Merge pull request #932 from alibaba-damo-academy/dev_lhn

support chunk size select for chunk-hopping encoder

funasr/bin/asr_infer.py

@@ -399,7 +399,7 @@ class Speech2TextParaformer:
     @torch.no_grad()
     def __call__(
             self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None,
-            begin_time: int = 0, end_time: int = None,
+            decoding_ind: int = None, begin_time: int = 0, end_time: int = None,
     ):
         """Inference
 
@@ -429,7 +429,9 @@ class Speech2TextParaformer:
         batch = to_device(batch, device=self.device)
 
         # b. Forward Encoder
-        enc, enc_len = self.asr_model.encode(**batch, ind=self.decoding_ind)
+        if decoding_ind is None:
+            decoding_ind = self.decoding_ind
+        enc, enc_len = self.asr_model.encode(**batch, ind=decoding_ind)
         if isinstance(enc, tuple):
             enc = enc[0]
         # assert len(enc) == 1, len(enc)

funasr/bin/asr_inference_launch.py

@@ -236,6 +236,7 @@ def inference_paraformer(
         timestamp_infer_config: Union[Path, str] = None,
         timestamp_model_file: Union[Path, str] = None,
         param_dict: dict = None,
+        decoding_ind: int = 0,
         **kwargs,
 ):
     ncpu = kwargs.get("ncpu", 1)
@@ -290,6 +291,7 @@ def inference_paraformer(
         nbest=nbest,
         hotword_list_or_file=hotword_list_or_file,
         clas_scale=clas_scale,
+        decoding_ind=decoding_ind,
     )
 
     speech2text = Speech2TextParaformer(**speech2text_kwargs)
@@ -312,6 +314,7 @@ def inference_paraformer(
             **kwargs,
     ):
 
+        decoding_ind = None
         hotword_list_or_file = None
         if param_dict is not None:
             hotword_list_or_file = param_dict.get('hotword')
@@ -319,6 +322,8 @@ def inference_paraformer(
             hotword_list_or_file = kwargs['hotword']
         if hotword_list_or_file is not None or 'hotword' in kwargs:
             speech2text.hotword_list = speech2text.generate_hotwords_list(hotword_list_or_file)
+        if param_dict is not None and "decoding_ind" in param_dict:
+            decoding_ind = param_dict["decoding_ind"]
 
         # 3. Build data-iterator
         if data_path_and_name_and_type is None and raw_inputs is not None:
@@ -365,6 +370,7 @@ def inference_paraformer(
             # N-best list of (text, token, token_int, hyp_object)
 
             time_beg = time.time()
+            batch["decoding_ind"] = decoding_ind
             results = speech2text(**batch)
             if len(results) < 1:
                 hyp = Hypothesis(score=0.0, scores={}, states={}, yseq=[])
@@ -1786,6 +1792,12 @@ def get_parser():
         default=1,
         help="The batch size for inference",
     )
+    group.add_argument(
+        "--decoding_ind",
+        type=int,
+        default=0,
+        help="chunk select for chunk encoder",
+    )
     group.add_argument("--nbest", type=int, default=5, help="Output N-best hypotheses")
     group.add_argument("--beam_size", type=int, default=20, help="Beam size")
     group.add_argument("--penalty", type=float, default=0.0, help="Insertion penalty")