mirror of https://github.com/coqui-ai/TTS.git
38 lines
1.8 KiB
Markdown
38 lines
1.8 KiB
Markdown
# Mozilla TTS Vocoders (Experimental)
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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.
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Currently, there are implementations of the following models.
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- Melgan
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- MultiBand-Melgan
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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 here 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 collecto all your wav files under a common parent folder and give this path to `data_path` field 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 from Mozilla TTS root path.
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```CUDA_VISIBLE_DEVICES='1' python vocoder/train.py --config_path path/to/config.json```
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Exampled config files can be found under `vocoder/configs/` folder.
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You can continue a previous training by the following command.
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```CUDA_VISIBLE_DEVICES='1' python vocoder/train.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='1' python vocoder/train.py --restore_path path/to/your/model.pth.tar```
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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.
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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. |