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| from typing import Dict | |
| import numpy as np | |
| import torch | |
| from torch import nn | |
| def numpy_to_torch(np_array, dtype, cuda=False, device="cpu"): | |
| if cuda: | |
| device = "cuda" | |
| if np_array is None: | |
| return None | |
| tensor = torch.as_tensor(np_array, dtype=dtype, device=device) | |
| return tensor | |
| def compute_style_mel(style_wav, ap, cuda=False, device="cpu"): | |
| if cuda: | |
| device = "cuda" | |
| style_mel = torch.FloatTensor( | |
| ap.melspectrogram(ap.load_wav(style_wav, sr=ap.sample_rate)), | |
| device=device, | |
| ).unsqueeze(0) | |
| return style_mel | |
| def run_model_torch( | |
| model: nn.Module, | |
| inputs: torch.Tensor, | |
| speaker_id: int = None, | |
| style_mel: torch.Tensor = None, | |
| style_text: str = None, | |
| d_vector: torch.Tensor = None, | |
| language_id: torch.Tensor = None, | |
| ) -> Dict: | |
| """Run a torch model for inference. It does not support batch inference. | |
| Args: | |
| model (nn.Module): The model to run inference. | |
| inputs (torch.Tensor): Input tensor with character ids. | |
| speaker_id (int, optional): Input speaker ids for multi-speaker models. Defaults to None. | |
| style_mel (torch.Tensor, optional): Spectrograms used for voice styling . Defaults to None. | |
| d_vector (torch.Tensor, optional): d-vector for multi-speaker models . Defaults to None. | |
| Returns: | |
| Dict: model outputs. | |
| """ | |
| input_lengths = torch.tensor(inputs.shape[1:2]).to(inputs.device) | |
| if hasattr(model, "module"): | |
| _func = model.module.inference | |
| else: | |
| _func = model.inference | |
| outputs = _func( | |
| inputs, | |
| aux_input={ | |
| "x_lengths": input_lengths, | |
| "speaker_ids": speaker_id, | |
| "d_vectors": d_vector, | |
| "style_mel": style_mel, | |
| "style_text": style_text, | |
| "language_ids": language_id, | |
| }, | |
| ) | |
| return outputs | |
| def trim_silence(wav, ap): | |
| return wav[: ap.find_endpoint(wav)] | |
| def inv_spectrogram(postnet_output, ap, CONFIG): | |
| if CONFIG.model.lower() in ["tacotron"]: | |
| wav = ap.inv_spectrogram(postnet_output.T) | |
| else: | |
| wav = ap.inv_melspectrogram(postnet_output.T) | |
| return wav | |
| def id_to_torch(aux_id, cuda=False, device="cpu"): | |
| if cuda: | |
| device = "cuda" | |
| if aux_id is not None: | |
| aux_id = np.asarray(aux_id) | |
| aux_id = torch.from_numpy(aux_id).to(device) | |
| return aux_id | |
| def embedding_to_torch(d_vector, cuda=False, device="cpu"): | |
| if cuda: | |
| device = "cuda" | |
| if d_vector is not None: | |
| d_vector = np.asarray(d_vector) | |
| d_vector = torch.from_numpy(d_vector).type(torch.FloatTensor) | |
| d_vector = d_vector.squeeze().unsqueeze(0).to(device) | |
| return d_vector | |
| # TODO: perform GL with pytorch for batching | |
| def apply_griffin_lim(inputs, input_lens, CONFIG, ap): | |
| """Apply griffin-lim to each sample iterating throught the first dimension. | |
| Args: | |
| inputs (Tensor or np.Array): Features to be converted by GL. First dimension is the batch size. | |
| input_lens (Tensor or np.Array): 1D array of sample lengths. | |
| CONFIG (Dict): TTS config. | |
| ap (AudioProcessor): TTS audio processor. | |
| """ | |
| wavs = [] | |
| for idx, spec in enumerate(inputs): | |
| wav_len = (input_lens[idx] * ap.hop_length) - ap.hop_length # inverse librosa padding | |
| wav = inv_spectrogram(spec, ap, CONFIG) | |
| # assert len(wav) == wav_len, f" [!] wav lenght: {len(wav)} vs expected: {wav_len}" | |
| wavs.append(wav[:wav_len]) | |
| return wavs | |
| def synthesis( | |
| model, | |
| text, | |
| CONFIG, | |
| use_cuda, | |
| speaker_id=None, | |
| style_wav=None, | |
| style_text=None, | |
| use_griffin_lim=False, | |
| do_trim_silence=False, | |
| d_vector=None, | |
| language_id=None, | |
| ): | |
| """Synthesize voice for the given text using Griffin-Lim vocoder or just compute output features to be passed to | |
| the vocoder model. | |
| Args: | |
| model (TTS.tts.models): | |
| The TTS model to synthesize audio with. | |
| text (str): | |
| The input text to convert to speech. | |
| CONFIG (Coqpit): | |
| Model configuration. | |
| use_cuda (bool): | |
| Enable/disable CUDA. | |
| speaker_id (int): | |
| Speaker ID passed to the speaker embedding layer in multi-speaker model. Defaults to None. | |
| style_wav (str | Dict[str, float]): | |
| Path or tensor to/of a waveform used for computing the style embedding based on GST or Capacitron. | |
| Defaults to None, meaning that Capacitron models will sample from the prior distribution to | |
| generate random but realistic prosody. | |
| style_text (str): | |
| Transcription of style_wav for Capacitron models. Defaults to None. | |
| enable_eos_bos_chars (bool): | |
| enable special chars for end of sentence and start of sentence. Defaults to False. | |
| do_trim_silence (bool): | |
| trim silence after synthesis. Defaults to False. | |
| d_vector (torch.Tensor): | |
| d-vector for multi-speaker models in share :math:`[1, D]`. Defaults to None. | |
| language_id (int): | |
| Language ID passed to the language embedding layer in multi-langual model. Defaults to None. | |
| """ | |
| # device | |
| device = next(model.parameters()).device | |
| if use_cuda: | |
| device = "cuda" | |
| # GST or Capacitron processing | |
| # TODO: need to handle the case of setting both gst and capacitron to true somewhere | |
| style_mel = None | |
| if CONFIG.has("gst") and CONFIG.gst and style_wav is not None: | |
| if isinstance(style_wav, dict): | |
| style_mel = style_wav | |
| else: | |
| style_mel = compute_style_mel(style_wav, model.ap, device=device) | |
| if CONFIG.has("capacitron_vae") and CONFIG.use_capacitron_vae and style_wav is not None: | |
| style_mel = compute_style_mel(style_wav, model.ap, device=device) | |
| style_mel = style_mel.transpose(1, 2) # [1, time, depth] | |
| language_name = None | |
| if language_id is not None: | |
| language = [k for k, v in model.language_manager.name_to_id.items() if v == language_id] | |
| assert len(language) == 1, "language_id must be a valid language" | |
| language_name = language[0] | |
| # convert text to sequence of token IDs | |
| text_inputs = np.asarray( | |
| model.tokenizer.text_to_ids(text, language=language_name), | |
| dtype=np.int32, | |
| ) | |
| # pass tensors to backend | |
| if speaker_id is not None: | |
| speaker_id = id_to_torch(speaker_id, device=device) | |
| if d_vector is not None: | |
| d_vector = embedding_to_torch(d_vector, device=device) | |
| if language_id is not None: | |
| language_id = id_to_torch(language_id, device=device) | |
| if not isinstance(style_mel, dict): | |
| # GST or Capacitron style mel | |
| style_mel = numpy_to_torch(style_mel, torch.float, device=device) | |
| if style_text is not None: | |
| style_text = np.asarray( | |
| model.tokenizer.text_to_ids(style_text, language=language_id), | |
| dtype=np.int32, | |
| ) | |
| style_text = numpy_to_torch(style_text, torch.long, device=device) | |
| style_text = style_text.unsqueeze(0) | |
| text_inputs = numpy_to_torch(text_inputs, torch.long, device=device) | |
| text_inputs = text_inputs.unsqueeze(0) | |
| # synthesize voice | |
| outputs = run_model_torch( | |
| model, | |
| text_inputs, | |
| speaker_id, | |
| style_mel, | |
| style_text, | |
| d_vector=d_vector, | |
| language_id=language_id, | |
| ) | |
| model_outputs = outputs["model_outputs"] | |
| model_outputs = model_outputs[0].data.cpu().numpy() | |
| alignments = outputs["alignments"] | |
| # convert outputs to numpy | |
| # plot results | |
| wav = None | |
| model_outputs = model_outputs.squeeze() | |
| if model_outputs.ndim == 2: # [T, C_spec] | |
| if use_griffin_lim: | |
| wav = inv_spectrogram(model_outputs, model.ap, CONFIG) | |
| # trim silence | |
| if do_trim_silence: | |
| wav = trim_silence(wav, model.ap) | |
| else: # [T,] | |
| wav = model_outputs | |
| return_dict = { | |
| "wav": wav, | |
| "alignments": alignments, | |
| "text_inputs": text_inputs, | |
| "outputs": outputs, | |
| } | |
| return return_dict | |
| def transfer_voice( | |
| model, | |
| CONFIG, | |
| use_cuda, | |
| reference_wav, | |
| speaker_id=None, | |
| d_vector=None, | |
| reference_speaker_id=None, | |
| reference_d_vector=None, | |
| do_trim_silence=False, | |
| use_griffin_lim=False, | |
| ): | |
| """Synthesize voice for the given text using Griffin-Lim vocoder or just compute output features to be passed to | |
| the vocoder model. | |
| Args: | |
| model (TTS.tts.models): | |
| The TTS model to synthesize audio with. | |
| CONFIG (Coqpit): | |
| Model configuration. | |
| use_cuda (bool): | |
| Enable/disable CUDA. | |
| reference_wav (str): | |
| Path of reference_wav to be used to voice conversion. | |
| speaker_id (int): | |
| Speaker ID passed to the speaker embedding layer in multi-speaker model. Defaults to None. | |
| d_vector (torch.Tensor): | |
| d-vector for multi-speaker models in share :math:`[1, D]`. Defaults to None. | |
| reference_speaker_id (int): | |
| Reference Speaker ID passed to the speaker embedding layer in multi-speaker model. Defaults to None. | |
| reference_d_vector (torch.Tensor): | |
| Reference d-vector for multi-speaker models in share :math:`[1, D]`. Defaults to None. | |
| enable_eos_bos_chars (bool): | |
| enable special chars for end of sentence and start of sentence. Defaults to False. | |
| do_trim_silence (bool): | |
| trim silence after synthesis. Defaults to False. | |
| """ | |
| # device | |
| device = next(model.parameters()).device | |
| if use_cuda: | |
| device = "cuda" | |
| # pass tensors to backend | |
| if speaker_id is not None: | |
| speaker_id = id_to_torch(speaker_id, device=device) | |
| if d_vector is not None: | |
| d_vector = embedding_to_torch(d_vector, device=device) | |
| if reference_d_vector is not None: | |
| reference_d_vector = embedding_to_torch(reference_d_vector, device=device) | |
| # load reference_wav audio | |
| reference_wav = embedding_to_torch( | |
| model.ap.load_wav( | |
| reference_wav, sr=model.args.encoder_sample_rate if model.args.encoder_sample_rate else model.ap.sample_rate | |
| ), | |
| device=device, | |
| ) | |
| if hasattr(model, "module"): | |
| _func = model.module.inference_voice_conversion | |
| else: | |
| _func = model.inference_voice_conversion | |
| model_outputs = _func(reference_wav, speaker_id, d_vector, reference_speaker_id, reference_d_vector) | |
| # convert outputs to numpy | |
| # plot results | |
| wav = None | |
| model_outputs = model_outputs.squeeze() | |
| if model_outputs.ndim == 2: # [T, C_spec] | |
| if use_griffin_lim: | |
| wav = inv_spectrogram(model_outputs, model.ap, CONFIG) | |
| # trim silence | |
| if do_trim_silence: | |
| wav = trim_silence(wav, model.ap) | |
| else: # [T,] | |
| wav = model_outputs | |
| return wav | |