增加了注释 GPT_SoVITS/TTS_infer_pack/TTS.py

GPT_SoVITS/TTS_infer_pack/TTS.py

@@ -21,7 +21,7 @@ from .text_segmentation_method import splits
 from .TextPreprocessor import TextPreprocessor
 i18n = I18nAuto()
 
-# tts_infer.yaml
+# configs/tts_infer.yaml
 """
 default:
   device: cpu
@@ -240,6 +240,12 @@ class TTS:
         self.t2s_model = t2s_model
         
     def set_ref_audio(self, ref_audio_path:str):
+        '''
+            To set the reference audio for the TTS model, 
+                including the prompt_semantic and refer_spepc.
+            Args:
+                ref_audio_path: str, the path of the reference audio.
+        '''
         self._set_prompt_semantic(ref_audio_path)
         self._set_ref_spepc(ref_audio_path)
         
@@ -399,6 +405,16 @@ class TTS:
         return _data, batch_index_list
     
     def recovery_order(self, data:list, batch_index_list:list)->list:
+        '''
+        Recovery the order of the audio according to the batch_index_list.
+        
+        Args:
+            data (List[list(np.ndarray)]): the out of order audio .
+            batch_index_list (List[list[int]]): the batch index list.
+        
+        Returns:
+            list (List[np.ndarray]): the data in the original order.
+        '''
         lenght = len(sum(batch_index_list, []))
         _data = [None]*lenght
         for i, index_list in enumerate(batch_index_list):
@@ -407,6 +423,9 @@ class TTS:
         return _data
 
     def stop(self,):
+        '''
+        Stop the inference process.
+        '''
         self.stop_flag = True
         
     
@@ -435,8 +454,8 @@ class TTS:
         returns:
             tulpe[int, np.ndarray]: sampling rate and audio data.
         """
+        ########## variables initialization ###########
         self.stop_flag:bool = False
-        
         text:str = inputs.get("text", "")
         text_lang:str = inputs.get("text_lang", "")
         ref_audio_path:str = inputs.get("ref_audio_path", "")
@@ -475,6 +494,8 @@ class TTS:
             ((self.prompt_cache["prompt_semantic"] is None) or (self.prompt_cache["refer_spepc"] is None)):
             raise ValueError("ref_audio_path cannot be empty, when the reference audio is not set using set_ref_audio()")
 
+
+        ###### setting reference audio and prompt text preprocessing ########
         t0 = ttime()
         if (ref_audio_path is not None) and (ref_audio_path != self.prompt_cache["ref_audio_path"]):
                 self.set_ref_audio(ref_audio_path)
@@ -494,12 +515,8 @@ class TTS:
                 self.prompt_cache["bert_features"] = bert_features
                 self.prompt_cache["norm_text"] = norm_text
                 
-        zero_wav = np.zeros(
-            int(self.configs.sampling_rate * 0.3),
-            dtype=np.float16 if self.configs.is_half else np.float32,
-        )
-        
         
+        ###### text preprocessing ########
         data = self.text_preprocessor.preprocess(text, text_lang, text_split_method)
         audio = []
         t1 = ttime()
@@ -516,6 +533,8 @@ class TTS:
                         device=self.configs.device
                     )
         
+        
+        ###### inference ######
         t_34 = 0.0
         t_45 = 0.0
         for item in data:
@@ -525,12 +544,10 @@ class TTS:
             all_bert_features = item["all_bert_features"]
             norm_text = item["norm_text"]
             
-            # phones = phones.to(self.configs.device)
             all_phoneme_ids = all_phoneme_ids.to(self.configs.device)
             all_bert_features = all_bert_features.to(self.configs.device)
             if self.configs.is_half:
                 all_bert_features = all_bert_features.half()
-            # all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]*all_phoneme_ids.shape[0], device=self.configs.device)
     
             print(i18n("前端处理后的文本(每句):"), norm_text)
             if no_prompt_text :
@@ -539,7 +556,6 @@ class TTS:
                 prompt = self.prompt_cache["prompt_semantic"].clone().repeat(all_phoneme_ids.shape[0], 1).to(self.configs.device)
             
             with torch.no_grad():
-                # pred_semantic = t2s_model.model.infer(
                 pred_semantic_list, idx_list = self.t2s_model.model.infer_panel(
                     all_phoneme_ids,
                     None,