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Clarify GPU Id use with vocoder training

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nmstoker 2020-07-11 17:56:49 +01:00
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@ -16,23 +16,23 @@ You can see here an example (Soon)[Colab Notebook]() training MelGAN with LJSpee
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''' 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. You need to define other relevant parameters in your ```config.json``` and then start traning with the following command from Mozilla TTS root path, where '0' is the Id of the GPU you wish to use.
```CUDA_VISIBLE_DEVICES='1' python vocoder/train.py --config_path path/to/config.json``` ```CUDA_VISIBLE_DEVICES='0' python vocoder/train.py --config_path path/to/config.json```
Exampled config files can be found under `vocoder/configs/` folder. Exampled config files can be found under `vocoder/configs/` folder.
You can continue a previous training by the following command. 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``` ```CUDA_VISIBLE_DEVICES='0' python vocoder/train.py --continue_path path/to/your/model/folder```
You can fine-tune a pre-trained model by the following command. 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``` ```CUDA_VISIBLE_DEVICES='0' 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. 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. You can also follow your training runs on Tensorboard as you do with our TTS models.
## Acknowledgement ## Acknowledgement
Thanks to @kan-bayashi for his [repository](https://github.com/kan-bayashi/ParallelWaveGAN) being the start point of our work. Thanks to @kan-bayashi for his [repository](https://github.com/kan-bayashi/ParallelWaveGAN) being the start point of our work.