| ## Training | |
| Before training, please install MeloTTS in dev mode and go to the `melo` folder. | |
| ``` | |
| pip install -e . | |
| cd melo | |
| ``` | |
| ### Data Preparation | |
| To train a TTS model, we need to prepare the audio files and a metadata file. We recommend using 44100Hz audio files and the metadata file should have the following format: | |
| ``` | |
| path/to/audio_001.wav |<speaker_name>|<language_code>|<text_001> | |
| path/to/audio_002.wav |<speaker_name>|<language_code>|<text_002> | |
| ``` | |
| The transcribed text can be obtained by ASR model, (e.g., [whisper](https://github.com/openai/whisper)). An example metadata can be found in `data/example/metadata.list` | |
| We can then run the preprocessing code: | |
| ``` | |
| python preprocess_text.py --metadata data/example/metadata.list | |
| ``` | |
| A config file `data/example/config.json` will be generated. Feel free to edit some hyper-parameters in that config file (for example, you may decrease the batch size if you have encountered the CUDA out-of-memory issue). | |
| ### Training | |
| The training can be launched by: | |
| ``` | |
| bash train.sh <path/to/config.json> <num_of_gpus> | |
| ``` | |
| We have found for some machine the training will sometimes crash due to an [issue](https://github.com/pytorch/pytorch/issues/2530) of gloo. Therefore, we add an auto-resume wrapper in the `train.sh`. | |
| ### Inference | |
| Simply run: | |
| ``` | |
| python infer.py --text "<some text here>" -m /path/to/checkpoint/G_<iter>.pth -o <output_dir> | |
| ``` | |