec_dir_name': Path(spec_dir_path).name,
+            'spec_dir_path': spec_dir_path,
+            'random_crop': random_crop,
+            # 'sample_rate': sample_rate,
+            'mel_num': mel_num,
+            'spec_len': spec_len,
+            'spec_crop_len': spec_crop_len,
+        }
+        for name, split in {'train': train, 'validation': validation, 'test': test}.items():
+            if split is not None:
+                split.params.specs_dataset_cfg = specs_dataset_cfg
+        super().__init__(batch_size, train, validation, test, wrap, num_workers)
+
+
+class ConditionedSpectrogramDataModuleFromConfig(DataModuleFromConfig):
+    '''avoiding duplication of hyper-parameters in the config by gross patching here '''
+    def __init__(self, batch_size, num_workers, spec_dir_path=None, rgb_feats_dir_path=None,
+                 flow_feats_dir_path=None, sample_rate=None, mel_num=None, spec_len=None, spec_crop_len=None,
+                 random_crop=None,  replace_feats_with_random=None,
+                 feat_depth=None, feat_len=None, feat_crop_len=None, crop_coord=None,
+                 for_which_class=None, feat_sampler_cfg=None, train=None, validation=None, test=None,
+                 wrap=False):
+        specs_dataset_cfg = {
+            # 'spec_dir_name': Path(spec_dir_path).name,
+            'spec_dir_path': spec_dir_path,
+            'random_crop': random_crop,
+            # 'sample_rate': sample_rate,
+            'mel_num': mel_num,
+            'spec_len': spec_len,
+            'spec_crop_len': spec_crop_len,
+            'crop_coord': crop_coord,
+            'for_which_class': for_which_class,
+        }
+        condition_dataset_cfg = {
+            'rgb_feats_dir_path': rgb_feats_dir_path,
+            'flow_feats_dir_path': flow_feats_dir_path,
+            'feat_depth': feat_depth,
+            'feat_len': feat_len,
+            'feat_crop_len': feat_crop_len,
+            'random_crop': random_crop,
+            'for_which_class': for_which_class,
+            'feat_sampler_cfg': feat_sampler_cfg,
+            'replace_feats_with_random': replace_feats_with_random,
+        }
+        for name, split in {'train': train, 'validation': validation, 'test': test}.items():
+            if split is not None:
+
+                if (split.target.split('.')[-1].startswith('VGGSoundSpecsCondOnFeats') \
+                   or split.target.split('.')[-1].startswith('VASSpecsCondOnFeats')):
+                    split_path = split.params.condition_dataset_cfg.split_path
+                    condition_dataset_cfg['split_path'] = split_path
+                split.params.condition_dataset_cfg = condition_dataset_cfg
+                split.params.specs_dataset_cfg = specs_dataset_cfg
+
+        super().__init__(batch_size, train, validation, test, wrap, num_workers)
+
+
+class SetupCallback(Callback):
+    def __init__(self, resume, now, logdir, ckptdir, cfgdir, config, lightning_config):
+        super().__init__()
+        self.resume = resume
+        self.now = now
+        self.logdir = logdir
+        self.ckptdir = ckptdir
+        self.cfgdir = cfgdir
+        self.config = config
+        self.lightning_config = lightning_config
+
+    def on_pretrain_routine_start(self, trainer, pl_module):
+        if trainer.global_rank == 0:
+            # Create logdirs and save configs
+            os.makedirs(self.logdir, exist_ok=True)
+            os.makedirs(self.ckptdir, exist_ok=True)
+            os.makedirs(self.cfgdir, exist_ok=True)
+
+            print('Project config')
+            print(self.config.pretty())
+            OmegaConf.save(self.config, os.path.join(self.cfgdir, '{}-project.yaml'.format(self.now)))
+
+            print('Lightning config')
+            print(self.lightning_config.pretty())
+            OmegaConf.save(OmegaConf.create({'lightning': self.lightning_config}),
+                           os.path.join(self.cfgdir, '{}-lightning.yaml'.format(self.now)))
+
+        else:
+            # ModelCheckpoint callback created log directory --- remove it
+            if not self.resume and os.path.exists(self.logdir):
+                dst, name = os.path.split(self.logdir)
+                dst = os.path.join(dst, 'child_runs', name)
+                os.makedirs(os.path.split(dst)[0], exist_ok=True)
+                try:
+                    os.rename(self.logdir, dst)
+                except FileNotFoundError:
+                    pass
+
+
+class VocoderMelGan(object):
+
+    def __init__(self, ckpt_vocoder):
+        ckpt_vocoder = Path(ckpt_vocoder)
+        vocoder_sd = torch.load(ckpt_vocoder / 'best_netG.pt', map_location='cpu')
+
+        with open(ckpt_vocoder / 'args.yml', 'r') as f:
+            vocoder_args = yaml.load(f, Loader=yaml.UnsafeLoader)
+
+        self.generator = Generator(vocoder_args.n_mel_channels, vocoder_args.ngf,
+                                   vocoder_args.n_residual_layers)
+        self.generator.load_state_dict(vocoder_sd)
+        self.generator.eval()
+
+    def vocode(self, spec, global_step=None):
+        with torch.no_grad():
+            return self.generator(torch.from_numpy(spec).unsqueeze(0)).squeeze().numpy()
+
+class VocoderGriffinLim(object):
+
+    def __init__(self, spec_dir_name):
+        self.spec_dir_name = spec_dir_name
+
+    def vocode(self, spec, global_step):
+        # inv_transform may stuck when the mel spec is bad. We time it out and replace with other sound
+        signal.signal(signal.SIGALRM, self.timeout_handler)
+        # no need to wait long time during the first couple of epochs
+        if global_step < 4096:
+            signal.alarm(7)
+        else:
+            signal.alarm(30)
+        try:
+            wave = inv_transforms(spec, self.spec_dir_name)
+            signal.alarm(0)
+        except TimeoutError as msg:
+            wave, _ = librosa.load('./data/10s_rick_roll_22050.wav', sr=None)
+            print(msg)
+        return wave
+
+    @classmethod
+    def timeout_handler(signum, frame):
+        raise TimeoutError('Bad spec: took too much time to reconstruct the sound from spectrogram')
+
+
+class ImageLogger(Callback):
+    def __init__(self, batch_frequency, max_images, clamp=True, increase_log_steps=True,
+                 for_specs=False, vocoder_cfg=None, spec_dir_name=None, sample_rate=None):
+        super().__init__()
+        self.batch_freq = batch_frequency
+        self.max_images = max_images
+        self.logger_log_images = {
+            pl.loggers.TestTubeLogger: self._testtube,
+        }
+        self.log_steps = [2 ** n for n in range(int(np.log2(self.batch_freq)) + 1)]
+        if not increase_log_steps:
+            self.log_steps = [self.batch_freq]
+        self.clamp = clamp
+        self.for_specs = for_specs
+        self.spec_dir_name = spec_dir_name
+        self.sample_rate = sample_rate
+        print('We will not save audio for conditioning and conditioning_rec')
+        if self.for_specs:
+            self.vocoder = instantiate_from_config(vocoder_cfg)
+
+    def _visualize_attention(self, attention, scale_by_prior=True):
+        if scale_by_prior:
+            B, H, T, T = attention.shape
+            # attention weight is 1/T: if we have a seq with length 3 the weights are 1/3, 1/3, and 1/3
+            # making T by T matrix with zeros in the upper triangular part
+            attention_uniform_prior = 1 / torch.arange(1, T+1).view(1, T, 1).repeat(B, 1, T)
+            attention_uniform_prior = attention_uniform_prior.tril().view(B, 1, T, T).to(attention.device)
+            attention = attention - attention_uniform_prior
+
+        attention_agg = attention.sum(dim=1, keepdims=True)
+        return attention_agg
+
+    def _log_rec_audio(self, specs, tag, global_step, pl_module=None, save_rec_path=None):
+
+        # specs are (B, 1, F, T)
+        for i, spec in enumerate(specs):
+            spec = spec.data.squeeze(0).cpu().numpy()
+            # audios are in [-1, 1], making them in [0, 1]
+            spec = (spec + 1) / 2
+            wave = self.vocoder.vocode(spec, global_step)
+            wave = torch.from_numpy(wave).unsqueeze(0)
+            if pl_module is not None:
+                pl_module.logger.experiment.add_audio(f'{tag}_{i}', wave, pl_module.global_step, self.sample_rate)
+            # in case we would like to save it on disk
+            if save_rec_path is not None:
+                try:
+                    librosa.output.write_wav(save_rec_path, wave.squeeze(0).numpy(), self.sample_rate)
+                except AttributeError:
+                    soundfile.write(save_rec_path, wave.squeeze(0).numpy(), self.sample_rate, 'FLOAT')
+
+    @rank_zero_only
+    def _testtube(self, pl_module, images, batch, batch_idx, split):
+
+        if pl_module.__class__.__name__ == 'Net2NetTransformer':
+            cond_stage_model = pl_module.cond_stage_model.__class__.__name__
+        else:
+            cond_stage_model = None
+
+        for k in images:
+            tag = f'{split}/{k}'
+            if cond_stage_model in ['ClassOnlyStage', 'FeatsClassStage'] and k in ['conditioning', 'conditioning_rec']:
+                # saving the classes for the current batch
+                pl_module.logger.experiment.add_text(tag, '; '.join(batch['label']))
+                # breaking here because we don't want to call add_image
+                if cond_stage_model == 'FeatsClassStage':
+                    grid = torchvision.utils.make_grid(images[k]['feature'].unsqueeze(1).permute(0, 1, 3, 2), nrow=1, normalize=True)
+                else:
+                    continue
+            elif k in ['att_nopix', 'att_half', 'att_det']:
+                B, H, T, T = images[k].shape
+                grid = torchvision.utils.make_grid(self._visualize_attention(images[k]), nrow=H, normalize=True)
+            elif cond_stage_model in ['RawFeatsStage', 'VQModel1d', 'FeatClusterStage'] and k in ['conditioning', 'conditioning_rec']:
+                grid = torchvision.utils.make_grid(images[k].unsqueeze(1).permute(0, 1, 3, 2), nrow=1, normalize=True)
+            else:
+                if self.for_specs:
+                    # flipping values along frequency dim, otherwise mels are upside-down (1, F, T)
+                    grid = torchvision.utils.make_grid(images[k].flip(dims=(2,)), nrow=1)
+                    # also reconstruct waveform given the spec and inv_transform
+                    if k not in ['conditioning', 'conditioning_rec', 'att_nopix', 'att_half', 'att_det']:
+                        self._log_rec_audio(images[k], tag, pl_module.global_step, pl_module=pl_module)
+                else:
+                    grid = torchvision.utils.make_grid(images[k])
+                # attention is already in [0, 1] therefore ignoring this line
+                grid = (grid + 1.0) / 2.0  # -1,1 -> 0,1; c,h,w
+            pl_module.logger.experiment.add_image(tag, grid, global_step=pl_module.global_step)
+
+    @rank_zero_only
+    def log_local(self, pl_module, split, images, batch, batch_idx):
+        root = os.path.join(pl_module.logger.save_dir, 'images', split)
+
+        if pl_module.__class__.__name__ == 'Net2NetTransformer':
+            cond_stage_model = pl_module.cond_stage_model.__class__.__name__
+        else:
+            cond_stage_model = None
+
+        for k in images:
+            if cond_stage_model in ['ClassOnlyStage', 'FeatsClassStage'] and k in ['conditioning', 'conditioning_rec']:
+                filename = '{}_gs-{:06}_e-{:03}_b-{:06}.txt'.format(
+                    k,
+                    pl_module.global_step,
+                    pl_module.current_epoch,
+                    batch_idx)
+                path = os.path.join(root, filename)
+                os.makedirs(os.path.split(path)[0], exist_ok=True)
+                # saving the classes for the current batch
+                with open(path, 'w') as file:
+                    file.write('\n'.join(batch['label']))
+                # next loop iteration here because we don't want to call add_image
+                if cond_stage_model == 'FeatsClassStage':
+                    grid = torchvision.utils.make_grid(images[k]['feature'].unsqueeze(1).permute(0, 1, 3, 2), nrow=1, normalize=True)
+                else:
+                    continue
+            elif k in ['att_nopix', 'att_half', 'att_det']:  # GPT CLass
+                B, H, T, T = images[k].shape
+                grid = torchvision.utils.make_grid(self._visualize_attention(images[k]), nrow=H, normalize=True)
+            elif cond_stage_model in ['RawFeatsStage', 'VQModel1d', 'FeatClusterStage'] and k in ['conditioning', 'conditioning_rec']:
+                grid = torchvision.utils.make_grid(images[k].unsqueeze(1).permute(0, 1, 3, 2), nrow=1, normalize=True)
+            else:
+                if self.for_specs:
+                    # flipping values along frequency dim, otherwise mels are upside-down (1, F, T)
+                    grid = torchvision.utils.make_grid(images[k].flip(dims=(2,)), nrow=1)
+                else:
+                    grid = torchvision.utils.make_grid(images[k], nrow=4)
+                # attention is already in [0, 1] therefore ignoring this line
+                grid = (grid+1.0)/2.0 # -1,1 -> 0,1; c,h,w
+
+            grid = grid.transpose(0,1).transpose(1,2).squeeze(-1)
+            grid = grid.numpy()
+            grid = (grid*255).astype(np.uint8)
+            filename = '{}_gs-{:06}_e-{:03}_b-{:06}.png'.format(
+                k,
+                pl_module.global_step,
+                pl_module.current_epoch,
+                batch_idx)
+            path = os.path.join(root, filename)
+            os.makedirs(os.path.split(path)[0], exist_ok=True)
+            Image.fromarray(grid).save(path)
+
+            # also save audio on the disk
+            if self.for_specs:
+                tag = f'{split}/{k}'
+                filename = filename.replace('.png', '.wav')
+                path = os.path.join(root, filename)
+                if k not in ['conditioning', 'conditioning_rec', 'att_nopix', 'att_half', 'att_det']:
+                    self._log_rec_audio(images[k], tag, pl_module.global_step, save_rec_path=path)
+
+    def log_img(self, pl_module, batch, batch_idx, split='train'):
+        if (self.check_frequency(batch_idx) and  # batch_idx % self.batch_freq == 0
+                hasattr(pl_module, 'log_images') and
+                callable(pl_module.log_images) and
+                self.max_images > 0 and
+                pl_module.first_stage_key != 'feature'):
+            logger = type(pl_module.logger)
+
+            is_train = pl_module.training
+            if is_train:
+                pl_module.eval()
+
+            with torch.no_grad():
+                images = pl_module.log_images(batch, split=split)
+
+            for k in images:
+                if isinstance(images[k], dict):
+                    N = min(images[k]['feature'].shape[0], self.max_images)
+                    images[k]['feature'] = images[k]['feature'][:N]
+                    if isinstance(images[k]['feature'], torch.Tensor):
+                        images[k]['feature'] = images[k]['feature'].detach().cpu()
+                        if self.clamp:
+                            images[k]['feature'] = torch.clamp(images[k]['feature'], -1., 1.)
+                else:
+                    N = min(images[k].shape[0], self.max_images)
+                    images[k] = images[k][:N]
+                    if isinstance(images[k], torch.Tensor):
+                        images[k] = images[k].detach().cpu()
+                        if self.clamp:
+                            images[k] = torch.clamp(images[k], -1., 1.)
+
+            self.log_local(pl_module, split, images, batch, batch_idx)
+
+            logger_log_images = self.logger_log_images.get(logger, lambda *args, **kwargs: None)
+            logger_log_images(pl_module, images, batch, pl_module.global_step, split)
+
+            if is_train:
+                pl_module.train()
+
+    def check_frequency(self, batch_idx):
+        if (batch_idx % self.batch_freq) == 0 or (batch_idx in self.log_steps):
+            try:
+                self.log_steps.pop(0)
+            except IndexError:
+                pass
+            return True
+        return False
+
+    def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
+        self.log_img(pl_module, batch, batch_idx, split='train')
+
+    def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
+        self.log_img(pl_module, batch, batch_idx, split='val')
+
+
+if __name__ == '__main__':
+    # adding a random number of seconds so that exp folder names coincide less often
+    random_seconds_shift = datetime.timedelta(seconds=np.random.randint(60))
+    now = (datetime.datetime.now() - random_seconds_shift).strftime('%Y-%m-%dT%H-%M-%S')
+
+    # add cwd for convenience and to make classes in this file available when
+    # running as `python train.py`
+    # (in particular `train.DataModuleFromConfig`)
+    sys.path.append(os.getcwd())
+
+    parser = get_parser()
+    parser = Trainer.add_argparse_args(parser)
+
+    opt, unknown = parser.parse_known_args()
+    if opt.name and opt.resume:
+        raise ValueError(
+            '-n/--name and -r/--resume cannot be specified both.'
+            'If you want to resume training in a new log folder, '
+            'use -n/--name in combination with --resume_from_checkpoint'
+        )
+    if opt.resume:
+        if not os.path.exists(opt.resume):
+            raise ValueError('Cannot find {}'.format(opt.resume))
+        if os.path.isfile(opt.resume):
+            paths = opt.resume.split('/')
+            idx = len(paths)-paths[::-1].index('logs')+1
+            logdir = '/'.join(paths[:idx])
+            ckpt = opt.resume
+        else:
+            assert os.path.isdir(opt.resume), opt.resume
+            logdir = opt.resume.rstrip('/')
+            # ckpt = os.path.join(logdir, 'checkpoints', 'last.ckpt')
+            # ckpt = sorted(glob.glob(os.path.join(logdir, 'checkpoints', '*.ckpt')))[-1]
+            if Path(os.path.join(logdir, 'checkpoints', 'last.ckpt')).exists():
+                ckpt = os.path.join(logdir, 'checkpoints', 'last.ckpt')
+            else:
+                ckpt = sorted(Path(logdir).glob('checkpoints/*.ckpt'))[-1]
+
+        opt.resume_from_checkpoint = ckpt
+        base_configs = sorted(glob.glob(os.path.join(logdir, 'configs/*.yaml')))
+        opt.base = base_configs+opt.base
+        _tmp = logdir.split('/')
+        nowname = _tmp[_tmp.index('logs')+1]
+    else:
+        if opt.name:
+            name = '_'+opt.name
+        elif opt.base:
+            cfg_fname = os.path.split(opt.base[0])[-1]
+            cfg_name = os.path.splitext(cfg_fname)[0]
+            name = '_'+cfg_name
+        else:
+            name = ''
+        nowname = now+name+opt.postfix
+        logdir = os.path.join('logs', nowname)
+
+    print(nowname)
+    ckptdir = os.path.join(logdir, 'checkpoints')
+    cfgdir = os.path.join(logdir, 'configs')
+    seed_everything(opt.seed)
+
+    try:
+        # init and save configs
+        configs = [OmegaConf.load(cfg) for cfg in opt.base]
+        cli = OmegaConf.from_dotlist(unknown)
+        config = OmegaConf.merge(*configs, cli)
+        lightning_config = config.pop('lightning', OmegaConf.create())
+        # merge trainer cli with config
+        trainer_config = lightning_config.get('trainer', OmegaConf.create())
+        # default to ddp
+        trainer_config['distributed_backend'] = 'ddp'
+        for k in nondefault_trainer_args(opt):
+            trainer_config[k] = getattr(opt, k)
+        if 'gpus' not in trainer_config:
+            del trainer_config['distributed_backend']
+            cpu = True
+        else:
+            gpuinfo = trainer_config['gpus']
+            print(f'Running on GPUs {gpuinfo}')
+            cpu = False
+        trainer_opt = argparse.Namespace(**trainer_config)
+        lightning_config.trainer = trainer_config
+
+        # model
+        model = instantiate_from_config(config.model)
+
+        # trainer and callbacks
+        trainer_kwargs = dict()
+
+        # default logger configs
+        default_logger_cfgs = {
+            'testtube': {
+                'target': 'pytorch_lightning.loggers.TestTubeLogger',
+                'params': {
+                    'name': 'testtube',
+                    'save_dir': logdir,
+                }
+            },
+        }
+        default_logger_cfg = default_logger_cfgs['testtube']
+        logger_cfg = lightning_config.logger or OmegaConf.create()
+        logger_cfg = OmegaConf.merge(default_logger_cfg, logger_cfg)
+        trainer_kwargs['logger'] = instantiate_from_config(logger_cfg)
+
+        # modelcheckpoint - use TrainResult/EvalResult(checkpoint_on=metric) to
+        # specify which metric is used to determine best models
+        default_modelckpt_cfg = {
+            'target': 'pytorch_lightning.callbacks.ModelCheckpoint',
+            'params': {
+                'dirpath': ckptdir,
+                'filename': '{epoch:06}',
+                'verbose': True,
+                'save_last': True,
+            }
+        }
+        if hasattr(model, 'monitor'):
+            print(f'Monitoring {model.monitor} as checkpoint metric.')
+            default_modelckpt_cfg['params']['monitor'] = model.monitor
+            default_modelckpt_cfg['params']['save_top_k'] = 3
+
+        modelckpt_cfg = lightning_config.modelcheckpoint or OmegaConf.create()
+        modelckpt_cfg = OmegaConf.merge(default_modelckpt_cfg, modelckpt_cfg)
+        trainer_kwargs['checkpoint_callback'] = instantiate_from_config(modelckpt_cfg)
+
+        # add callback which sets up log directory
+        default_callbacks_cfg = {
+            'setup_callback': {
+                'target': 'train.SetupCallback',
+                'params': {
+                    'resume': opt.resume,
+                    'now': now,
+                    'logdir': logdir,
+                    'ckptdir': ckptdir,
+                    'cfgdir': cfgdir,
+                    'config': config,
+                    'lightning_config': lightning_config,
+                }
+            },
+            'image_logger': {
+                'target': 'train.ImageLogger',
+                'params': {
+                    'batch_frequency': 750,
+                    'max_images': 4,
+                    'clamp': True
+                }
+            },
+            'learning_rate_logger': {
+                'target': 'train.LearningRateMonitor',
+                'params': {
+                    'logging_interval': 'step',
+                    #'log_momentum': True
+                }
+            },
+        }
+        # patching the default config for the spectrogram input
+        if 'Spectrogram' in config.data.target:
+            spec_dir_name = Path(config.data.params.spec_dir_path).name
+            default_callbacks_cfg['image_logger']['params']['spec_dir_name'] = spec_dir_name
+            default_callbacks_cfg['image_logger']['params']['sample_rate'] = config.data.params.sample_rate
+
+        callbacks_cfg = lightning_config.callbacks or OmegaConf.create()
+        callbacks_cfg = OmegaConf.merge(default_callbacks_cfg, callbacks_cfg)
+        trainer_kwargs['callbacks'] = [instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg]
+
+        trainer = Trainer.from_argparse_args(trainer_opt, **trainer_kwargs)
+
+        # data
+        data = instantiate_from_config(config.data)
+        # NOTE according to https://pytorch-lightning.readthedocs.io/en/latest/datamodules.html
+        # calling these ourselves should not be necessary but it is.
+        # lightning still takes care of proper multiprocessing though
+        data.prepare_data()
+        data.setup()
+
+        # configure learning rate
+        bs, base_lr = config.data.params.batch_size, config.model.base_learning_rate
+        if not cpu:
+            ngpu = len(lightning_config.trainer.gpus.strip(',').split(','))
+        else:
+            ngpu = 1
+        accumulate_grad_batches = lightning_config.trainer.accumulate_grad_batches or 1
+        print(f'accumulate_grad_batches = {accumulate_grad_batches}')
+        lightning_config.trainer.accumulate_grad_batches = accumulate_grad_batches
+        model.learning_rate = accumulate_grad_batches * ngpu * bs * base_lr
+        print('Setting learning rate to {:.2e} = {} (accumulate_grad_batches) * {} (num_gpus) * {} (batchsize) * {:.2e} (base_lr)'.format(
+            model.learning_rate, accumulate_grad_batches, ngpu, bs, base_lr))
+
+        # allow checkpointing via USR1
+        def melk(*args, **kwargs):
+            # run all checkpoint hooks
+            if trainer.global_rank == 0:
+                print('Summoning checkpoint.')
+                ckpt_path = os.path.join(ckptdir, 'last.ckpt')
+                trainer.save_checkpoint(ckpt_path)
+
+        def divein(*args, **kwargs):
+            if trainer.global_rank == 0:
+                import pudb; pudb.set_trace()
+
+        signal.signal(signal.SIGUSR1, melk)
+        signal.signal(signal.SIGUSR2, divein)
+
+        # run
+        if opt.train:
+            try:
+                trainer.fit(model, data)
+            except Exception:
+                melk()
+                raise
+        if not opt.no_test and not trainer.interrupted:
+            trainer.test(model, data)
+    except Exception:
+        if opt.debug and trainer.global_rank==0:
+            try:
+                import pudb as debugger
+            except ImportError:
+                import pdb as debugger
+            debugger.post_mortem()
+        raise
+    finally:
+        # move newly created debug project to debug_runs
+        if opt.debug and not opt.resume and trainer.global_rank==0:
+            dst, name = os.path.split(logdir)
+            dst = os.path.join(dst, 'debug_runs', name)
+            os.makedirs(os.path.split(dst)[0], exist_ok=True)
+            os.rename(logdir, dst)
diff --git a/vocoder/data/vggsound.csv b/vocoder/data/vggsound.csv
new file mode 100644
index 0000000..dbcbf5b
--- /dev/null
+++ b/vocoder/data/vggsound.csv
@@ -0,0 +1,199176 @@
+---g-f_I2yQ,1,people marching,test
+--0PQM4-hqg,30,waterfall burbling,train
+--56QUhyDQM,185,playing tennis,train
+--5OkAjCI7g,40,people belly laughing,train
+--8puiAGLhs,30,car engine starting,train
+--96EN9NUQM,242,alarm clock ringing,train
+--9O4XZOge4,30,"female speech, woman speaking",train
+--Aa4M484QM,0,cricket chirping,train
+--Bu2xe4OSo,430,wind noise,train
+--CC5pH97q4,4,foghorn,train
+--CC5pH97q4,33,foghorn,train
+--CudrykwoE,67,cricket chirping,train
+--CudrykwoE,78,cricket chirping,train
+--DTR0_mIGI,11,people battle cry,train
+--DTR0_mIGI,32,people battle cry,train
+--EquRwM9IQ,8,playing volleyball,train
+--FenyW2i_4,0,female singing,train
+--G-wKyj6JQ,30,playing harpsichord,train
+--HuBNHXu9I,30,"male speech, man speaking",train
+--I3Bjp_ptc,254,playing bassoon,train
+--IOqc-XgYo,0,playing piano,train
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diff --git a/vocoder/data/vggsound_test.txt b/vocoder/data/vggsound_test.txt
new file mode 100644
index 0000000..227999d
--- /dev/null
+++ b/vocoder/data/vggsound_test.txt
@@ -0,0 +1,14512 @@
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diff --git a/vocoder/data/vggsound_train.txt b/vocoder/data/vggsound_train.txt
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index 0000000..a956df6
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diff --git a/vocoder/data/vggsound_valid.txt b/vocoder/data/vggsound_valid.txt
new file mode 100644
index 0000000..8291426
--- /dev/null
+++ b/vocoder/data/vggsound_valid.txt
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diff --git a/vocoder/logs/vggsound/args.yml b/vocoder/logs/vggsound/args.yml
new file mode 100644
index 0000000..173f272
--- /dev/null
+++ b/vocoder/logs/vggsound/args.yml
@@ -0,0 +1,28 @@
+!!python/object:argparse.Namespace
+batch_size: 64
+cond_disc: false
+data_path: !!python/object/apply:pathlib.PosixPath
+- /
+- home
+- nvme
+- data
+- vggsound
+- features
+- melspec_10s_22050hz
+downsamp_factor: 4
+epochs: 3000
+lambda_feat: 10
+load_path: null
+log_interval: 100
+n_layers_D: 4
+n_mel_channels: 80
+n_residual_layers: 3
+n_test_samples: 8
+ndf: 16
+ngf: 32
+num_D: 3
+save_interval: 1000
+save_path: ./logs/vggsound
+seq_len: 8192
+splits_path: !!python/object/apply:pathlib.PosixPath
+- data
diff --git a/vocoder/logs/vggsound/best_netG.pt b/vocoder/logs/vggsound/best_netG.pt
new file mode 100644
index 0000000..3277182
Binary files /dev/null and b/vocoder/logs/vggsound/best_netG.pt differ
diff --git a/vocoder/mel2wav/__init__.py b/vocoder/mel2wav/__init__.py
new file mode 100644
index 0000000..889ccde
--- /dev/null
+++ b/vocoder/mel2wav/__init__.py
@@ -0,0 +1 @@
+from mel2wav.interface import load_model, MelVocoder
diff --git a/vocoder/mel2wav/dataset.py b/vocoder/mel2wav/dataset.py
new file mode 100644
index 0000000..4fa883b
--- /dev/null
+++ b/vocoder/mel2wav/dataset.py
@@ -0,0 +1,70 @@
+import torch
+import torch.utils.data
+import torch.nn.functional as F
+
+from librosa.core import load
+from librosa.util import normalize
+
+from pathlib import Path
+import numpy as np
+import random
+
+
+def files_to_list(filename, data_root):
+    """
+    Takes a text file of filenames and makes a list of filenames
+    """
+    with open(filename, encoding="utf-8") as f:
+        files = f.readlines()
+
+    files = [data_root / f'{f.rstrip()}_audio.npy' for f in files]
+    return files
+
+
+class AudioDataset(torch.utils.data.Dataset):
+    """
+    This is the main class that calculates the spectrogram and returns the
+    spectrogram, audio pair.
+    """
+
+    def __init__(self, data_root, split_path, segment_length, sampling_rate, augment=True):
+        self.sampling_rate = sampling_rate
+        self.segment_length = segment_length
+        self.audio_files = files_to_list(split_path, data_root)
+        # self.audio_files = [Path(training_files).parent / x for x in self.audio_files]
+        random.seed(1234)
+        random.shuffle(self.audio_files)
+        self.augment = augment
+
+    def __getitem__(self, index):
+        # Read audio
+        filename = self.audio_files[index]
+        audio, sampling_rate = self.load_wav_to_torch(filename)
+        # Take segment
+        if audio.size(0) >= self.segment_length:
+            max_audio_start = audio.size(0) - self.segment_length
+            audio_start = random.randint(0, max_audio_start)
+            audio = audio[audio_start:audio_start + self.segment_length]
+        else:
+            audio = F.pad(
+                audio, (0, self.segment_length - audio.size(0)), "constant"
+            ).data
+
+        # audio = audio / 32768.0
+        return audio.unsqueeze(0)
+
+    def __len__(self):
+        return len(self.audio_files)
+
+    def load_wav_to_torch(self, full_path):
+        """
+        Loads wavdata into torch array
+        """
+        data = np.load(full_path)
+        data = 0.95 * normalize(data)
+
+        if self.augment:
+            amplitude = np.random.uniform(low=0.3, high=1.0)
+            data = data * amplitude
+
+        return torch.from_numpy(data).float(), self.sampling_rate
diff --git a/vocoder/mel2wav/extract_mel_spectrogram.py b/vocoder/mel2wav/extract_mel_spectrogram.py
new file mode 100644
index 0000000..4af9b20
--- /dev/null
+++ b/vocoder/mel2wav/extract_mel_spectrogram.py
@@ -0,0 +1,208 @@
+import argparse
+import os
+import os.path as P
+from copy import deepcopy
+from functools import partial
+from glob import glob
+from multiprocessing import Pool
+from pathlib import Path
+
+import librosa
+import numpy as np
+import torchvision
+
+
+class MelSpectrogram(object):
+    def __init__(self, sr, nfft, fmin, fmax, nmels, hoplen, spec_power, inverse=False):
+        self.sr = sr
+        self.nfft = nfft
+        self.fmin = fmin
+        self.fmax = fmax
+        self.nmels = nmels
+        self.hoplen = hoplen
+        self.spec_power = spec_power
+        self.inverse = inverse
+
+        self.mel_basis = librosa.filters.mel(sr=sr, n_fft=nfft, fmin=fmin, fmax=fmax, n_mels=nmels)
+
+    def __call__(self, x):
+        if self.inverse:
+            spec = librosa.feature.inverse.mel_to_stft(
+                x, sr=self.sr, n_fft=self.nfft, fmin=self.fmin, fmax=self.fmax, power=self.spec_power
+            )
+            wav = librosa.griffinlim(spec, hop_length=self.hoplen)
+            return wav
+        else:
+            spec = np.abs(librosa.stft(x, n_fft=self.nfft, hop_length=self.hoplen)) ** self.spec_power
+            mel_spec = np.dot(self.mel_basis, spec)
+            return mel_spec
+
+class LowerThresh(object):
+    def __init__(self, min_val, inverse=False):
+        self.min_val = min_val
+        self.inverse = inverse
+
+    def __call__(self, x):
+        if self.inverse:
+            return x
+        else:
+            return np.maximum(self.min_val, x)
+
+class Add(object):
+    def __init__(self, val, inverse=False):
+        self.inverse = inverse
+        self.val = val
+
+    def __call__(self, x):
+        if self.inverse:
+            return x - self.val
+        else:
+            return x + self.val
+
+class Subtract(Add):
+    def __init__(self, val, inverse=False):
+        self.inverse = inverse
+        self.val = val
+
+    def __call__(self, x):
+        if self.inverse:
+            return x + self.val
+        else:
+            return x - self.val
+
+class Multiply(object):
+    def __init__(self, val, inverse=False) -> None:
+        self.val = val
+        self.inverse = inverse
+
+    def __call__(self, x):
+        if self.inverse:
+            return x / self.val
+        else:
+            return x * self.val
+
+class Divide(Multiply):
+    def __init__(self, val, inverse=False):
+        self.inverse = inverse
+        self.val = val
+
+    def __call__(self, x):
+        if self.inverse:
+            return x * self.val
+        else:
+            return x / self.val
+
+
+class Log10(object):
+    def __init__(self, inverse=False):
+        self.inverse = inverse
+
+    def __call__(self, x):
+        if self.inverse:
+            return 10 ** x
+        else:
+            return np.log10(x)
+
+class Clip(object):
+    def __init__(self, min_val, max_val, inverse=False):
+        self.min_val = min_val
+        self.max_val = max_val
+        self.inverse = inverse
+
+    def __call__(self, x):
+        if self.inverse:
+            return x
+        else:
+            return np.clip(x, self.min_val, self.max_val)
+
+class TrimSpec(object):
+    def __init__(self, max_len, inverse=False):
+        self.max_len = max_len
+        self.inverse = inverse
+
+    def __call__(self, x):
+        if self.inverse:
+            return x
+        else:
+            return x[:, :self.max_len]
+
+class MaxNorm(object):
+    def __init__(self, inverse=False):
+        self.inverse = inverse
+        self.eps = 1e-10
+
+    def __call__(self, x):
+        if self.inverse:
+            return x
+        else:
+            return x / (x.max() + self.eps)
+
+
+TRANSFORMS = torchvision.transforms.Compose([
+    MelSpectrogram(sr=22050, nfft=1024, fmin=125, fmax=7600, nmels=80, hoplen=1024//4, spec_power=1),
+    LowerThresh(1e-5),
+    Log10(),
+    Multiply(20),
+    Subtract(20),
+    Add(100),
+    Divide(100),
+    Clip(0, 1.0),
+    TrimSpec(860)
+])
+
+
+def inv_transforms(x, folder_name='melspec_10s_22050hz'):
+    '''relies on the GLOBAL contant TRANSFORMS which should be defined in this document'''
+    if folder_name == 'melspec_10s_22050hz':
+        i_transforms = deepcopy(TRANSFORMS.transforms[::-1])
+    else:
+        raise NotImplementedError
+    for t in i_transforms:
+        t.inverse = True
+    i_transforms = torchvision.transforms.Compose(i_transforms)
+    return i_transforms(x)
+
+
+def get_spectrogram(audio_path, save_dir, length, folder_name='melspec_10s_22050hz', save_results=True):
+    wav, _ = librosa.load(audio_path, sr=None)
+    # this cannot be a transform without creating a huge overhead with inserting audio_name in each
+    y = np.zeros(length)
+    if wav.shape[0] < length:
+        y[:len(wav)] = wav
+    else:
+        y = wav[:length]
+
+    if folder_name == 'melspec_10s_22050hz':
+        print('using', folder_name)
+        mel_spec = TRANSFORMS(y)
+    else:
+        raise NotImplementedError
+
+    if save_results:
+        os.makedirs(save_dir, exist_ok=True)
+        audio_name = os.path.basename(audio_path).split('.')[0]
+        np.save(P.join(save_dir, audio_name + '_mel.npy'), mel_spec)
+        np.save(P.join(save_dir, audio_name + '_audio.npy'), y)
+    else:
+        return y, mel_spec
+
+
+
+if __name__ == '__main__':
+    paser = argparse.ArgumentParser()
+    paser.add_argument("-i", "--input_dir", default="data/features/dog/audio_10s_22050hz")
+    paser.add_argument("-o", "--output_dir", default="data/features/dog/melspec_10s_22050hz")
+    paser.add_argument("-l", "--length", default=220500)
+    paser.add_argument("-n", '--num_worker', type=int, default=32)
+    args = paser.parse_args()
+    input_dir = args.input_dir
+    output_dir = args.output_dir
+    length = args.length
+
+    audio_paths = glob(P.join(input_dir, "*.wav"))
+    audio_paths.sort()
+
+    with Pool(args.num_worker) as p:
+        p.map(partial(
+            get_spectrogram, save_dir=output_dir, length=length, folder_name=Path(output_dir).name
+        ), audio_paths)
diff --git a/vocoder/mel2wav/interface.py b/vocoder/mel2wav/interface.py
new file mode 100644
index 0000000..8ea6c54
--- /dev/null
+++ b/vocoder/mel2wav/interface.py
@@ -0,0 +1,70 @@
+from mel2wav.modules import Generator, Audio2Mel
+
+from pathlib import Path
+import yaml
+import torch
+import os
+
+
+def get_default_device():
+    if torch.cuda.is_available():
+        return "cuda"
+    else:
+        return "cpu"
+
+
+def load_model(mel2wav_path, device=get_default_device()):
+    """
+    Args:
+        mel2wav_path (str or Path): path to the root folder of dumped text2mel
+        device (str or torch.device): device to load the model
+    """
+    root = Path(mel2wav_path)
+    with open(root / "args.yml", "r") as f:
+        args = yaml.load(f, Loader=yaml.FullLoader)
+    netG = Generator(args.n_mel_channels, args.ngf, args.n_residual_layers).to(device)
+    netG.load_state_dict(torch.load(root / "best_netG.pt", map_location=device))
+    return netG
+
+
+class MelVocoder:
+    def __init__(
+        self,
+        path,
+        device=get_default_device(),
+        github=False,
+        model_name="multi_speaker",
+    ):
+        self.fft = Audio2Mel().to(device)
+        if github:
+            netG = Generator(80, 32, 3).to(device)
+            root = Path(os.path.dirname(__file__)).parent
+            netG.load_state_dict(
+                torch.load(root / f"models/{model_name}.pt", map_location=device)
+            )
+            self.mel2wav = netG
+        else:
+            self.mel2wav = load_model(path, device)
+        self.device = device
+
+    def __call__(self, audio):
+        """
+        Performs audio to mel conversion (See Audio2Mel in mel2wav/modules.py)
+        Args:
+            audio (torch.tensor): PyTorch tensor containing audio (batch_size, timesteps)
+        Returns:
+            torch.tensor: log-mel-spectrogram computed on input audio (batch_size, 80, timesteps)
+        """
+        return self.fft(audio.unsqueeze(1).to(self.device))
+
+    def inverse(self, mel):
+        """
+        Performs mel2audio conversion
+        Args:
+            mel (torch.tensor): PyTorch tensor containing log-mel spectrograms (batch_size, 80, timesteps)
+        Returns:
+            torch.tensor:  Inverted raw audio (batch_size, timesteps)
+
+        """
+        with torch.no_grad():
+            return self.mel2wav(mel.to(self.device)).squeeze(1)
diff --git a/vocoder/mel2wav/modules.py b/vocoder/mel2wav/modules.py
new file mode 100644
index 0000000..da39c5d
--- /dev/null
+++ b/vocoder/mel2wav/modules.py
@@ -0,0 +1,199 @@
+import torch.nn as nn
+import torch.nn.functional as F
+import torch
+from librosa.filters import mel as librosa_mel_fn
+from torch.nn.utils import weight_norm
+import numpy as np
+
+
+def weights_init(m):
+    classname = m.__class__.__name__
+    if classname.find("Conv") != -1:
+        m.weight.data.normal_(0.0, 0.02)
+    elif classname.find("BatchNorm2d") != -1:
+        m.weight.data.normal_(1.0, 0.02)
+        m.bias.data.fill_(0)
+
+
+def WNConv1d(*args, **kwargs):
+    return weight_norm(nn.Conv1d(*args, **kwargs))
+
+
+def WNConvTranspose1d(*args, **kwargs):
+    return weight_norm(nn.ConvTranspose1d(*args, **kwargs))
+
+
+class Audio2Mel(nn.Module):
+    def __init__(
+        self,
+        n_fft=1024,
+        hop_length=256,
+        win_length=1024,
+        sampling_rate=22050,
+        n_mel_channels=80,
+        mel_fmin=0.0,
+        mel_fmax=None,
+    ):
+        super().__init__()
+        ##############################################
+        # FFT Parameters                              #
+        ##############################################
+        window = torch.hann_window(win_length).float()
+        mel_basis = librosa_mel_fn(
+            sampling_rate, n_fft, n_mel_channels, mel_fmin, mel_fmax
+        )
+        mel_basis = torch.from_numpy(mel_basis).float()
+        self.register_buffer("mel_basis", mel_basis)
+        self.register_buffer("window", window)
+        self.n_fft = n_fft
+        self.hop_length = hop_length
+        self.win_length = win_length
+        self.sampling_rate = sampling_rate
+        self.n_mel_channels = n_mel_channels
+
+    def forward(self, audio):
+        p = (self.n_fft - self.hop_length) // 2
+        audio = F.pad(audio, (p, p), "reflect").squeeze(1)
+        fft = torch.stft(
+            audio,
+            n_fft=self.n_fft,
+            hop_length=self.hop_length,
+            win_length=self.win_length,
+            window=self.window,
+            center=False,
+        )
+        real_part, imag_part = fft.unbind(-1)
+        magnitude = torch.sqrt(real_part ** 2 + imag_part ** 2)
+        mel_output = torch.matmul(self.mel_basis, magnitude)
+        log_mel_spec = torch.log10(torch.clamp(mel_output, min=1e-5))
+        return log_mel_spec
+
+
+class ResnetBlock(nn.Module):
+    def __init__(self, dim, dilation=1):
+        super().__init__()
+        self.block = nn.Sequential(
+            nn.LeakyReLU(0.2),
+            nn.ReflectionPad1d(dilation),
+            WNConv1d(dim, dim, kernel_size=3, dilation=dilation),
+            nn.LeakyReLU(0.2),
+            WNConv1d(dim, dim, kernel_size=1),
+        )
+        self.shortcut = WNConv1d(dim, dim, kernel_size=1)
+
+    def forward(self, x):
+        return self.shortcut(x) + self.block(x)
+
+
+class Generator(nn.Module):
+    def __init__(self, input_size, ngf, n_residual_layers):
+        super().__init__()
+        ratios = [8, 8, 2, 2]
+        self.hop_length = np.prod(ratios)
+        mult = int(2 ** len(ratios))
+
+        model = [
+            nn.ReflectionPad1d(3),
+            WNConv1d(input_size, mult * ngf, kernel_size=7, padding=0),
+        ]
+
+        # Upsample to raw audio scale
+        for i, r in enumerate(ratios):
+            model += [
+                nn.LeakyReLU(0.2),
+                WNConvTranspose1d(
+                    mult * ngf,
+                    mult * ngf // 2,
+                    kernel_size=r * 2,
+                    stride=r,
+                    padding=r // 2 + r % 2,
+                    output_padding=r % 2,
+                ),
+            ]
+
+            for j in range(n_residual_layers):
+                model += [ResnetBlock(mult * ngf // 2, dilation=3 ** j)]
+
+            mult //= 2
+
+        model += [
+            nn.LeakyReLU(0.2),
+            nn.ReflectionPad1d(3),
+            WNConv1d(ngf, 1, kernel_size=7, padding=0),
+            nn.Tanh(),
+        ]
+
+        self.model = nn.Sequential(*model)
+        self.apply(weights_init)
+
+    def forward(self, x):
+        return self.model(x)
+
+
+class NLayerDiscriminator(nn.Module):
+    def __init__(self, ndf, n_layers, downsampling_factor):
+        super().__init__()
+        model = nn.ModuleDict()
+
+        model["layer_0"] = nn.Sequential(
+            nn.ReflectionPad1d(7),
+            WNConv1d(1, ndf, kernel_size=15),
+            nn.LeakyReLU(0.2, True),
+        )
+
+        nf = ndf
+        stride = downsampling_factor
+        for n in range(1, n_layers + 1):
+            nf_prev = nf
+            nf = min(nf * stride, 1024)
+
+            model["layer_%d" % n] = nn.Sequential(
+                WNConv1d(
+                    nf_prev,
+                    nf,
+                    kernel_size=stride * 10 + 1,
+                    stride=stride,
+                    padding=stride * 5,
+                    groups=nf_prev // 4,
+                ),
+                nn.LeakyReLU(0.2, True),
+            )
+
+        nf = min(nf * 2, 1024)
+        model["layer_%d" % (n_layers + 1)] = nn.Sequential(
+            WNConv1d(nf_prev, nf, kernel_size=5, stride=1, padding=2),
+            nn.LeakyReLU(0.2, True),
+        )
+
+        model["layer_%d" % (n_layers + 2)] = WNConv1d(
+            nf, 1, kernel_size=3, stride=1, padding=1
+        )
+
+        self.model = model
+
+    def forward(self, x):
+        results = []
+        for key, layer in self.model.items():
+            x = layer(x)
+            results.append(x)
+        return results
+
+
+class Discriminator(nn.Module):
+    def __init__(self, num_D, ndf, n_layers, downsampling_factor):
+        super().__init__()
+        self.model = nn.ModuleDict()
+        for i in range(num_D):
+            self.model[f"disc_{i}"] = NLayerDiscriminator(
+                ndf, n_layers, downsampling_factor
+            )
+
+        self.downsample = nn.AvgPool1d(4, stride=2, padding=1, count_include_pad=False)
+        self.apply(weights_init)
+
+    def forward(self, x):
+        results = []
+        for key, disc in self.model.items():
+            results.append(disc(x))
+            x = self.downsample(x)
+        return results
diff --git a/vocoder/mel2wav/utils.py b/vocoder/mel2wav/utils.py
new file mode 100644
index 0000000..f2e21a4
--- /dev/null
+++ b/vocoder/mel2wav/utils.py
@@ -0,0 +1,28 @@
+import scipy.io.wavfile
+from mel2wav.extract_mel_spectrogram import TRANSFORMS
+import torch
+
+
+def save_sample(file_path, sampling_rate, audio):
+    """Helper function to save sample
+
+    Args:
+        file_path (str or pathlib.Path): save file path
+        sampling_rate (int): sampling rate of audio (usually 22050)
+        audio (torch.FloatTensor): torch array containing audio in [-1, 1]
+    """
+    audio = (audio.numpy() * 32768).astype("int16")
+    scipy.io.wavfile.write(file_path, sampling_rate, audio)
+
+def wav2mel(batch, wave_len=None):
+
+    if len(batch.shape) == 3:
+        assert batch.shape[1] == 1, 'Multi-channel audio?'
+        batch = batch.squeeze(1)
+
+    batch = torch.stack([torch.from_numpy(TRANSFORMS(e.numpy())) for e in batch.cpu()]).float()
+
+    if wave_len is not None:
+        batch = batch[:, :, :wave_len]
+
+    return batch
diff --git a/vocoder/modules.py b/vocoder/modules.py
new file mode 100644
index 0000000..da39c5d
--- /dev/null
+++ b/vocoder/modules.py
@@ -0,0 +1,199 @@
+import torch.nn as nn
+import torch.nn.functional as F
+import torch
+from librosa.filters import mel as librosa_mel_fn
+from torch.nn.utils import weight_norm
+import numpy as np
+
+
+def weights_init(m):
+    classname = m.__class__.__name__
+    if classname.find("Conv") != -1:
+        m.weight.data.normal_(0.0, 0.02)
+    elif classname.find("BatchNorm2d") != -1:
+        m.weight.data.normal_(1.0, 0.02)
+        m.bias.data.fill_(0)
+
+
+def WNConv1d(*args, **kwargs):
+    return weight_norm(nn.Conv1d(*args, **kwargs))
+
+
+def WNConvTranspose1d(*args, **kwargs):
+    return weight_norm(nn.ConvTranspose1d(*args, **kwargs))
+
+
+class Audio2Mel(nn.Module):
+    def __init__(
+        self,
+        n_fft=1024,
+        hop_length=256,
+        win_length=1024,
+        sampling_rate=22050,
+        n_mel_channels=80,
+        mel_fmin=0.0,
+        mel_fmax=None,
+    ):
+        super().__init__()
+        ##############################################
+        # FFT Parameters                              #
+        ##############################################
+        window = torch.hann_window(win_length).float()
+        mel_basis = librosa_mel_fn(
+            sampling_rate, n_fft, n_mel_channels, mel_fmin, mel_fmax
+        )
+        mel_basis = torch.from_numpy(mel_basis).float()
+        self.register_buffer("mel_basis", mel_basis)
+        self.register_buffer("window", window)
+        self.n_fft = n_fft
+        self.hop_length = hop_length
+        self.win_length = win_length
+        self.sampling_rate = sampling_rate
+        self.n_mel_channels = n_mel_channels
+
+    def forward(self, audio):
+        p = (self.n_fft - self.hop_length) // 2
+        audio = F.pad(audio, (p, p), "reflect").squeeze(1)
+        fft = torch.stft(
+            audio,
+            n_fft=self.n_fft,
+            hop_length=self.hop_length,
+            win_length=self.win_length,
+            window=self.window,
+            center=False,
+        )
+        real_part, imag_part = fft.unbind(-1)
+        magnitude = torch.sqrt(real_part ** 2 + imag_part ** 2)
+        mel_output = torch.matmul(self.mel_basis, magnitude)
+        log_mel_spec = torch.log10(torch.clamp(mel_output, min=1e-5))
+        return log_mel_spec
+
+
+class ResnetBlock(nn.Module):
+    def __init__(self, dim, dilation=1):
+        super().__init__()
+        self.block = nn.Sequential(
+            nn.LeakyReLU(0.2),
+            nn.ReflectionPad1d(dilation),
+            WNConv1d(dim, dim, kernel_size=3, dilation=dilation),
+            nn.LeakyReLU(0.2),
+            WNConv1d(dim, dim, kernel_size=1),
+        )
+        self.shortcut = WNConv1d(dim, dim, kernel_size=1)
+
+    def forward(self, x):
+        return self.shortcut(x) + self.block(x)
+
+
+class Generator(nn.Module):
+    def __init__(self, input_size, ngf, n_residual_layers):
+        super().__init__()
+        ratios = [8, 8, 2, 2]
+        self.hop_length = np.prod(ratios)
+        mult = int(2 ** len(ratios))
+
+        model = [
+            nn.ReflectionPad1d(3),
+            WNConv1d(input_size, mult * ngf, kernel_size=7, padding=0),
+        ]
+
+        # Upsample to raw audio scale
+        for i, r in enumerate(ratios):
+            model += [
+                nn.LeakyReLU(0.2),
+                WNConvTranspose1d(
+                    mult * ngf,
+                    mult * ngf // 2,
+                    kernel_size=r * 2,
+                    stride=r,
+                    padding=r // 2 + r % 2,
+                    output_padding=r % 2,
+                ),
+            ]
+
+            for j in range(n_residual_layers):
+                model += [ResnetBlock(mult * ngf // 2, dilation=3 ** j)]
+
+            mult //= 2
+
+        model += [
+            nn.LeakyReLU(0.2),
+            nn.ReflectionPad1d(3),
+            WNConv1d(ngf, 1, kernel_size=7, padding=0),
+            nn.Tanh(),
+        ]
+
+        self.model = nn.Sequential(*model)
+        self.apply(weights_init)
+
+    def forward(self, x):
+        return self.model(x)
+
+
+class NLayerDiscriminator(nn.Module):
+    def __init__(self, ndf, n_layers, downsampling_factor):
+        super().__init__()
+        model = nn.ModuleDict()
+
+        model["layer_0"] = nn.Sequential(
+            nn.ReflectionPad1d(7),
+            WNConv1d(1, ndf, kernel_size=15),
+            nn.LeakyReLU(0.2, True),
+        )
+
+        nf = ndf
+        stride = downsampling_factor
+        for n in range(1, n_layers + 1):
+            nf_prev = nf
+            nf = min(nf * stride, 1024)
+
+            model["layer_%d" % n] = nn.Sequential(
+                WNConv1d(
+                    nf_prev,
+                    nf,
+                    kernel_size=stride * 10 + 1,
+                    stride=stride,
+                    padding=stride * 5,
+                    groups=nf_prev // 4,
+                ),
+                nn.LeakyReLU(0.2, True),
+            )
+
+        nf = min(nf * 2, 1024)
+        model["layer_%d" % (n_layers + 1)] = nn.Sequential(
+            WNConv1d(nf_prev, nf, kernel_size=5, stride=1, padding=2),
+            nn.LeakyReLU(0.2, True),
+        )
+
+        model["layer_%d" % (n_layers + 2)] = WNConv1d(
+            nf, 1, kernel_size=3, stride=1, padding=1
+        )
+
+        self.model = model
+
+    def forward(self, x):
+        results = []
+        for key, layer in self.model.items():
+            x = layer(x)
+            results.append(x)
+        return results
+
+
+class Discriminator(nn.Module):
+    def __init__(self, num_D, ndf, n_layers, downsampling_factor):
+        super().__init__()
+        self.model = nn.ModuleDict()
+        for i in range(num_D):
+            self.model[f"disc_{i}"] = NLayerDiscriminator(
+                ndf, n_layers, downsampling_factor
+            )
+
+        self.downsample = nn.AvgPool1d(4, stride=2, padding=1, count_include_pad=False)
+        self.apply(weights_init)
+
+    def forward(self, x):
+        results = []
+        for key, disc in self.model.items():
+            results.append(disc(x))
+            x = self.downsample(x)
+        return results
diff --git a/vocoder/scripts/generate_from_folder.py b/vocoder/scripts/generate_from_folder.py
new file mode 100644
index 0000000..7893da6
--- /dev/null
+++ b/vocoder/scripts/generate_from_folder.py
@@ -0,0 +1,36 @@
+from mel2wav import MelVocoder
+
+from pathlib import Path
+from tqdm import tqdm
+import argparse
+import librosa
+import torch
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--load_path", type=Path, required=True)
+    parser.add_argument("--save_path", type=Path, required=True)
+    parser.add_argument("--folder", type=Path, required=True)
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+    vocoder = MelVocoder(args.load_path)
+
+    args.save_path.mkdir(exist_ok=True, parents=True)
+
+    for i, fname in tqdm(enumerate(args.folder.glob("*.wav"))):
+        wavname = fname.name
+        wav, sr = librosa.core.load(fname)
+
+        mel, _ = vocoder(torch.from_numpy(wav)[None])
+        recons = vocoder.inverse(mel).squeeze().cpu().numpy()
+
+        librosa.output.write_wav(args.save_path / wavname, recons, sr=sr)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/vocoder/scripts/train.py b/vocoder/scripts/train.py
new file mode 100644
index 0000000..66aee56
--- /dev/null
+++ b/vocoder/scripts/train.py
@@ -0,0 +1,233 @@
+import sys
+sys.path.insert(0, '.')  # nopep8
+
+from mel2wav.dataset import AudioDataset
+from mel2wav.modules import Generator, Discriminator
+from mel2wav.utils import save_sample, wav2mel
+
+import torch
+import torch.nn.functional as F
+from torch.utils.data import DataLoader
+from torch.utils.tensorboard import SummaryWriter
+
+import yaml
+import numpy as np
+import time
+import argparse
+from pathlib import Path
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--save_path", required=True)
+    parser.add_argument("--load_path", default=None)
+
+    parser.add_argument("--n_mel_channels", type=int, default=80)
+    parser.add_argument("--ngf", type=int, default=32)
+    parser.add_argument("--n_residual_layers", type=int, default=3)
+
+    parser.add_argument("--ndf", type=int, default=16)
+    parser.add_argument("--num_D", type=int, default=3)
+    parser.add_argument("--n_layers_D", type=int, default=4)
+    parser.add_argument("--downsamp_factor", type=int, default=4)
+    parser.add_argument("--lambda_feat", type=float, default=10)
+    parser.add_argument("--cond_disc", action="store_true")
+
+    parser.add_argument("--data_path", default=None, type=Path)
+    parser.add_argument("--splits_path", default='./data', type=Path)
+    parser.add_argument("--batch_size", type=int, default=16)
+    parser.add_argument("--seq_len", type=int, default=8192)
+
+    parser.add_argument("--epochs", type=int, default=100000)
+    parser.add_argument("--log_interval", type=int, default=100)
+    parser.add_argument("--save_interval", type=int, default=1000)
+    parser.add_argument("--n_test_samples", type=int, default=8)
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+
+    root = Path(args.save_path)
+    load_root = Path(args.load_path) if args.load_path else None
+    root.mkdir(parents=True, exist_ok=True)
+
+    ####################################
+    # Dump arguments and create logger #
+    ####################################
+    with open(root / "args.yml", "w") as f:
+        yaml.dump(args, f)
+    writer = SummaryWriter(str(root))
+
+    #######################
+    # Load PyTorch Models #
+    #######################
+    netG = Generator(args.n_mel_channels, args.ngf, args.n_residual_layers).cuda()
+    netD = Discriminator(args.num_D, args.ndf, args.n_layers_D, args.downsamp_factor).cuda()
+
+    print(netG)
+    print(netD)
+
+    #####################
+    # Create optimizers #
+    #####################
+    optG = torch.optim.Adam(netG.parameters(), lr=1e-4, betas=(0.5, 0.9))
+    optD = torch.optim.Adam(netD.parameters(), lr=1e-4, betas=(0.5, 0.9))
+
+    if load_root and load_root.exists():
+        netG.load_state_dict(torch.load(load_root / "netG.pt"))
+        optG.load_state_dict(torch.load(load_root / "optG.pt"))
+        netD.load_state_dict(torch.load(load_root / "netD.pt"))
+        optD.load_state_dict(torch.load(load_root / "optD.pt"))
+
+    #######################
+    # Create data loaders #
+    #######################
+    train_set = AudioDataset(
+        Path(args.data_path), Path(args.splits_path) / 'vggsound_train.txt', args.seq_len, sampling_rate=22050
+    )
+    test_set = AudioDataset(
+        Path(args.data_path),
+        Path(args.splits_path) / 'vggsound_test.txt',
+        22050 * 10,
+        sampling_rate=22050,
+        augment=False,
+    )
+
+    train_loader = DataLoader(train_set, batch_size=args.batch_size, num_workers=4)
+    test_loader = DataLoader(test_set, batch_size=1)
+
+    ##########################
+    # Dumping original audio #
+    ##########################
+    test_voc = []
+    test_audio = []
+    for i, x_t in enumerate(test_loader):
+        x_t = x_t.cuda()
+        s_t = wav2mel(x_t)
+
+        test_voc.append(s_t.cuda())
+        test_audio.append(x_t)
+
+        audio = x_t.squeeze().cpu()
+        save_sample(root / f'original_{i}.wav', 22050, audio)
+        writer.add_audio(f'original/sample_{i}.wav', audio, 0, sample_rate=22050)
+
+        if i == args.n_test_samples - 1:
+            break
+
+    costs = []
+    start = time.time()
+
+    # enable cudnn autotuner to speed up training
+    torch.backends.cudnn.benchmark = True
+
+    best_mel_reconst = 1000000
+    steps = 0
+    for epoch in range(1, args.epochs + 1):
+        for iterno, x_t in enumerate(train_loader):
+            x_t = x_t.cuda()
+            # librosa mel spec extraction returns weird length of specs. Therefore, we need to crop them
+            # 256 = hoplen
+            trim_len = x_t.shape[-1] // 256
+            s_t = wav2mel(x_t.squeeze(1), trim_len)
+            x_pred_t = netG(s_t.cuda())
+
+            with torch.no_grad():
+                s_pred_t = wav2mel(x_pred_t.squeeze(1).detach().cpu(), trim_len)
+                s_error = F.l1_loss(s_t, s_pred_t).item()
+
+            #######################
+            # Train Discriminator #
+            #######################
+            D_fake_det = netD(x_pred_t.detach())
+            D_real = netD(x_t)
+
+            loss_D = 0
+            for scale in D_fake_det:
+                loss_D += F.relu(1 + scale[-1]).mean()
+
+            for scale in D_real:
+                loss_D += F.relu(1 - scale[-1]).mean()
+
+            netD.zero_grad()
+            loss_D.backward()
+            optD.step()
+
+            ###################
+            # Train Generator #
+            ###################
+            D_fake = netD(x_pred_t)
+
+            loss_G = 0
+            for scale in D_fake:
+                loss_G += -scale[-1].mean()
+
+            loss_feat = 0
+            feat_weights = 4.0 / (args.n_layers_D + 1)
+            D_weights = 1.0 / args.num_D
+            wt = D_weights * feat_weights
+            for i in range(args.num_D):
+                for j in range(len(D_fake[i]) - 1):
+                    loss_feat += wt * F.l1_loss(D_fake[i][j], D_real[i][j].detach())
+
+            netG.zero_grad()
+            (loss_G + args.lambda_feat * loss_feat).backward()
+            optG.step()
+
+            ######################
+            # Update tensorboard #
+            ######################
+            costs.append([loss_D.item(), loss_G.item(), loss_feat.item(), s_error])
+
+            writer.add_scalar("loss/discriminator", costs[-1][0], steps)
+            writer.add_scalar("loss/generator", costs[-1][1], steps)
+            writer.add_scalar("loss/feature_matching", costs[-1][2], steps)
+            writer.add_scalar("loss/mel_reconstruction", costs[-1][3], steps)
+            steps += 1
+
+            if steps % args.save_interval == 0:
+                st = time.time()
+                with torch.no_grad():
+                    for i, (voc, _) in enumerate(zip(test_voc, test_audio)):
+                        pred_audio = netG(voc)
+                        pred_audio = pred_audio.squeeze().cpu()
+                        save_sample(root / ("generated_%d.wav" % i), 22050, pred_audio)
+                        writer.add_audio(
+                            "generated/sample_%d.wav" % i,
+                            pred_audio,
+                            epoch,
+                            sample_rate=22050,
+                        )
+
+                torch.save(netG.state_dict(), root / "netG.pt")
+                torch.save(optG.state_dict(), root / "optG.pt")
+
+                torch.save(netD.state_dict(), root / "netD.pt")
+                torch.save(optD.state_dict(), root / "optD.pt")
+
+                if np.asarray(costs).mean(0)[-1] < best_mel_reconst:
+                    best_mel_reconst = np.asarray(costs).mean(0)[-1]
+                    torch.save(netD.state_dict(), root / "best_netD.pt")
+                    torch.save(netG.state_dict(), root / "best_netG.pt")
+
+                print("Took %5.4fs to generate samples" % (time.time() - st))
+                print("-" * 100)
+
+            if steps % args.log_interval == 0:
+                print(
+                    "Epoch {} | Iters {} / {} | ms/batch {:5.2f} | loss {}".format(
+                        epoch,
+                        iterno,
+                        len(train_loader),
+                        1000 * (time.time() - start) / args.log_interval,
+                        np.asarray(costs).mean(0),
+                    )
+                )
+                costs = []
+                start = time.time()
+
+
+if __name__ == "__main__":
+    main()