Bark voice cloning

Please read

This code works on python 3.10, i have not tested it on other versions. Some older versions will have issues.

Voice cloning with bark in high quality?

It's possible now.

examples_biden_example.mov

How do I clone a voice?

For developers:

• code examples on huggingface model page

For everyone:

• audio-webui with bark and voice cloning
• online huggingface voice cloning space
• interactive python notebook

Voices cloned aren't very convincing, why are other people's cloned voices better than mine?

Make sure these things are NOT in your voice input: (in no particular order)

• Noise (You can use a noise remover before)
• Music (There are also music remover tools) (Unless you want music in the background)
• A cut-off at the end (This will cause it to try and continue on the generation)
• Under 1 second of training data (i personally suggest around 10 seconds for good potential, but i've had great results with 5 seconds as well.)

What makes for good prompt audio? (in no particular order)

• Clearly spoken
• No weird background noises
• Only one speaker
• Audio which ends after a sentence ends
• Regular/common voice (They usually have more success, it's still capable of cloning complex voices, but not as good at it)
• Around 10 seconds of data

Pretrained models

Official

Name	HuBERT Model	Quantizer Version	Epoch	Language	Dataset
quantifier_hubert_base_ls960.pth	HuBERT Base	0	3	ENG	GitMylo/bark-semantic-training
quantifier_hubert_base_ls960_14.pth	HuBERT Base	0	14	ENG	GitMylo/bark-semantic-training
quantifier_V1_hubert_base_ls960_23.pth	HuBERT Base	1	23	ENG	GitMylo/bark-semantic-training

Community

Author	Name	HuBERT Model	Quantizer Version	Epoch	Language	Dataset
HobisPL	polish-HuBERT-quantizer_8_epoch.pth	HuBERT Base	1	8	POL	Hobis/bark-polish-semantic-wav-training
C0untFloyd	german-HuBERT-quantizer_14_epoch.pth	HuBERT Base	1	14	GER	CountFloyd/bark-german-semantic-wav-training
gc-pwr	it_tokenizer.pth	HuBERT Base	1	24	ITA

For developers: Implementing voice cloning in your bark projects

• Simply copy the files from this directory into your project.
• The hubert manager contains methods to download HuBERT and the custom Quantizer model.
• Loading the CustomHuBERT should be pretty straightforward
• The notebook contains code to use on cuda or cpu. Instead of just cpu.

from hubert.pre_kmeans_hubert import CustomHubert
import torchaudio

# Load the HuBERT model,
# checkpoint_path should work fine with data/models/hubert/hubert.pt for the default config
hubert_model = CustomHubert(checkpoint_path='path/to/checkpoint')

# Run the model to extract semantic features from an audio file, where wav is your audio file
wav, sr = torchaudio.load('path/to/wav') # This is where you load your wav, with soundfile or torchaudio for example

if wav.shape[0] == 2:  # Stereo to mono if needed
    wav = wav.mean(0, keepdim=True)

semantic_vectors = hubert_model.forward(wav, input_sample_hz=sr)

• Loading and running the custom kmeans

import torch
from hubert.customtokenizer import CustomTokenizer

# Load the CustomTokenizer model from a checkpoint
# With default config, you can use the pretrained model from huggingface
# With the default setup from HuBERTManager, this will be in data/models/hubert/tokenizer.pth
tokenizer = CustomTokenizer.load_from_checkpoint('data/models/hubert/tokenizer.pth')  # Automatically uses the right layers

# Process the semantic vectors from the previous HuBERT run (This works in batches, so you can send the entire HuBERT output)
semantic_tokens = tokenizer.get_token(semantic_vectors)

# Congratulations! You now have semantic tokens which can be used inside of a speaker prompt file.

How do I train it myself?

Simply run the training commands.

A simple way to create semantic data and wavs for training, is with my script: bark-data-gen. But remember that the creation of the wavs will take around the same time if not longer than the creation of the semantics. This can take a while to generate because of that.

For example, if you have a dataset with zips containing audio files, one zip for semantics, and one for the wav files. Inside of a folder called "Literature"

You should run process.py --path Literature --mode prepare for extracting all the data to one directory

You should run process.py --path Literature --mode prepare2 for creating HuBERT semantic vectors, ready for training

You should run process.py --path Literature --mode train for training

And when your model has trained enough, you can run process.py --path Literature --mode test to test the latest model.

Disclaimer

I am not responsible for audio generated using semantics created by this model. Just don't use it for illegal purposes.