hubconf.py

dependencies = ["torch", "gdown", "pysbd", "gruut", "anyascii", "pypinyin", "coqpit", "mecab-python3", "unidic-lite"]
import torch

from TTS.utils.manage import ModelManager
from TTS.utils.synthesizer import Synthesizer


def tts(model_name="tts_models/en/ljspeech/tacotron2-DCA", vocoder_name=None, use_cuda=False):
    """TTS entry point for PyTorch Hub that provides a Synthesizer object to synthesize speech from a give text.

    Example:
        >>> synthesizer = torch.hub.load('coqui-ai/TTS', 'tts', source='github')
        >>> wavs = synthesizer.tts("This is a test! This is also a test!!")
            wavs - is a list of values of the synthesized speech.

    Args:
        model_name (str, optional): One of the model names from .model.json. Defaults to 'tts_models/en/ljspeech/tacotron2-DCA'.
        vocoder_name (str, optional): One of the model names from .model.json. Defaults to 'vocoder_models/en/ljspeech/multiband-melgan'.
        pretrained (bool, optional): [description]. Defaults to True.

    Returns:
        TTS.utils.synthesizer.Synthesizer: Synthesizer object wrapping both vocoder and tts models.
    """
    manager = ModelManager()

    model_path, config_path, model_item = manager.download_model(model_name)
    vocoder_name = model_item["default_vocoder"] if vocoder_name is None else vocoder_name
    vocoder_path, vocoder_config_path, _ = manager.download_model(vocoder_name)

    # create synthesizer
    synt = Synthesizer(
        tts_checkpoint=model_path,
        tts_config_path=config_path,
        vocoder_checkpoint=vocoder_path,
        vocoder_config=vocoder_config_path,
        use_cuda=use_cuda,
    )
    return synt


if __name__ == "__main__":
    synthesizer = torch.hub.load("coqui-ai/TTS:dev", "tts", source="github")
    synthesizer.tts("This is a test!")