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| # Copyright (c) Facebook, Inc. and its affiliates. | |
| # | |
| # This source code is licensed under the MIT license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import os | |
| import torch | |
| import commons | |
| import utils | |
| from models import SynthesizerTrn | |
| from scipy.io.wavfile import write | |
| from pathlib import Path | |
| from typing import Union | |
| class TextMapper(object): | |
| def __init__(self, vocab_file): | |
| self.symbols = [x.replace("\n", "") for x in open(vocab_file).readlines()] | |
| self.SPACE_ID = self.symbols.index(" ") | |
| self._symbol_to_id = {s: i for i, s in enumerate(self.symbols)} | |
| self._id_to_symbol = {i: s for i, s in enumerate(self.symbols)} | |
| def text_to_sequence(self, text, cleaner_names): | |
| '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text. | |
| Args: | |
| text: string to convert to a sequence | |
| cleaner_names: names of the cleaner functions to run the text through | |
| Returns: | |
| List of integers corresponding to the symbols in the text | |
| ''' | |
| sequence = [] | |
| clean_text = text.strip() | |
| for symbol in clean_text: | |
| symbol_id = self._symbol_to_id[symbol] | |
| sequence += [symbol_id] | |
| return sequence | |
| def get_text(self, text, hps): | |
| text_norm = self.text_to_sequence(text, hps.data.text_cleaners) | |
| if hps.data.add_blank: | |
| text_norm = commons.intersperse(text_norm, 0) | |
| text_norm = torch.LongTensor(text_norm) | |
| return text_norm | |
| def filter_oov(self, text): | |
| val_chars = self._symbol_to_id | |
| txt_filt = "".join(list(filter(lambda x: x in val_chars, text))) | |
| print(f"text after filtering OOV: {txt_filt}") | |
| return txt_filt | |
| class MMS(): | |
| def __init__(self, model_path: Union[str, Path]): | |
| ckpt_dir = model_path | |
| vocab_file = f"{ckpt_dir}/vocab.txt" | |
| config_file = f"{ckpt_dir}/config.json" | |
| assert os.path.isfile(config_file), f"{config_file} doesn't exist" | |
| self.hps = utils.get_hparams_from_file(config_file) | |
| self.text_mapper = TextMapper(vocab_file) | |
| self.net_g = SynthesizerTrn( | |
| len(self.text_mapper.symbols), | |
| self.hps.data.filter_length // 2 + 1, | |
| self.hps.train.segment_size // self.hps.data.hop_length, | |
| **self.hps.model) | |
| g_pth = f"{ckpt_dir}/G_100000.pth" | |
| print(f"load {g_pth}") | |
| _ = utils.load_checkpoint(g_pth, self.net_g, None) | |
| def synthesize(self, wav_path: str, txt): | |
| print(f"text: {txt}") | |
| txt = txt.lower() | |
| txt = self.text_mapper.filter_oov(txt) | |
| stn_tst = self.text_mapper.get_text(txt, self.hps) | |
| with torch.no_grad(): | |
| x_tst = stn_tst.unsqueeze(0) | |
| x_tst_lengths = torch.LongTensor([stn_tst.size(0)]) | |
| hyp = self.net_g.infer( | |
| x_tst, x_tst_lengths, noise_scale=.667, | |
| noise_scale_w=0.8, length_scale=1.0 | |
| )[0][0,0].cpu().float().numpy() | |
| os.makedirs(os.path.dirname(wav_path), exist_ok=True) | |
| print(f"wav: {wav_path}") | |
| write(wav_path, self.hps.data.sampling_rate, hyp) | |
| return |