Merge pull request #319 from alibaba-damo-academy/dev_lhn

support bytes input

funasr/bin/asr_inference_paraformer_streaming.py

@@ -581,7 +581,22 @@ def inference_modelscope(
         speech2text = Speech2TextExport(**speech2text_kwargs)
     else:
         speech2text = Speech2Text(**speech2text_kwargs)
-
+        
+    def _load_bytes(input):
+        middle_data = np.frombuffer(input, dtype=np.int16)
+        middle_data = np.asarray(middle_data)
+        if middle_data.dtype.kind not in 'iu':
+            raise TypeError("'middle_data' must be an array of integers")
+        dtype = np.dtype('float32')
+        if dtype.kind != 'f':
+            raise TypeError("'dtype' must be a floating point type")
+
+        i = np.iinfo(middle_data.dtype)
+        abs_max = 2 ** (i.bits - 1)
+        offset = i.min + abs_max
+        array = np.frombuffer((middle_data.astype(dtype) - offset) / abs_max, dtype=np.float32)
+        return array
+    
     def _forward(
             data_path_and_name_and_type,
             raw_inputs: Union[np.ndarray, torch.Tensor] = None,
@@ -592,6 +607,9 @@ def inference_modelscope(
     ):
 
         # 3. Build data-iterator
+        if data_path_and_name_and_type is not None and data_path_and_name_and_type[2] == "bytes":
+            raw_inputs = _load_bytes(data_path_and_name_and_type[0])
+            raw_inputs = torch.tensor(raw_inputs)
         if data_path_and_name_and_type is None and raw_inputs is not None:
             if isinstance(raw_inputs, np.ndarray):
                 raw_inputs = torch.tensor(raw_inputs)