| import os | |
| import re | |
| import onnxruntime | |
| import numpy as np | |
| from huggingface_hub import snapshot_download | |
| from gruut import sentences | |
| import numpy as np | |
| import scipy.io.wavfile | |
| 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"], | |
| local_dir=model_dir, | |
| local_dir_use_symlinks=False | |
| ) | |
| sess_options = onnxruntime.SessionOptions() | |
| self.model = onnxruntime.InferenceSession(os.path.join(model_dir, "exported/model.onnx"), sess_options=sess_options) | |
| with open(os.path.join(model_dir, "exported/vocab.txt"), "r", encoding="utf-8") as vocab_file: | |
| self.symbols = vocab_file.read().split("\n") | |
| self.symbols = list(map(chr, list(map(int, self.symbols)))) | |
| self.symbol_to_id = {s: i for i, s in enumerate(self.symbols)} | |
| self.add_time_to_end = add_time_to_end | |
| def _ru_phonems(self, text: str) -> str: | |
| text = text.lower() | |
| phonemes = "" | |
| for sent in sentences(text, lang="ru"): | |
| for word in sent: | |
| if word.phonemes: | |
| phonemes += "".join(word.phonemes) | |
| phonemes = re.sub(re.compile(r'\s+'), ' ', phonemes).lstrip().rstrip() | |
| return phonemes | |
| def _text_to_sequence(self, text: str) -> list[int]: | |
| '''convert text to seq''' | |
| sequence = [] | |
| clean_text = self._ru_phonems(text) | |
| for symbol in clean_text: | |
| symbol_id = self.symbol_to_id[symbol] | |
| sequence += [symbol_id] | |
| return sequence | |
| def _intersperse(self, lst, item): | |
| result = [item] * (len(lst) * 2 + 1) | |
| result[1::2] = lst | |
| return result | |
| def _get_text(self, text: str) -> list[int]: | |
| text_norm = self._text_to_sequence(text) | |
| text_norm = self._intersperse(text_norm, 0) | |
| return text_norm | |
| def _add_silent(self, audio, silence_duration: float = 0.7, 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): | |
| '''save audio to wav''' | |
| scipy.io.wavfile.write(path, 22050, audio) | |
| def __call__(self, text: str, play = False): | |
| phoneme_ids = self._get_text(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, 1, 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) | |
| return audio |