Delete infer_onnx.py

infer_onnx.py

@@ -1,97 +0,0 @@
-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