from pathlib import Path
from TTS.utils.manage import ModelManager
from TTS.utils.synthesizer import Synthesizer
class TTS:
"""TODO: Add voice conversion and Capacitron support."""
def __init__(
self,
model_name: str = None,
model_path: str = None,
config_path: str = None,
vocoder_path: str = None,
vocoder_config_path: str = None,
progress_bar: bool = True,
gpu=False,
):
"""🐸TTS python interface that allows to load and use the released models.
Example with a multi-speaker model:
>>> from TTS.api import TTS
>>> tts = TTS(TTS.list_models()[0])
>>> wav = tts.tts("This is a test! This is also a test!!", speaker=tts.speakers[0], language=tts.languages[0])
>>> tts.tts_to_file(text="Hello world!", speaker=tts.speakers[0], language=tts.languages[0], file_path="output.wav")
Example with a single-speaker model:
>>> tts = TTS(model_name="tts_models/de/thorsten/tacotron2-DDC", progress_bar=False, gpu=False)
>>> tts.tts_to_file(text="Ich bin eine Testnachricht.", file_path="output.wav")
Example loading a model from a path:
>>> tts = TTS(model_path="/path/to/checkpoint_100000.pth", config_path="/path/to/config.json", progress_bar=False, gpu=False)
>>> tts.tts_to_file(text="Ich bin eine Testnachricht.", file_path="output.wav")
Example voice cloning with YourTTS in English, French and Portuguese:
>>> tts = TTS(model_name="tts_models/multilingual/multi-dataset/your_tts", progress_bar=False, gpu=True)
>>> tts.tts_to_file("This is voice cloning.", speaker_wav="my/cloning/audio.wav", language="en", file_path="thisisit.wav")
>>> tts.tts_to_file("C'est le clonage de la voix.", speaker_wav="my/cloning/audio.wav", language="fr", file_path="thisisit.wav")
>>> tts.tts_to_file("Isso é clonagem de voz.", speaker_wav="my/cloning/audio.wav", language="pt", file_path="thisisit.wav")
Args:
model_name (str, optional): Model name to load. You can list models by ```tts.models```. Defaults to None.
model_path (str, optional): Path to the model checkpoint. Defaults to None.
config_path (str, optional): Path to the model config. Defaults to None.
vocoder_path (str, optional): Path to the vocoder checkpoint. Defaults to None.
vocoder_config_path (str, optional): Path to the vocoder config. Defaults to None.
progress_bar (bool, optional): Whether to pring a progress bar while downloading a model. Defaults to True.
gpu (bool, optional): Enable/disable GPU. Some models might be too slow on CPU. Defaults to False.
"""
self.manager = ModelManager(models_file=self.get_models_file_path(), progress_bar=progress_bar, verbose=False)
self.synthesizer = None
if model_name:
self.load_model_by_name(model_name, gpu)
if model_path:
self.load_model_by_path(
model_path, config_path, vocoder_path=vocoder_path, vocoder_config=vocoder_config_path, gpu=gpu
)
@property
def models(self):
return self.manager.list_tts_models()
@property
def is_multi_speaker(self):
if hasattr(self.synthesizer.tts_model, "speaker_manager") and self.synthesizer.tts_model.speaker_manager:
return self.synthesizer.tts_model.speaker_manager.num_speakers > 1
return False
@property
def is_multi_lingual(self):
if hasattr(self.synthesizer.tts_model, "language_manager") and self.synthesizer.tts_model.language_manager:
return self.synthesizer.tts_model.language_manager.num_languages > 1
return False
@property
def speakers(self):
if not self.is_multi_speaker:
return None
return self.synthesizer.tts_model.speaker_manager.speaker_names
@property
def languages(self):
if not self.is_multi_lingual:
return None
return self.synthesizer.tts_model.language_manager.language_names
@staticmethod
def get_models_file_path():
return Path(__file__).parent / ".models.json"
@staticmethod
def list_models():
manager = ModelManager(models_file=TTS.get_models_file_path(), progress_bar=False, verbose=False)
return manager.list_tts_models()
def download_model_by_name(self, model_name: str):
model_path, config_path, model_item = self.manager.download_model(model_name)
if model_item["default_vocoder"] is None:
return model_path, config_path, None, None
vocoder_path, vocoder_config_path, _ = self.manager.download_model(model_item["default_vocoder"])
return model_path, config_path, vocoder_path, vocoder_config_path
def load_model_by_name(self, model_name: str, gpu: bool = False):
"""Load one of 🐸TTS models by name.
Args:
model_name (str): Model name to load. You can list models by ```tts.models```.
gpu (bool, optional): Enable/disable GPU. Some models might be too slow on CPU. Defaults to False.
TODO: Add tests
"""
model_path, config_path, vocoder_path, vocoder_config_path = self.download_model_by_name(model_name)
# init synthesizer
# None values are fetch from the model
self.synthesizer = Synthesizer(
tts_checkpoint=model_path,
tts_config_path=config_path,
tts_speakers_file=None,
tts_languages_file=None,
vocoder_checkpoint=vocoder_path,
vocoder_config=vocoder_config_path,
encoder_checkpoint=None,
encoder_config=None,
use_cuda=gpu,
)
def load_model_by_path(
self, model_path: str, config_path: str, vocoder_path: str = None, vocoder_config: str = None, gpu: bool = False
):
"""Load a model from a path.
Args:
model_path (str): Path to the model checkpoint.
config_path (str): Path to the model config.
vocoder_path (str, optional): Path to the vocoder checkpoint. Defaults to None.
vocoder_config (str, optional): Path to the vocoder config. Defaults to None.
gpu (bool, optional): Enable/disable GPU. Some models might be too slow on CPU. Defaults to False.
"""
self.synthesizer = Synthesizer(
tts_checkpoint=model_path,
tts_config_path=config_path,
tts_speakers_file=None,
tts_languages_file=None,
vocoder_checkpoint=vocoder_path,
vocoder_config=vocoder_config,
encoder_checkpoint=None,
encoder_config=None,
use_cuda=gpu,
)
def _check_arguments(self, speaker: str = None, language: str = None, speaker_wav: str = None):
if self.is_multi_speaker and (speaker is None and speaker_wav is None):
raise ValueError("Model is multi-speaker but no speaker is provided.")
if self.is_multi_lingual and language is None:
raise ValueError("Model is multi-lingual but no language is provided.")
if not self.is_multi_speaker and speaker is not None:
raise ValueError("Model is not multi-speaker but speaker is provided.")
if not self.is_multi_lingual and language is not None:
raise ValueError("Model is not multi-lingual but language is provided.")
def tts(self, text: str, speaker: str = None, language: str = None, speaker_wav: str = None):
"""Convert text to speech.
Args:
text (str):
Input text to synthesize.
speaker (str, optional):
Speaker name for multi-speaker. You can check whether loaded model is multi-speaker by
`tts.is_multi_speaker` and list speakers by `tts.speakers`. Defaults to None.
language (str, optional):
Language code for multi-lingual models. You can check whether loaded model is multi-lingual
`tts.is_multi_lingual` and list available languages by `tts.languages`. Defaults to None.
speaker_wav (str, optional):
Path to a reference wav file to use for voice cloning with supporting models like YourTTS.
Defaults to None.
"""
self._check_arguments(speaker=speaker, language=language, speaker_wav=speaker_wav)
wav = self.synthesizer.tts(
text=text,
speaker_name=speaker,
language_name=language,
speaker_wav=speaker_wav,
reference_wav=None,
style_wav=None,
style_text=None,
reference_speaker_name=None,
)
return wav
def tts_to_file(
self,
text: str,
speaker: str = None,
language: str = None,
speaker_wav: str = None,
file_path: str = "output.wav",
):
"""Convert text to speech.
Args:
text (str):
Input text to synthesize.
speaker (str, optional):
Speaker name for multi-speaker. You can check whether loaded model is multi-speaker by
`tts.is_multi_speaker` and list speakers by `tts.speakers`. Defaults to None.
language (str, optional):
Language code for multi-lingual models. You can check whether loaded model is multi-lingual
`tts.is_multi_lingual` and list available languages by `tts.languages`. Defaults to None.
speaker_wav (str, optional):
Path to a reference wav file to use for voice cloning with supporting models like YourTTS.
Defaults to None.
file_path (str, optional):
Output file path. Defaults to "output.wav".
"""
wav = self.tts(text=text, speaker=speaker, language=language, speaker_wav=speaker_wav)
self.synthesizer.save_wav(wav=wav, path=file_path)