| import scipy.io.wavfile | |
| import os | |
| import onnxruntime | |
| import numpy as np | |
| from huggingface_hub import snapshot_download | |
| from num2words import num2words | |
| import re | |
| from transliterate import translit | |
| import json | |
| class TTS: | |
| def __init__(self, model_name: str, save_path: str = "./model", add_time_to_end: float = 0.8) -> None: | |
| if not os.path.exists(save_path): | |
| os.mkdir(save_path) | |
| model_dir = os.path.join(save_path, model_name) | |
| if not os.path.exists(model_dir): | |
| snapshot_download(repo_id=model_name, | |
| allow_patterns=["*.txt", "*.onnx", "*.json"], | |
| local_dir=model_dir, | |
| local_dir_use_symlinks=False | |
| ) | |
| self.model = onnxruntime.InferenceSession(os.path.join(model_dir, "exported/model.onnx"), providers=['CPUExecutionProvider']) | |
| with open(os.path.join(model_dir, "exported/config.json")) as config_file: | |
| self.config = json.load(config_file)["model_config"] | |
| if os.path.exists(os.path.join(model_dir, "exported/dictionary.txt")): | |
| from tokenizer import TokenizerG2P | |
| print("Use g2p") | |
| self.tokenizer = TokenizerG2P(os.path.join(model_dir, "exported")) | |
| else: | |
| from tokenizer import TokenizerGRUUT | |
| print("Use gruut") | |
| self.tokenizer = TokenizerGRUUT(os.path.join(model_dir, "exported")) | |
| self.add_time_to_end = add_time_to_end | |
| def _add_silent(self, audio, silence_duration: float = 1.0, sample_rate: int = 22050): | |
| num_samples_silence = int(sample_rate * silence_duration) | |
| silence_array = np.zeros(num_samples_silence, dtype=np.float32) | |
| audio_with_silence = np.concatenate((audio, silence_array), axis=0) | |
| return audio_with_silence | |
| def save_wav(self, audio, path:str, sample_rate: int = 22050): | |
| '''save audio to wav''' | |
| scipy.io.wavfile.write(path, sample_rate, audio) | |
| def _intersperse(self, lst, item): | |
| result = [item] * (len(lst) * 2 + 1) | |
| result[1::2] = lst | |
| return result | |
| def _get_seq(self, text): | |
| phoneme_ids = self.tokenizer._get_seq(text) | |
| phoneme_ids_inter = self._intersperse(phoneme_ids, 0) | |
| return phoneme_ids_inter | |
| def _num2wordsshor(self, match): | |
| match = match.group() | |
| ret = num2words(match, lang ='ru') | |
| return ret | |
| def __call__(self, text: str, length_scale=1.2): | |
| text = translit(text, 'ru') | |
| text = re.sub(r'\d+',self._num2wordsshor,text) | |
| phoneme_ids = self._get_seq(text) | |
| text = np.expand_dims(np.array(phoneme_ids, dtype=np.int64), 0) | |
| text_lengths = np.array([text.shape[1]], dtype=np.int64) | |
| scales = np.array( | |
| [0.667, length_scale, 0.8], | |
| dtype=np.float32, | |
| ) | |
| audio = self.model.run( | |
| None, | |
| { | |
| "input": text, | |
| "input_lengths": text_lengths, | |
| "scales": scales, | |
| "sid": None, | |
| }, | |
| )[0][0,0][0] | |
| audio = self._add_silent(audio, silence_duration = self.add_time_to_end, sample_rate=self.config["samplerate"]) | |
| return audio |