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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 |