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