code format, test=doc

paddlespeech/server/engine/tts/online/tts_engine.py

@@ -127,33 +127,40 @@ class TTSServerExecutor(TTSExecutor):
         self.voc_block = voc_block
         self.voc_pad = voc_pad
 
-    def get_model_info(self, step, model_name, ckpt, stat):
+    def get_model_info(self,
+                       field: str,
+                       model_name: str,
+                       ckpt: Optional[os.PathLike],
+                       stat: Optional[os.PathLike]):
         """get model information
 
         Args:
-            step (string): am or voc
-            model_name (string): model type, support fastspeech2, higigan, mb_melgan 
-            ckpt (string): ckpt file
-            stat (string): stat file, including mean and standard deviation
+            field (str): am or voc
+            model_name (str): model type, support fastspeech2, higigan, mb_melgan
+            ckpt (Optional[os.PathLike]): ckpt file
+            stat (Optional[os.PathLike]): stat file, including mean and standard deviation
 
         Returns:
-            model, model_mu, model_std
+            [module]: model module
+            [Tensor]: mean
+            [Tensor]: standard deviation
         """
+
         model_class = dynamic_import(model_name, model_alias)
 
-        if step == "am":
+        if field == "am":
             odim = self.am_config.n_mels
             model = model_class(
                 idim=self.vocab_size, odim=odim, **self.am_config["model"])
             model.set_state_dict(paddle.load(ckpt)["main_params"])
 
-        elif step == "voc":
+        elif field == "voc":
             model = model_class(**self.voc_config["generator_params"])
             model.set_state_dict(paddle.load(ckpt)["generator_params"])
             model.remove_weight_norm()
 
         else:
-            logger.error("Please set correct step, am or voc")
+            logger.error("Please set correct field, am or voc")
 
         model.eval()
         model_mu, model_std = np.load(stat)
@@ -346,7 +353,8 @@ class TTSServerExecutor(TTSExecutor):
         voc_block = self.voc_block
         voc_pad = self.voc_pad
         voc_upsample = self.voc_config.n_shift
-        flag = 1
+        # first_flag 用于标记首包
+        first_flag = 1
 
         get_tone_ids = False
         merge_sentences = False
@@ -376,7 +384,7 @@ class TTSServerExecutor(TTSExecutor):
             if am == "fastspeech2_csmsc":
                 # am 
                 mel = self.am_inference(part_phone_ids)
-                if flag == 1:
+                if first_flag == 1:
                     first_am_et = time.time()
                     self.first_am_infer = first_am_et - frontend_et
 
@@ -388,11 +396,11 @@ class TTSServerExecutor(TTSExecutor):
                     sub_wav = self.voc_inference(mel_chunk)
                     sub_wav = self.depadding(sub_wav, voc_chunk_num, i,
                                              voc_block, voc_pad, voc_upsample)
-                    if flag == 1:
+                    if first_flag == 1:
                         first_voc_et = time.time()
                         self.first_voc_infer = first_voc_et - first_am_et
                         self.first_response_time = first_voc_et - frontend_st
-                        flag = 0
+                        first_flag = 0
 
                     yield sub_wav
 
@@ -427,9 +435,10 @@ class TTSServerExecutor(TTSExecutor):
                             (mel_streaming, sub_mel), axis=0)
 
                     # streaming voc
+                    # 当流式AM推理的mel帧数大于流式voc推理的chunk size，开始进行流式voc 推理
                     while (mel_streaming.shape[0] >= end and
                            voc_chunk_id < voc_chunk_num):
-                        if flag == 1:
+                        if first_flag == 1:
                             first_am_et = time.time()
                             self.first_am_infer = first_am_et - frontend_et
                         voc_chunk = mel_streaming[start:end, :]
@@ -439,11 +448,11 @@ class TTSServerExecutor(TTSExecutor):
                         sub_wav = self.depadding(sub_wav, voc_chunk_num,
                                                  voc_chunk_id, voc_block,
                                                  voc_pad, voc_upsample)
-                        if flag == 1:
+                        if first_flag == 1:
                             first_voc_et = time.time()
                             self.first_voc_infer = first_voc_et - first_am_et
                             self.first_response_time = first_voc_et - frontend_st
-                            flag = 0
+                            first_flag = 0
 
                         yield sub_wav
 
@@ -470,7 +479,8 @@ class TTSEngine(BaseEngine):
     def __init__(self, name=None):
         """Initialize TTS server engine
         """
-        super(TTSEngine, self).__init__()
+        #super(TTSEngine, self).__init__()
+        super().__init__()
 
     def init(self, config: dict) -> bool:
         self.config = config