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Mozilla TTS Vocoders (Experimental)
We provide here different vocoder implementations which can be combined with our TTS models to enable "FASTER THAN REAL-TIME" end-to-end TTS stack.
Currently, there are implementations of the following models.
- Melgan
- MultiBand-Melgan
- GAN-TTS (Discriminator Only)
It is also very easy to adapt different vocoder models as we provide here 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 collecto all your wav files under a common parent folder and give this path to data_path field in '''config.json'''
You need to define other relevant parameters in your config.json and then start traning with the following command from Mozilla TTS root path.
CUDA_VISIBLE_DEVICES='1' python vocoder/train.py --config_path path/to/config.json
Exampled config files can be found under vocoder/configs/ folder.
You can continue a previous training by the following command.
CUDA_VISIBLE_DEVICES='1' python vocoder/train.py --continue_path path/to/your/model/folder
You can fine-tune a pre-trained model by the following command.
CUDA_VISIBLE_DEVICES='1' python vocoder/train.py --restore_path path/to/your/model.pth.tar
Restoring a model starts a new training in a different output folder. It only restores model weights with the given checkpoint file. However, continuing a training starts from the same conditions the previous training run left off.
You can also follow your training runs on Tensorboard as you do with our TTS models.