mirror of https://github.com/coqui-ai/TTS.git
42afad5e79
* Fixed bug related to yourtts speaker embeddings issue * Reverted code for base_tts * Bug fix on VITS d_vector_file type * Ignore the test speakers on YourTTS recipe * Add speaker encoder model and config on YourTTS recipe to easily do zero-shot inference * Update YourTTS config file * Update ModelManager._update_path to deal with list attributes * Fix lint checks * Remove unused code * Fix unit tests * Reset name_to_id to get the right speaker ids on load_embeddings_from_list_of_files * Set weighted_sampler_multipliers as an empty dict to prevent users' mistakes Co-authored-by: Edresson Casanova <edresson1@gmail.com> |
||
|---|---|---|
| .. | ||
| 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.