Before training, please install MeloTTS in dev mode and go to the melo folder.
pip install -e .
cd melo
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). 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).
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 of gloo. Therefore, we add an auto-resume wrapper in the train.sh.
Simply run:
python infer.py --text "<some text here>" -m /path/to/checkpoint/G_<iter>.pth -o <output_dir>