[update] 更新funasr parformer_streaming cache管理，增加seesion_id字段

utils/stt/funasr_utils.py

@@ -79,9 +79,9 @@ class FunAutoSpeechRecognizer(STTBase):
     def _init_asr(self):
         # 随机初始化一段音频数据
         init_audio_data = np.random.randint(-32768, 32767, size=self.chunk_partial_size, dtype=np.int16)
-        self.asr_model.generate(input=init_audio_data, cache=self.asr_cache, is_final=False, chunk_size=self.chunk_size, encoder_chunk_look_back=self.encoder_chunk_look_back, decoder_chunk_look_back=self.decoder_chunk_look_back)
+        self.session_signup("init")
-        self.audio_cache = {}
+        self.asr_model.generate(input=init_audio_data, cache=self.asr_cache, is_final=False, chunk_size=self.chunk_size, encoder_chunk_look_back=self.encoder_chunk_look_back, decoder_chunk_look_back=self.decoder_chunk_look_back, session_id="init")
-        self.asr_cache = {}
+        self.session_signout("init")
         # print("init ASR model done.")
     
     # when chat trying to use asr , sign up
@@ -108,6 +108,82 @@ class FunAutoSpeechRecognizer(STTBase):
         """
         text_dict = dict(text=[], is_end=is_end)
 
+        audio_cache = self.audio_cache[session_id]
+        # asr_cache = self.asr_cache[session_id]
+        
+        audio_data = self.check_audio_type(audio_data)
+        if audio_cache is None:
+            audio_cache = audio_data
+        else:
+            if audio_cache.shape[0] > 0:
+                audio_cache = np.concatenate([audio_cache, audio_data], axis=0)
+        
+        if not is_end and audio_cache.shape[0] < self.chunk_partial_size:
+            self.audio_cache[session_id] = audio_cache
+            return text_dict
+        
+        total_chunk_num = int((len(audio_cache)-1)/self.chunk_partial_size)
+        
+        if is_end:
+            # if the audio data is the end of a sentence, \
+            # we need to add one more chunk to the end to \
+            # ensure the end of the sentence is recognized correctly.
+            auto_det_end = True
+        
+        if auto_det_end:
+            total_chunk_num += 1
+
+        end_idx = None
+        for i in range(total_chunk_num):
+            if auto_det_end:
+                is_end = i == total_chunk_num - 1
+            start_idx = i*self.chunk_partial_size
+            if auto_det_end:
+                end_idx = (i+1)*self.chunk_partial_size if i < total_chunk_num-1 else -1
+            else:
+                end_idx = (i+1)*self.chunk_partial_size if i < total_chunk_num else -1
+            # print(f"cut part: {start_idx}:{end_idx}, is_end: {is_end}, i: {i}, total_chunk_num: {total_chunk_num}")
+            # t_stamp = time.time()
+            
+            speech_chunk = audio_cache[start_idx:end_idx]
+
+            # TODO: exceptions processes
+            print("i:", i)
+            try:
+                res = self.asr_model.generate(input=speech_chunk, cache=self.asr_cache, is_final=is_end, chunk_size=self.chunk_size, encoder_chunk_look_back=self.encoder_chunk_look_back, decoder_chunk_look_back=self.decoder_chunk_look_back, session_id=session_id)
+            except ValueError as e:
+                print(f"ValueError: {e}")
+                continue
+            text_dict['text'].append(self.text_postprecess(res[0], data_id='text'))
+            # print(f"each chunk time: {time.time()-t_stamp}")
+            
+        if is_end:
+            audio_cache = None
+            asr_cache = {}
+        else:
+            if end_idx:
+                audio_cache = audio_cache[end_idx:] # cut the processed part from audio_cache
+        text_dict['is_end'] = is_end
+        
+        
+        self.audio_cache[session_id] = audio_cache
+        # self.asr_cache[session_id] = asr_cache
+        return text_dict
+    
+    def streaming_recognize_origin(self,
+                            session_id, 
+                            audio_data, 
+                            is_end=False, 
+                            auto_det_end=False):
+        """recognize partial result
+        
+        Args:
+            audio_data: bytes or numpy array, partial audio data
+            is_end: bool, whether the audio data is the end of a sentence
+            auto_det_end: bool, whether to automatically detect the end of a audio data
+        """
+        text_dict = dict(text=[], is_end=is_end)
+
         audio_cache = self.audio_cache[session_id]
         asr_cache = self.asr_cache[session_id]