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 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']) 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): '''save audio to wav''' scipy.io.wavfile.write(path, 22050, 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) return audio