History

Eren Gölge 5ee73c2bae Merge branch 'dev' of https://github.com/mozilla/TTS into dev		2021-01-22 13:26:27 +01:00
..
configs	change noise scheduling for wavegrad. Compute beta values externally to enable better flexibility	2020-11-14 13:01:10 +01:00
datasets	wavegrad_dataset update	2020-11-25 14:50:50 +01:00
layers	set requires_grad=False	2021-01-16 19:46:04 +01:00
models	linter fixes and test fixes	2021-01-22 02:32:35 +01:00
tf	time seperable convolution encoder, huber loss for duration predictor	2020-09-17 03:10:58 +02:00
utils	linter fixes and test fixes	2021-01-22 02:32:35 +01:00
README.md	rename the project to old TTS	2020-09-09 12:27:23 +02:00
__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.tar

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.