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

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

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

For CUDA 10.2, run:

conda create -n wetts python=3.8 montreal-forced-aligner pytorch=1.11 \
torchaudio cudatoolkit=10.2 -c pytorch -c conda-forge

For CUDA 11.3, run:

conda create -n wetts python=3.8 montreal-forced-aligner pytorch=1.11 \
torchaudio cudatoolkit=11.3 -c pytorch -c conda-forge

Installing other dependencies using:

conda activate wetts
python -m pip install -r requirements.txt

We mainly focus on production and on-device TTS, and we plan to use:

• AM: FastSpeech2
• vocoder: hifigan/melgan

And we are going to provide reference solution of:

• Prosody
• Polyphones
• Text Normalization

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

• BZNSYP, 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.

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

• x86
• Android
• Raspberry Pi
• Other on-device platforms

1. We borrow some code from FastSpeech2 for FastSpeech2 implentation.
2. We refer PaddleSpeech for feature extraction, pinyin lexicon preparation for alignment, and the length regulator in FastSpeech2.