Awesome
LightGrad: Lightweight Diffusion Probabilistic Model for Text-to-speech
Demos are available at: https://thuhcsi.github.io/LightGrad/
Setup Environment
Install python 3.10.
Then, run:
git clone --recursive https://github.com/thuhcsi/LightGrad.git
python -m pip install -r requirements.txt
Training
Preprocess for BZNSYP
Download dataset from url. Run
python preprocess.py bznsyp [PATH_TO_DIRECTORY_CONTAINING_DATASET] \
[PATH_TO_DIRECTORY_FOR_SAVING_PREPROCESS_RESULTS] \
--test_sample_count 200 --valid_sample_count 200
This will produce phn2id.json
, train_dataset.json
, test_dataset.json
, valid_dataset.json
in [PATH_TO_DIRECTORY_FOR_SAVING_PREPROCESS_RESULTS]
.
Preprocess for LJSpeech
Download dataset from url. Run
python preprocess.py ljspeech [PATH_TO_DIRECTORY_CONTAINING_DATASET] \
[PATH_TO_DIRECTORY_FOR_SAVING_PREPROCESS_RESULTS] \
--test_sample_count 200 --valid_sample_count 200
This will produce phn2id.json
, train_dataset.json
, test_dataset.json
, valid_dataset.json
in [PATH_TO_DIRECTORY_FOR_SAVING_PREPROCESS_RESULTS]
.
Training for BZNSYP
Edit config/bznsyp_config.yaml
, set train_datalist_path
, valid_datalist_path
, phn2id_path
and log_dir
.
Run:
python train.py -c config/bznsyp_config.yaml
Training for LJSpeech
Edit config/ljspeech_config.yaml
, set train_datalist_path
, valid_datalist_path
, phn2id_path
and log_dir
.
Run:
python train.py -c config/ljspeech_config.yaml
Inference
Edit inference.ipynb
.
Set HiFiGAN_CONFIG
, HiFiGAN_ckpt
and ckpt_path
to corresponding files, respectively.
- Note:
add_blank
ininference.ipynb
should be the same as that inLightGrad/dataset.py
.