History

Stanislav Kachnov 2c9f00a808 Fix tune wavegrad (#1844 ) * fix imports in tune_wavegrad * load_config returns Coqpit object instead None * set action (store true) for flag "--use_cuda"; start to tune if module is running as the main program * fix var order in the result of batch collating * make style * make style with black and isort		2022-08-22 09:55:32 +02:00
..
configs	Fix the wrong default loss name for GAN models	2022-02-25 10:48:03 +01:00
datasets	Fix tune wavegrad (#1844 )	2022-08-22 09:55:32 +02:00
layers	Introduce numpy and torch transforms (#1705 )	2022-08-08 11:57:50 +02:00
models	Introduce numpy and torch transforms (#1705 )	2022-08-08 11:57:50 +02:00
utils	Implement VITS model 🚀	2021-08-09 18:02:36 +00:00
README.md	Update model file extension (#1422 )	2022-03-22 17:55:00 +01:00
__init__.py	rename the project to old TTS	2020-09-09 12:27:23 +02:00
pqmf_output.wav	rename the project to old TTS	2020-09-09 12:27:23 +02:00

README.md

Mozilla TTS Vocoders (Experimental)

Here there are vocoder model implementations which can be combined with the other TTS models.

Currently, following models are implemented:

Melgan
MultiBand-Melgan
ParallelWaveGAN
GAN-TTS (Discriminator Only)

It is also very easy to adapt different vocoder models as we provide a flexible and modular (but not too modular) framework.

Training a model

You can see here an example (Soon)Colab Notebook training MelGAN with LJSpeech dataset.

In order to train a new model, you need to gather all wav files into a folder and give this folder to data_path in '''config.json'''

You need to define other relevant parameters in your config.json and then start traning with the following command.

CUDA_VISIBLE_DEVICES='0' python tts/bin/train_vocoder.py --config_path path/to/config.json

Example config files can be found under tts/vocoder/configs/ folder.

You can continue a previous training run by the following command.

CUDA_VISIBLE_DEVICES='0' python tts/bin/train_vocoder.py --continue_path path/to/your/model/folder

You can fine-tune a pre-trained model by the following command.

CUDA_VISIBLE_DEVICES='0' python tts/bin/train_vocoder.py --restore_path path/to/your/model.pth

Restoring a model starts a new training in a different folder. It only restores model weights with the given checkpoint file. However, continuing a training starts from the same directory where the previous training run left off.

You can also follow your training runs on Tensorboard as you do with our TTS models.

Acknowledgement

Thanks to @kan-bayashi for his repository being the start point of our work.