Skip to content

Latest commit

 

History

History
117 lines (85 loc) · 6.3 KB

readme.md

File metadata and controls

117 lines (85 loc) · 6.3 KB
Raw

GenerSpeech: Towards Style Transfer for Generalizable Out-Of-Domain Text-to-Speech

Rongjie Huang, Yi Ren, Jinglin Liu, Chenye Cui, Zhou Zhao | Zhejiang University, Sea AI Lab

PyTorch Implementation of GenerSpeech (NeurIPS'22): a text-to-speech model towards high-fidelity zero-shot style transfer of OOD custom voice.

arXiv GitHub Stars visitors

We provide our implementation and pretrained models in this repository.

Visit our demo page for audio samples.

News

  • December, 2022: GenerSpeech (NeurIPS 2022) released at Github.

Key Features

  • Multi-level Style Transfer for expressive text-to-speech.
  • Enhanced model generalization to out-of-distribution (OOD) style reference.

Quick Started

We provide an example of how you can generate high-fidelity samples using GenerSpeech.

To try on your own dataset, simply clone this repo in your local machine provided with NVIDIA GPU + CUDA cuDNN and follow the below instructions.

Support Datasets and Pretrained Models

You can use pretrained models we provide here. Details of each folder are as in follows:

Model Dataset (16 kHz) Discription
GenerSpeech LibriTTS,ESD Acousitic model (config)
HIFI-GAN LibriTTS,ESD Neural Vocoder
Encoder / Emotion Encoder

More supported datasets are coming soon.

Dependencies

A suitable conda environment named generspeech can be created and activated with:

conda env create -f environment.yaml
conda activate generspeech

Multi-GPU

By default, this implementation uses as many GPUs in parallel as returned by torch.cuda.device_count(). You can specify which GPUs to use by setting the CUDA_DEVICES_AVAILABLE environment variable before running the training module.

Inference towards style transfer of custom voice

Here we provide a speech synthesis pipeline using GenerSpeech.

  1. Prepare GenerSpeech (acoustic model): Download and put checkpoint at checkpoints/GenerSpeech
  2. Prepare HIFI-GAN (neural vocoder): Download and put checkpoint at checkpoints/trainset_hifigan
  3. Prepare Emotion Encoder: Download and put checkpoint at checkpoints/Emotion_encoder.pt
  4. Prepare dataset: Download and put statistical files at data/binary/training_set
  5. Prepare path/to/reference_audio (16k): By default, GenerSpeech uses ASR + MFA to obtain the text-speech alignment from reference.
CUDA_VISIBLE_DEVICES=$GPU python inference/GenerSpeech.py --config modules/GenerSpeech/config/generspeech.yaml  --exp_name GenerSpeech --hparams="text='here we go',ref_audio='assets/0011_001570.wav'"

Generated wav files are saved in infer_out by default.

Train your own model

Data Preparation and Configuration

  1. Set raw_data_dir, processed_data_dir, binary_data_dir in the config file, and download dataset to raw_data_dir.
  2. Check preprocess_cls in the config file. The dataset structure needs to follow the processor preprocess_cls, or you could rewrite it according to your dataset. We provide a Libritts processor as an example in modules/GenerSpeech/config/generspeech.yaml
  3. Download global emotion encoder to emotion_encoder_path. For more details, please refer to this branch.
  4. Preprocess Dataset
# Preprocess step: unify the file structure.
python data_gen/tts/bin/preprocess.py --config $path/to/config
# Align step: MFA alignment.
python data_gen/tts/bin/train_mfa_align.py --config $path/to/config
# Binarization step: Binarize data for fast IO.
CUDA_VISIBLE_DEVICES=$GPU python data_gen/tts/bin/binarize.py --config $path/to/config

You could also build a dataset via NATSpeech, which shares a common MFA data-processing procedure. We also provide our processed dataset (16kHz LibriTTS+ESD) here.

Training GenerSpeech

CUDA_VISIBLE_DEVICES=$GPU python tasks/run.py --config modules/GenerSpeech/config/generspeech.yaml  --exp_name GenerSpeech --reset

Inference using GenerSpeech

CUDA_VISIBLE_DEVICES=$GPU python tasks/run.py --config modules/GenerSpeech/config/generspeech.yaml  --exp_name GenerSpeech --infer

Acknowledgements

This implementation uses parts of the code from the following Github repos: FastDiff, NATSpeech, as described in our code.

Citations

If you find this code useful in your research, please cite our work:

@inproceedings{huanggenerspeech,
  title={GenerSpeech: Towards Style Transfer for Generalizable Out-Of-Domain Text-to-Speech},
  author={Huang, Rongjie and Ren, Yi and Liu, Jinglin and Cui, Chenye and Zhao, Zhou},
  booktitle={Advances in Neural Information Processing Systems}
}

Disclaimer

Any organization or individual is prohibited from using any technology mentioned in this paper to generate 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.