| | |
|---|
| | | if isinstance(raw_inputs, torch.Tensor): |
|---|
| | | raw_inputs = raw_inputs.numpy() |
|---|
| | | data_path_and_name_and_type = [raw_inputs, "speech", "waveform"] |
|---|
| | | if param_dict is not None and "decoding_model" in param_dict: |
|---|
| | | if param_dict["decoding_model"] == "fast": |
|---|
| | | speech2text.decoding_ind = 0 |
|---|
| | | speech2text.decoding_mode = "model1" |
|---|
| | | elif param_dict["decoding_model"] == "normal": |
|---|
| | | speech2text.decoding_ind = 0 |
|---|
| | | speech2text.decoding_mode = "model2" |
|---|
| | | elif param_dict["decoding_model"] == "offline": |
|---|
| | | speech2text.decoding_ind = 1 |
|---|
| | | speech2text.decoding_mode = "model2" |
|---|
| | | else: |
|---|
| | | raise NotImplementedError("unsupported decoding model {}".format(param_dict["decoding_model"])) |
|---|
| | | loader = ASRTask.build_streaming_iterator( |
|---|
| | | data_path_and_name_and_type, |
|---|
| | | dtype=dtype, |
|---|
