| | |
|---|
| | | 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, |
|---|
| | |
|---|
| | | ): |
|---|
| | | |
|---|
| | | # 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) |
|---|
