WeTTS

Production First and Production Ready End-to-End Text-to-Speech Toolkit

Note: This project is at its early statge now. Its design and implementation are subjected to change.

Install

We suggest installing WeTTS with Anaconda or Miniconda. Clone this repo:

git clone https://github.com/wenet-e2e/wetts.git

Create environment:

conda create -n wetts python=3.8 -y
conda activate wetts
pip install -r requirements.txt
conda install -n wetts pytorch=1.11 torchaudio cudatoolkit=10.2 -c pytorch -c conda-forge -y

Please note you should use cudatoolkit=11.3 for CUDA 11.3.

Roadmap

We mainly focus on end to end, production, and on-device TTS. We are going to use:

backend: end to end model, such as:
- VITS
frontend:
- Text Normalization: WeTextProcessing
- Prosody & Polyphones: Unified Mandarin TTS Front-end Based on Distilled BERT Model

Dataset

We plan to support a variaty of open source TTS datasets, include but not limited to:

baker, Chinese Standard Mandarin Speech corpus open sourced by Data Baker.
AISHELL-3, a large-scale and high-fidelity multi-speaker Mandarin speech corpus.
Opencpop, Mandarin singing voice synthesis (SVS) corpus open sourced by Netease Fuxi.

Runtime

We plan to support a variaty of hardwares and platforms, including:

x86
Android
Raspberry Pi
Other on-device platforms

Discussion & Communication

For Chinese users, you can aslo scan the QR code on the left to follow our offical account of WeNet. We created a WeChat group for better discussion and quicker response. Please scan the personal QR code on the right, and the guy is responsible for inviting you to the chat group.

Or you can directly discuss on Github Issues.

Acknowledgement

We borrow a lot of code from vits for VITS implementation.
We refer PaddleSpeech for pinyin lexicon generation.

donstang/wetts