Text-to-speech with The Massively Multilingual Speech (MMS) project (facebookresearch)

This project is a TTS-MMS testing lab for study Facebook's TTSMMS project specific to Shan (lang_code: shn) language.

Install

clone this project

git clone https://github.com/haohaaorg/ttsmms_lab.git

cd ttsmms_lab

pip install -r requirements.txt

download tts model from facebookresearch and extract to model/

mkdir -p model/shn/ && wget -qO- https://dl.fbaipublicfiles.com/mms/tts/shn.tar.gz | tar -xz -C model/shn/ --strip-components 1

Usage

use this shn_tts.py file or create a new one for using Shan's language model

from ttsmms import TTS

tts=TTS("./model/shn")

tts.synthesis("ၼုမ်ႇသိုၵ်းႁၢၼ် ႁဵတ်းၵၢၼ်ၵွၼ်းၶေႃၸိုင်ႈတႆး", wav_path="output/example_shn.wav")
# output: output/example_shn.wav file

use this eng_tts.py file file or create a new one for using English's language model

from ttsmms import TTS

tts = TTS("./model/eng")

tts.synthesis("speech", wav_path="output/example_eng.wav")
# output: output/example_eng.wav file

or with combine 2 model, as this is a pre-train model which may not support all text or words the idea is to use both english and shan model for multilang in single line text

• shn_tts_combined.py

don't forget to download english model from

 mkdir -p model/eng/ && wget -qO- https://dl.fbaipublicfiles.com/mms/tts/eng.tar.gz | tar -xz -C model/eng/ --strip-components 1

Run

python shn_tts.py

# python eng_tts.py
# python shn_tts_combined.py

Run with ipynb

Run with GPU (NVIDIA-CUDA) make a difference in term of precision and accuracy, for example

• Run with local CPU (intel core-i5) - example_CPU_local_run.wav
• Run with GPU (Google Colab with CUDA) - example_GPU_google_colab_run.wav

Google-Colab: fairseq_lab.ipynb - require python3.8

License

MIT