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| import torch | |
| from synthesizer import audio | |
| from synthesizer.hparams import hparams | |
| from synthesizer.models.tacotron import Tacotron | |
| from synthesizer.utils.symbols import symbols | |
| from synthesizer.utils.text import text_to_sequence | |
| from vocoder.display import simple_table | |
| from pathlib import Path | |
| from typing import Union, List | |
| import numpy as np | |
| import librosa | |
| class Synthesizer: | |
| sample_rate = hparams.sample_rate | |
| hparams = hparams | |
| def __init__(self, model_fpath: Path, verbose=True): | |
| """ | |
| The model isn't instantiated and loaded in memory until needed or until load() is called. | |
| :param model_fpath: path to the trained model file | |
| :param verbose: if False, prints less information when using the model | |
| """ | |
| self.model_fpath = model_fpath | |
| self.verbose = verbose | |
| # Check for GPU | |
| if torch.cuda.is_available(): | |
| self.device = torch.device("cuda") | |
| else: | |
| self.device = torch.device("cpu") | |
| if self.verbose: | |
| print("Synthesizer using device:", self.device) | |
| # Tacotron model will be instantiated later on first use. | |
| self._model = None | |
| def is_loaded(self): | |
| """ | |
| Whether the model is loaded in memory. | |
| """ | |
| return self._model is not None | |
| def load(self): | |
| """ | |
| Instantiates and loads the model given the weights file that was passed in the constructor. | |
| """ | |
| self._model = Tacotron(embed_dims=hparams.tts_embed_dims, | |
| num_chars=len(symbols), | |
| encoder_dims=hparams.tts_encoder_dims, | |
| decoder_dims=hparams.tts_decoder_dims, | |
| n_mels=hparams.num_mels, | |
| fft_bins=hparams.num_mels, | |
| postnet_dims=hparams.tts_postnet_dims, | |
| encoder_K=hparams.tts_encoder_K, | |
| lstm_dims=hparams.tts_lstm_dims, | |
| postnet_K=hparams.tts_postnet_K, | |
| num_highways=hparams.tts_num_highways, | |
| dropout=hparams.tts_dropout, | |
| stop_threshold=hparams.tts_stop_threshold, | |
| speaker_embedding_size=hparams.speaker_embedding_size).to(self.device) | |
| self._model.load(self.model_fpath) | |
| self._model.eval() | |
| if self.verbose: | |
| print("Loaded synthesizer \"%s\" trained to step %d" % (self.model_fpath.name, self._model.state_dict()["step"])) | |
| def synthesize_spectrograms(self, texts: List[str], | |
| embeddings: Union[np.ndarray, List[np.ndarray]], | |
| return_alignments=False): | |
| """ | |
| Synthesizes mel spectrograms from texts and speaker embeddings. | |
| :param texts: a list of N text prompts to be synthesized | |
| :param embeddings: a numpy array or list of speaker embeddings of shape (N, 256) | |
| :param return_alignments: if True, a matrix representing the alignments between the | |
| characters | |
| and each decoder output step will be returned for each spectrogram | |
| :return: a list of N melspectrograms as numpy arrays of shape (80, Mi), where Mi is the | |
| sequence length of spectrogram i, and possibly the alignments. | |
| """ | |
| # Load the model on the first request. | |
| if not self.is_loaded(): | |
| self.load() | |
| # Preprocess text inputs | |
| inputs = [text_to_sequence(text.strip(), hparams.tts_cleaner_names) for text in texts] | |
| if not isinstance(embeddings, list): | |
| embeddings = [embeddings] | |
| # Batch inputs | |
| batched_inputs = [inputs[i:i+hparams.synthesis_batch_size] | |
| for i in range(0, len(inputs), hparams.synthesis_batch_size)] | |
| batched_embeds = [embeddings[i:i+hparams.synthesis_batch_size] | |
| for i in range(0, len(embeddings), hparams.synthesis_batch_size)] | |
| specs = [] | |
| for i, batch in enumerate(batched_inputs, 1): | |
| if self.verbose: | |
| print(f"\n| Generating {i}/{len(batched_inputs)}") | |
| # Pad texts so they are all the same length | |
| text_lens = [len(text) for text in batch] | |
| max_text_len = max(text_lens) | |
| chars = [pad1d(text, max_text_len) for text in batch] | |
| chars = np.stack(chars) | |
| # Stack speaker embeddings into 2D array for batch processing | |
| speaker_embeds = np.stack(batched_embeds[i-1]) | |
| # Convert to tensor | |
| chars = torch.tensor(chars).long().to(self.device) | |
| speaker_embeddings = torch.tensor(speaker_embeds).float().to(self.device) | |
| # Inference | |
| _, mels, alignments = self._model.generate(chars, speaker_embeddings) | |
| mels = mels.detach().cpu().numpy() | |
| for m in mels: | |
| # Trim silence from end of each spectrogram | |
| while np.max(m[:, -1]) < hparams.tts_stop_threshold: | |
| m = m[:, :-1] | |
| specs.append(m) | |
| if self.verbose: | |
| print("\n\nDone.\n") | |
| return (specs, alignments) if return_alignments else specs | |
| def load_preprocess_wav(fpath): | |
| """ | |
| Loads and preprocesses an audio file under the same conditions the audio files were used to | |
| train the synthesizer. | |
| """ | |
| wav = librosa.load(str(fpath), hparams.sample_rate)[0] | |
| if hparams.rescale: | |
| wav = wav / np.abs(wav).max() * hparams.rescaling_max | |
| return wav | |
| def make_spectrogram(fpath_or_wav: Union[str, Path, np.ndarray]): | |
| """ | |
| Creates a mel spectrogram from an audio file in the same manner as the mel spectrograms that | |
| were fed to the synthesizer when training. | |
| """ | |
| if isinstance(fpath_or_wav, str) or isinstance(fpath_or_wav, Path): | |
| wav = Synthesizer.load_preprocess_wav(fpath_or_wav) | |
| else: | |
| wav = fpath_or_wav | |
| mel_spectrogram = audio.melspectrogram(wav, hparams).astype(np.float32) | |
| return mel_spectrogram | |
| def griffin_lim(mel): | |
| """ | |
| Inverts a mel spectrogram using Griffin-Lim. The mel spectrogram is expected to have been built | |
| with the same parameters present in hparams.py. | |
| """ | |
| return audio.inv_mel_spectrogram(mel, hparams) | |
| def pad1d(x, max_len, pad_value=0): | |
| return np.pad(x, (0, max_len - len(x)), mode="constant", constant_values=pad_value) | |