mirror of https://github.com/coqui-ai/TTS.git
bc422f2f3c
* added EnergyDataset * add energy to Dataset * add comupte_energy * added energy params * added energy to forward_tts * added plot_avg_energy for visualisation * Update forward_tts.py * create file * added fastspeech2 recipe * add fastspeech2 config * removed energy from fast pitch * add energy loss to forward tts * Update fastspeech2_config.py * change run_name * Update numpy_transforms.py * fix typo * fix typo * fix typo * linting issues * use_energy default value --> False * Update numpy_transforms.py * linting fixes * fix typo * liniting_fix * liniting_fix * fix * fixes * fixes * lint fix * lint fixws * added training test * wrong import * wrong import * trailing whitespace * style fix * changed class name because of error * class name change * class name change * change class name * fixed styles |
||
|---|---|---|
| .. | ||
| blizzard2013 | ||
| kokoro/tacotron2-DDC | ||
| ljspeech | ||
| multilingual/vits_tts | ||
| thorsten_DE | ||
| vctk | ||
| README.md | ||
README.md
🐸💬 TTS Training Recipes
TTS recipes intended to host scripts running all the necessary steps to train a TTS model on a particular dataset.
For each dataset, you need to download the dataset once. Then you run the training for the model you want.
Run each script from the root TTS folder as follows.
$ sh ./recipes/<dataset>/download_<dataset>.sh
$ python recipes/<dataset>/<model_name>/train.py
For some datasets you might need to resample the audio files. For example, VCTK dataset can be resampled to 22050Hz as follows.
python TTS/bin/resample.py --input_dir recipes/vctk/VCTK/wav48_silence_trimmed --output_sr 22050 --output_dir recipes/vctk/VCTK/wav48_silence_trimmed --n_jobs 8 --file_ext flac
If you train a new model using TTS, feel free to share your training to expand the list of recipes.
You can also open a new discussion and share your progress with the 🐸 community.