|
import scipy.io.wavfile |
|
import os |
|
import onnxruntime |
|
import numpy as np |
|
from huggingface_hub import snapshot_download |
|
|
|
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 |