| # 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](https://github.com/kan-bayashi/ParallelWaveGAN) being the start point of our work. | |