VoiceCraft/README.md

# VoiceCraft: Zero-Shot Speech Editing and Text-to-Speech in the Wild
[Demo](https://jasonppy.github.io/VoiceCraft_web) [Paper](https://jasonppy.github.io/assets/pdfs/VoiceCraft.pdf)


### TL;DR
VoiceCraft is a token infilling neural codec language model, that achieves state-of-the-art performance on both **speech editing** and **zero-shot text-to-speech (TTS)** on in-the-wild data including audiobooks, internet videos, and podcasts.

To clone or edit an unseen voice, VoiceCraft needs only a few seconds of reference.

## How to run inference
There are three ways:

1. with Google Colab. see [quickstart colab](#quickstart-colab)
2. with docker. see [quickstart docker](#quickstart-docker)
3. without docker. see [environment setup](#environment-setup)

When you are inside the docker image or you have installed all dependencies, Checkout [`inference_tts.ipynb`](./inference_tts.ipynb).

If you want to do model development such as training/finetuning, I recommend following [envrionment setup](#environment-setup) and [training](#training).

## News
:star: 03/28/2024: Model weights for giga330M and giga830M are up on HuggingFace🤗 [here](https://huggingface.co/pyp1/VoiceCraft/tree/main)!

:star: 04/05/2024: I finetuned giga330M with the TTS objective on gigaspeech and 1/5 of librilight, the model outperforms giga830M on TTS. Weights are [here](https://huggingface.co/pyp1/VoiceCraft/tree/main). Make sure maximal prompt + generation length <= 16 seconds (due to our limited compute, we had to drop utterances longer than 16s in training data)

## TODO
- [x] Codebase upload
- [x] Environment setup
- [x] Inference demo for speech editing and TTS
- [x] Training guidance
- [x] RealEdit dataset and training manifest
- [x] Model weights (giga330M.pth, giga830M.pth, and gigaHalfLibri330M_TTSEnhanced_max16s.pth)
- [x] Write colab notebooks for better hands-on experience
- [ ] HuggingFace Spaces demo
- [ ] Better guidance on training/finetuning


## QuickStart Colab

:star: To try out speech editing or TTS Inference with VoiceCraft, the simplest way is using Google Colab.
Instructions to run are on the Colab itself.

1. To try [Speech Editing](https://colab.research.google.com/drive/1FV7EC36dl8UioePY1xXijXTMl7X47kR_?usp=sharing)
2. To try [TTS Inference](https://colab.research.google.com/drive/1lch_6it5-JpXgAQlUTRRI2z2_rk5K67Z?usp=sharing)

## QuickStart Docker
:star: To try out TTS inference with VoiceCraft, you can also use docker. Thank [@ubergarm](https://github.com/ubergarm) and [@jayc88](https://github.com/jay-c88) for making this happen.

Tested on Linux and Windows and should work with any host with docker installed.
```bash
# 1. clone the repo on in a directory on a drive with plenty of free space
git clone git@github.com:jasonppy/VoiceCraft.git
cd VoiceCraft

# 2. assumes you have docker installed with nvidia container container-toolkit (windows has this built into the driver)
# https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/1.13.5/install-guide.html
# sudo apt-get install -y nvidia-container-toolkit-base || yay -Syu nvidia-container-toolkit || echo etc...

# 3. First build the docker image
docker build --tag "voicecraft" .

# 4. Try to start an existing container otherwise create a new one passing in all GPUs
./start-jupyter.sh  # linux
start-jupyter.bat   # windows

# 5. now open a webpage on the host box to the URL shown at the bottom of:
docker logs jupyter

# 6. optionally look inside from another terminal
docker exec -it jupyter /bin/bash
export USER=(your_linux_username_used_above)
export HOME=/home/$USER
sudo apt-get update

# 7. confirm video card(s) are visible inside container
nvidia-smi

# 8. Now in browser, open inference_tts.ipynb and work through one cell at a time
echo GOOD LUCK
```

## Environment setup
```bash
conda create -n voicecraft python=3.9.16
conda activate voicecraft

pip install -e git+https://github.com/facebookresearch/audiocraft.git@c5157b5bf14bf83449c17ea1eeb66c19fb4bc7f0#egg=audiocraft
pip install xformers==0.0.22
pip install torchaudio==2.0.2 torch==2.0.1 # this assumes your system is compatible with CUDA 11.7, otherwise checkout https://pytorch.org/get-started/previous-versions/#v201
apt-get install ffmpeg # if you don't already have ffmpeg installed
apt-get install espeak-ng # backend for the phonemizer installed below
pip install tensorboard==2.16.2
pip install phonemizer==3.2.1
pip install datasets==2.16.0
pip install torchmetrics==0.11.1
# install MFA for getting forced-alignment, this could take a few minutes
conda install -c conda-forge montreal-forced-aligner=2.2.17 openfst=1.8.2 kaldi=5.5.1068
# conda install pocl # above gives an warning for installing pocl, not sure if really need this

# to run ipynb
conda install -n voicecraft ipykernel --no-deps --force-reinstall

# below is only needed if you want to run gradio_app.py
sudo apt-get install espeak # NOTE: only required if you want to use gradio_app, which is used by whisperx for forced alignment
sudo apt-get install libespeak-dev # NOTE: only required if you want to use gradio_app, which is used by whisperx for forced alignment
```

If you have encountered version issues when running things, checkout [environment.yml](./environment.yml) for exact matching.

## Inference Examples
Checkout [`inference_speech_editing.ipynb`](./inference_speech_editing.ipynb) and [`inference_tts.ipynb`](./inference_tts.ipynb)

## Gradio
After environment setup install additional dependencies:
```bash
apt-get install -y espeak espeak-data libespeak1 libespeak-dev
apt-get install -y festival*
apt-get install -y build-essential
apt-get install -y flac libasound2-dev libsndfile1-dev vorbis-tools
apt-get install -y libxml2-dev libxslt-dev zlib1g-dev
pip install -r gradio_requirements.txt
```

Run gradio server from terminal or [`gradio_app.ipynb`](./gradio_app.ipynb):
```bash
python gradio_app.py
TMP_PATH=/tmp python gradio_app.py # if you want to change tmp folder path
```
It is ready to use on [default url](http://127.0.0.1:7860).

### How to use it
1. (optionally) Select models
2. Load models
3. Transcribe
4. (optionally) Tweak some parameters
5. Run
6. (optionally) Rerun part-by-part in Long TTS mode

### Some features
Smart transcript: write only what you want to generate

TTS mode: Zero-shot TTS

Edit mode: Speech editing

Long TTS mode: Easy TTS on long texts


## Training
To train an VoiceCraft model, you need to prepare the following parts:
1. utterances and their transcripts
2. encode the utterances into codes using e.g. Encodec
3. convert transcripts into phoneme sequence, and a phoneme set (we named it vocab.txt)
4. manifest (i.e. metadata)

Step 1,2,3 are handled in [./data/phonemize_encodec_encode_hf.py](./data/phonemize_encodec_encode_hf.py), where
1. Gigaspeech is downloaded through HuggingFace. Note that you need to sign an agreement in order to download the dataset (it needs your auth token)
2. phoneme sequence and encodec codes are also extracted using the script.

An example run:

```bash
conda activate voicecraft
export CUDA_VISIBLE_DEVICES=0
cd ./data
python phonemize_encodec_encode_hf.py \
--dataset_size xs \
--download_to path/to/store_huggingface_downloads \
--save_dir path/to/store_extracted_codes_and_phonemes \
--encodec_model_path path/to/encodec_model \
--mega_batch_size 120 \
--batch_size 32 \
--max_len 30000
```
where encodec_model_path is avaliable [here](https://huggingface.co/pyp1/VoiceCraft). This model is trained on Gigaspeech XL, it has 56M parameters, 4 codebooks, each codebook has 2048 codes. Details are described in our [paper](https://jasonppy.github.io/assets/pdfs/VoiceCraft.pdf). If you encounter OOM during extraction, try decrease the batch_size and/or max_len.
The extracted codes, phonemes, and vocab.txt will be stored at `path/to/store_extracted_codes_and_phonemes/${dataset_size}/{encodec_16khz_4codebooks,phonemes,vocab.txt}`.

As for manifest, please download train.txt and validation.txt from [here](https://huggingface.co/datasets/pyp1/VoiceCraft_RealEdit/tree/main), and put them under `path/to/store_extracted_codes_and_phonemes/manifest/`. Please also download vocab.txt from [here](https://huggingface.co/datasets/pyp1/VoiceCraft_RealEdit/tree/main) if you want to use our pretrained VoiceCraft model (so that the phoneme-to-token matching is the same).

Now, you are good to start training!

```bash
conda activate voicecraft
cd ./z_scripts
bash e830M.sh
```


## License
The codebase is under CC BY-NC-SA 4.0 ([LICENSE-CODE](./LICENSE-CODE)), and the model weights are under Coqui Public Model License 1.0.0 ([LICENSE-MODEL](./LICENSE-MODEL)). Note that we use some of the code from other repository that are under different licenses: `./models/codebooks_patterns.py` is under MIT license; `./models/modules`, `./steps/optim.py`, `data/tokenizer.py` are under Apache License, Version 2.0; the phonemizer we used is under GNU 3.0 License.

<!-- How to use g2p to convert english text into IPA phoneme sequence
first install it with `pip install g2p`
```python
from g2p import make_g2p
transducer = make_g2p('eng', 'eng-ipa')
transducer("hello").output_string
# it will output: 'hʌloʊ'
``` -->

## Acknowledgement
We thank Feiteng for his [VALL-E reproduction](https://github.com/lifeiteng/vall-e), and we thank audiocraft team for open-sourcing [encodec](https://github.com/facebookresearch/audiocraft).

## Citation
```
@article{peng2024voicecraft,
  author    = {Peng, Puyuan and Huang, Po-Yao and Li, Daniel and Mohamed, Abdelrahman and Harwath, David},
  title     = {VoiceCraft: Zero-Shot Speech Editing and Text-to-Speech in the Wild},
  journal   = {arXiv},
  year      = {2024},
}
```

## Disclaimer
Any organization or individual is prohibited from using any technology mentioned in this paper to generate or edit someone's speech without his/her consent, including but not limited to government leaders, political figures, and celebrities. If you do not comply with this item, you could be in violation of copyright laws.