check chunk window process, test=doc

paddlespeech/server/engine/asr/online/asr_engine.py

@@ -145,6 +145,8 @@ class PaddleASRConnectionHanddler:
             if self.cached_feat is None:
                 self.cached_feat = x_chunk
             else:
+                assert(len(x_chunk.shape) == 3)
+                assert(len(self.cached_feat.shape) == 3)
                 self.cached_feat = paddle.concat([self.cached_feat, x_chunk], axis=1)
             
             num_frames = x_chunk.shape[1]
@@ -170,6 +172,7 @@ class PaddleASRConnectionHanddler:
         self.num_samples = 0
 
         self.num_frames = 0
+        self.chunk_num = 0
         self.global_frame_offset = 0
         self.result = []
 
@@ -210,23 +213,24 @@ class PaddleASRConnectionHanddler:
         if num_frames < decoding_window and not is_finished:
             return None, None
         
-        # logger.info("start to do model forward")
-        # required_cache_size = decoding_chunk_size * num_decoding_left_chunks
-        # outputs = []
-
-        # # num_frames - context + 1 ensure that current frame can get context window
-        # if is_finished:
-        #      # if get the finished chunk, we need process the last context
-        #     left_frames = context
-        # else:
-        #     # we only process decoding_window frames for one chunk
-        #     left_frames = decoding_window 
+        logger.info("start to do model forward")
+        required_cache_size = decoding_chunk_size * num_decoding_left_chunks
+        outputs = []
+
+        # num_frames - context + 1 ensure that current frame can get context window
+        if is_finished:
+             # if get the finished chunk, we need process the last context
+            left_frames = context
+        else:
+            # we only process decoding_window frames for one chunk
+            left_frames = decoding_window 
         
         # logger.info(f"")
-        # end = None
-        # for cur in range(0, num_frames - left_frames + 1, stride):
-        #     end = min(cur + decoding_window, num_frames)
-        #     print(f"cur: {cur}, end: {end}")
+        end = None
+        for cur in range(0, num_frames - left_frames + 1, stride):
+            end = min(cur + decoding_window, num_frames)
+            print(f"cur chunk: {self.chunk_num}, cur: {cur}, end: {end}")
+            self.chunk_num += 1
             # chunk_xs = self.cached_feat[:, cur:end, :]
             # (y, self.subsampling_cache, self.elayers_output_cache,
             #  self.conformer_cnn_cache) = self.model.encoder.forward_chunk(
@@ -236,7 +240,14 @@ class PaddleASRConnectionHanddler:
             # outputs.append(y)
             # update the offset
             # self.offset += y.shape[1]
-        # self.cached_feat = self.cached_feat[end:]
+        
+        # remove the processed feat
+        if end == num_frames:
+            self.cached_feat = None 
+        else:
+            assert self.cached_feat.shape[0] == 1
+            self.cached_feat = self.cached_feat[0,end:,:].unsqueeze(0)
+            assert len(self.cached_feat.shape) == 3, f"current cache feat shape is: {self.cached_feat.shape}"
         # ys = paddle.cat(outputs, 1)
         # masks = paddle.ones([1, ys.shape[1]], dtype=paddle.bool)
         # masks = masks.unsqueeze(1)
@@ -309,9 +320,9 @@ class ASRServerExecutor(ASRExecutor):
             logger.info(f"Load the pretrained model, tag = {tag}")
             res_path = self._get_pretrained_path(tag)  # wenetspeech_zh
             self.res_path = res_path
-            # self.cfg_path = "/home/users/xiongxinlei/task/paddlespeech-develop/PaddleSpeech/examples/aishell/asr1/model.yaml"
-            self.cfg_path = os.path.join(res_path,
-                                         pretrained_models[tag]['cfg_path'])
+            self.cfg_path = "/home/users/xiongxinlei/task/paddlespeech-develop/PaddleSpeech/examples/aishell/asr1/model.yaml"
+            # self.cfg_path = os.path.join(res_path,
+            #                              pretrained_models[tag]['cfg_path'])
 
             self.am_model = os.path.join(res_path,
                                          pretrained_models[tag]['model'])