40 lines
1.7 KiB
Markdown
40 lines
1.7 KiB
Markdown
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# Mozilla TTS Vocoders (Experimental)
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Here there are vocoder model implementations which can be combined with the other TTS models.
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Currently, following models are implemented:
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- Melgan
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- MultiBand-Melgan
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- ParallelWaveGAN
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- GAN-TTS (Discriminator Only)
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It is also very easy to adapt different vocoder models as we provide a flexible and modular (but not too modular) framework.
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## Training a model
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You can see here an example (Soon)[Colab Notebook]() training MelGAN with LJSpeech dataset.
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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'''
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You need to define other relevant parameters in your ```config.json``` and then start traning with the following command.
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```CUDA_VISIBLE_DEVICES='0' python tts/bin/train_vocoder.py --config_path path/to/config.json```
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Example config files can be found under `tts/vocoder/configs/` folder.
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You can continue a previous training run by the following command.
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```CUDA_VISIBLE_DEVICES='0' python tts/bin/train_vocoder.py --continue_path path/to/your/model/folder```
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You can fine-tune a pre-trained model by the following command.
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```CUDA_VISIBLE_DEVICES='0' python tts/bin/train_vocoder.py --restore_path path/to/your/model.pth```
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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.
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You can also follow your training runs on Tensorboard as you do with our TTS models.
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## Acknowledgement
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Thanks to @kan-bayashi for his [repository](https://github.com/kan-bayashi/ParallelWaveGAN) being the start point of our work.
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