import os import time from typing import List import numpy as np import pysbd import torch from torch import nn from TTS.config import load_config from TTS.tts.configs.vits_config import VitsConfig from TTS.tts.models import setup_model as setup_tts_model from TTS.tts.models.vits import Vits # pylint: disable=unused-wildcard-import # pylint: disable=wildcard-import from TTS.tts.utils.synthesis import synthesis, transfer_voice, trim_silence from TTS.utils.audio import AudioProcessor from TTS.utils.audio.numpy_transforms import save_wav from TTS.vc.models import setup_model as setup_vc_model from TTS.vocoder.models import setup_model as setup_vocoder_model from TTS.vocoder.utils.generic_utils import interpolate_vocoder_input class Synthesizer(nn.Module): def __init__( self, tts_checkpoint: str = "", tts_config_path: str = "", tts_speakers_file: str = "", tts_languages_file: str = "", vocoder_checkpoint: str = "", vocoder_config: str = "", encoder_checkpoint: str = "", encoder_config: str = "", vc_checkpoint: str = "", vc_config: str = "", model_dir: str = "", voice_dir: str = None, use_cuda: bool = False, ) -> None: """General 🐸 TTS interface for inference. It takes a tts and a vocoder model and synthesize speech from the provided text. The text is divided into a list of sentences using `pysbd` and synthesize speech on each sentence separately. If you have certain special characters in your text, you need to handle them before providing the text to Synthesizer. TODO: set the segmenter based on the source language Args: tts_checkpoint (str, optional): path to the tts model file. tts_config_path (str, optional): path to the tts config file. vocoder_checkpoint (str, optional): path to the vocoder model file. Defaults to None. vocoder_config (str, optional): path to the vocoder config file. Defaults to None. encoder_checkpoint (str, optional): path to the speaker encoder model file. Defaults to `""`, encoder_config (str, optional): path to the speaker encoder config file. Defaults to `""`, vc_checkpoint (str, optional): path to the voice conversion model file. Defaults to `""`, vc_config (str, optional): path to the voice conversion config file. Defaults to `""`, use_cuda (bool, optional): enable/disable cuda. Defaults to False. """ super().__init__() self.tts_checkpoint = tts_checkpoint self.tts_config_path = tts_config_path self.tts_speakers_file = tts_speakers_file self.tts_languages_file = tts_languages_file self.vocoder_checkpoint = vocoder_checkpoint self.vocoder_config = vocoder_config self.encoder_checkpoint = encoder_checkpoint self.encoder_config = encoder_config self.vc_checkpoint = vc_checkpoint self.vc_config = vc_config self.use_cuda = use_cuda self.tts_model = None self.vocoder_model = None self.vc_model = None self.speaker_manager = None self.tts_speakers = {} self.language_manager = None self.num_languages = 0 self.tts_languages = {} self.d_vector_dim = 0 self.seg = self._get_segmenter("en") self.use_cuda = use_cuda self.voice_dir = voice_dir if self.use_cuda: assert torch.cuda.is_available(), "CUDA is not availabe on this machine." if tts_checkpoint: self._load_tts(tts_checkpoint, tts_config_path, use_cuda) self.output_sample_rate = self.tts_config.audio["sample_rate"] if vocoder_checkpoint: self._load_vocoder(vocoder_checkpoint, vocoder_config, use_cuda) self.output_sample_rate = self.vocoder_config.audio["sample_rate"] if vc_checkpoint: self._load_vc(vc_checkpoint, vc_config, use_cuda) self.output_sample_rate = self.vc_config.audio["output_sample_rate"] if model_dir: if "fairseq" in model_dir: self._load_fairseq_from_dir(model_dir, use_cuda) self.output_sample_rate = self.tts_config.audio["sample_rate"] else: self._load_tts_from_dir(model_dir, use_cuda) self.output_sample_rate = self.tts_config.audio["output_sample_rate"] @staticmethod def _get_segmenter(lang: str): """get the sentence segmenter for the given language. Args: lang (str): target language code. Returns: [type]: [description] """ return pysbd.Segmenter(language=lang, clean=True) def _load_vc(self, vc_checkpoint: str, vc_config_path: str, use_cuda: bool) -> None: """Load the voice conversion model. 1. Load the model config. 2. Init the model from the config. 3. Load the model weights. 4. Move the model to the GPU if CUDA is enabled. Args: vc_checkpoint (str): path to the model checkpoint. tts_config_path (str): path to the model config file. use_cuda (bool): enable/disable CUDA use. """ # pylint: disable=global-statement self.vc_config = load_config(vc_config_path) self.vc_model = setup_vc_model(config=self.vc_config) self.vc_model.load_checkpoint(self.vc_config, vc_checkpoint) if use_cuda: self.vc_model.cuda() def _load_fairseq_from_dir(self, model_dir: str, use_cuda: bool) -> None: """Load the fairseq model from a directory. We assume it is VITS and the model knows how to load itself from the directory and there is a config.json file in the directory. """ self.tts_config = VitsConfig() self.tts_model = Vits.init_from_config(self.tts_config) self.tts_model.load_fairseq_checkpoint(self.tts_config, checkpoint_dir=model_dir, eval=True) self.tts_config = self.tts_model.config if use_cuda: self.tts_model.cuda() def _load_tts_from_dir(self, model_dir: str, use_cuda: bool) -> None: """Load the TTS model from a directory. We assume the model knows how to load itself from the directory and there is a config.json file in the directory. """ config = load_config(os.path.join(model_dir, "config.json")) self.tts_config = config self.tts_model = setup_tts_model(config) self.tts_model.load_checkpoint(config, checkpoint_dir=model_dir, eval=True) if use_cuda: self.tts_model.cuda() def _load_tts(self, tts_checkpoint: str, tts_config_path: str, use_cuda: bool) -> None: """Load the TTS model. 1. Load the model config. 2. Init the model from the config. 3. Load the model weights. 4. Move the model to the GPU if CUDA is enabled. 5. Init the speaker manager in the model. Args: tts_checkpoint (str): path to the model checkpoint. tts_config_path (str): path to the model config file. use_cuda (bool): enable/disable CUDA use. """ # pylint: disable=global-statement self.tts_config = load_config(tts_config_path) if self.tts_config["use_phonemes"] and self.tts_config["phonemizer"] is None: raise ValueError("Phonemizer is not defined in the TTS config.") self.tts_model = setup_tts_model(config=self.tts_config) if not self.encoder_checkpoint: self._set_speaker_encoder_paths_from_tts_config() self.tts_model.load_checkpoint(self.tts_config, tts_checkpoint, eval=True) if use_cuda: self.tts_model.cuda() if self.encoder_checkpoint and hasattr(self.tts_model, "speaker_manager"): self.tts_model.speaker_manager.init_encoder(self.encoder_checkpoint, self.encoder_config, use_cuda) def _set_speaker_encoder_paths_from_tts_config(self): """Set the encoder paths from the tts model config for models with speaker encoders.""" if hasattr(self.tts_config, "model_args") and hasattr( self.tts_config.model_args, "speaker_encoder_config_path" ): self.encoder_checkpoint = self.tts_config.model_args.speaker_encoder_model_path self.encoder_config = self.tts_config.model_args.speaker_encoder_config_path def _load_vocoder(self, model_file: str, model_config: str, use_cuda: bool) -> None: """Load the vocoder model. 1. Load the vocoder config. 2. Init the AudioProcessor for the vocoder. 3. Init the vocoder model from the config. 4. Move the model to the GPU if CUDA is enabled. Args: model_file (str): path to the model checkpoint. model_config (str): path to the model config file. use_cuda (bool): enable/disable CUDA use. """ self.vocoder_config = load_config(model_config) self.vocoder_ap = AudioProcessor(verbose=False, **self.vocoder_config.audio) self.vocoder_model = setup_vocoder_model(self.vocoder_config) self.vocoder_model.load_checkpoint(self.vocoder_config, model_file, eval=True) if use_cuda: self.vocoder_model.cuda() def split_into_sentences(self, text) -> List[str]: """Split give text into sentences. Args: text (str): input text in string format. Returns: List[str]: list of sentences. """ return self.seg.segment(text) def save_wav(self, wav: List[int], path: str, pipe_out=None) -> None: """Save the waveform as a file. Args: wav (List[int]): waveform as a list of values. path (str): output path to save the waveform. pipe_out (BytesIO, optional): Flag to stdout the generated TTS wav file for shell pipe. """ # if tensor convert to numpy if torch.is_tensor(wav): wav = wav.cpu().numpy() if isinstance(wav, list): wav = np.array(wav) save_wav(wav=wav, path=path, sample_rate=self.output_sample_rate, pipe_out=pipe_out) def voice_conversion(self, source_wav: str, target_wav: str) -> List[int]: output_wav = self.vc_model.voice_conversion(source_wav, target_wav) return output_wav def tts( self, text: str = "", speaker_name: str = "", language_name: str = "", speaker_wav=None, style_wav=None, style_text=None, reference_wav=None, reference_speaker_name=None, split_sentences: bool = True, **kwargs, ) -> List[int]: """🐸 TTS magic. Run all the models and generate speech. Args: text (str): input text. speaker_name (str, optional): speaker id for multi-speaker models. Defaults to "". language_name (str, optional): language id for multi-language models. Defaults to "". speaker_wav (Union[str, List[str]], optional): path to the speaker wav for voice cloning. Defaults to None. style_wav ([type], optional): style waveform for GST. Defaults to None. style_text ([type], optional): transcription of style_wav for Capacitron. Defaults to None. reference_wav ([type], optional): reference waveform for voice conversion. Defaults to None. reference_speaker_name ([type], optional): speaker id of reference waveform. Defaults to None. split_sentences (bool, optional): split the input text into sentences. Defaults to True. **kwargs: additional arguments to pass to the TTS model. Returns: List[int]: [description] """ start_time = time.time() wavs = [] if not text and not reference_wav: raise ValueError( "You need to define either `text` (for sythesis) or a `reference_wav` (for voice conversion) to use the Coqui TTS API." ) if text: sens = [text] if split_sentences: print(" > Text splitted to sentences.") sens = self.split_into_sentences(text) print(sens) # handle multi-speaker if "voice_dir" in kwargs: self.voice_dir = kwargs["voice_dir"] kwargs.pop("voice_dir") speaker_embedding = None speaker_id = None if self.tts_speakers_file or hasattr(self.tts_model.speaker_manager, "name_to_id"): if speaker_name and isinstance(speaker_name, str) and not self.tts_config.model == "xtts": if self.tts_config.use_d_vector_file: # get the average speaker embedding from the saved d_vectors. speaker_embedding = self.tts_model.speaker_manager.get_mean_embedding( speaker_name, num_samples=None, randomize=False ) speaker_embedding = np.array(speaker_embedding)[None, :] # [1 x embedding_dim] else: # get speaker idx from the speaker name speaker_id = self.tts_model.speaker_manager.name_to_id[speaker_name] # handle Neon models with single speaker. elif len(self.tts_model.speaker_manager.name_to_id) == 1: speaker_id = list(self.tts_model.speaker_manager.name_to_id.values())[0] elif not speaker_name and not speaker_wav: raise ValueError( " [!] Looks like you are using a multi-speaker model. " "You need to define either a `speaker_idx` or a `speaker_wav` to use a multi-speaker model." ) else: speaker_embedding = None else: if speaker_name and self.voice_dir is None: raise ValueError( f" [!] Missing speakers.json file path for selecting speaker {speaker_name}." "Define path for speaker.json if it is a multi-speaker model or remove defined speaker idx. " ) # handle multi-lingual language_id = None if self.tts_languages_file or ( hasattr(self.tts_model, "language_manager") and self.tts_model.language_manager is not None and not self.tts_config.model == "xtts" ): if len(self.tts_model.language_manager.name_to_id) == 1: language_id = list(self.tts_model.language_manager.name_to_id.values())[0] elif language_name and isinstance(language_name, str): try: language_id = self.tts_model.language_manager.name_to_id[language_name] except KeyError as e: raise ValueError( f" [!] Looks like you use a multi-lingual model. " f"Language {language_name} is not in the available languages: " f"{self.tts_model.language_manager.name_to_id.keys()}." ) from e elif not language_name: raise ValueError( " [!] Look like you use a multi-lingual model. " "You need to define either a `language_name` or a `style_wav` to use a multi-lingual model." ) else: raise ValueError( f" [!] Missing language_ids.json file path for selecting language {language_name}." "Define path for language_ids.json if it is a multi-lingual model or remove defined language idx. " ) # compute a new d_vector from the given clip. if ( speaker_wav is not None and self.tts_model.speaker_manager is not None and hasattr(self.tts_model.speaker_manager, "encoder_ap") and self.tts_model.speaker_manager.encoder_ap is not None ): speaker_embedding = self.tts_model.speaker_manager.compute_embedding_from_clip(speaker_wav) vocoder_device = "cpu" use_gl = self.vocoder_model is None if not use_gl: vocoder_device = next(self.vocoder_model.parameters()).device if self.use_cuda: vocoder_device = "cuda" if not reference_wav: # not voice conversion for sen in sens: if hasattr(self.tts_model, "synthesize"): outputs = self.tts_model.synthesize( text=sen, config=self.tts_config, speaker_id=speaker_name, voice_dirs=self.voice_dir, d_vector=speaker_embedding, speaker_wav=speaker_wav, language=language_name, **kwargs, ) else: # synthesize voice outputs = synthesis( model=self.tts_model, text=sen, CONFIG=self.tts_config, use_cuda=self.use_cuda, speaker_id=speaker_id, style_wav=style_wav, style_text=style_text, use_griffin_lim=use_gl, d_vector=speaker_embedding, language_id=language_id, ) waveform = outputs["wav"] if not use_gl: mel_postnet_spec = outputs["outputs"]["model_outputs"][0].detach().cpu().numpy() # denormalize tts output based on tts audio config mel_postnet_spec = self.tts_model.ap.denormalize(mel_postnet_spec.T).T # renormalize spectrogram based on vocoder config vocoder_input = self.vocoder_ap.normalize(mel_postnet_spec.T) # compute scale factor for possible sample rate mismatch scale_factor = [ 1, self.vocoder_config["audio"]["sample_rate"] / self.tts_model.ap.sample_rate, ] if scale_factor[1] != 1: print(" > interpolating tts model output.") vocoder_input = interpolate_vocoder_input(scale_factor, vocoder_input) else: vocoder_input = torch.tensor(vocoder_input).unsqueeze(0) # pylint: disable=not-callable # run vocoder model # [1, T, C] waveform = self.vocoder_model.inference(vocoder_input.to(vocoder_device)) if torch.is_tensor(waveform) and waveform.device != torch.device("cpu") and not use_gl: waveform = waveform.cpu() if not use_gl: waveform = waveform.numpy() waveform = waveform.squeeze() # trim silence if "do_trim_silence" in self.tts_config.audio and self.tts_config.audio["do_trim_silence"]: waveform = trim_silence(waveform, self.tts_model.ap) wavs += list(waveform) wavs += [0] * 10000 else: # get the speaker embedding or speaker id for the reference wav file reference_speaker_embedding = None reference_speaker_id = None if self.tts_speakers_file or hasattr(self.tts_model.speaker_manager, "name_to_id"): if reference_speaker_name and isinstance(reference_speaker_name, str): if self.tts_config.use_d_vector_file: # get the speaker embedding from the saved d_vectors. reference_speaker_embedding = self.tts_model.speaker_manager.get_embeddings_by_name( reference_speaker_name )[0] reference_speaker_embedding = np.array(reference_speaker_embedding)[ None, : ] # [1 x embedding_dim] else: # get speaker idx from the speaker name reference_speaker_id = self.tts_model.speaker_manager.name_to_id[reference_speaker_name] else: reference_speaker_embedding = self.tts_model.speaker_manager.compute_embedding_from_clip( reference_wav ) outputs = transfer_voice( model=self.tts_model, CONFIG=self.tts_config, use_cuda=self.use_cuda, reference_wav=reference_wav, speaker_id=speaker_id, d_vector=speaker_embedding, use_griffin_lim=use_gl, reference_speaker_id=reference_speaker_id, reference_d_vector=reference_speaker_embedding, ) waveform = outputs if not use_gl: mel_postnet_spec = outputs[0].detach().cpu().numpy() # denormalize tts output based on tts audio config mel_postnet_spec = self.tts_model.ap.denormalize(mel_postnet_spec.T).T # renormalize spectrogram based on vocoder config vocoder_input = self.vocoder_ap.normalize(mel_postnet_spec.T) # compute scale factor for possible sample rate mismatch scale_factor = [ 1, self.vocoder_config["audio"]["sample_rate"] / self.tts_model.ap.sample_rate, ] if scale_factor[1] != 1: print(" > interpolating tts model output.") vocoder_input = interpolate_vocoder_input(scale_factor, vocoder_input) else: vocoder_input = torch.tensor(vocoder_input).unsqueeze(0) # pylint: disable=not-callable # run vocoder model # [1, T, C] waveform = self.vocoder_model.inference(vocoder_input.to(vocoder_device)) if torch.is_tensor(waveform) and waveform.device != torch.device("cpu"): waveform = waveform.cpu() if not use_gl: waveform = waveform.numpy() wavs = waveform.squeeze() # compute stats process_time = time.time() - start_time audio_time = len(wavs) / self.tts_config.audio["sample_rate"] print(f" > Processing time: {process_time}") print(f" > Real-time factor: {process_time / audio_time}") return wavs