| | |
|---|
| | | device = "cuda" |
|---|
| | | else: |
|---|
| | | device = "cpu" |
|---|
| | | |
|---|
| | | if param_dict is not None and "decoding_model" in param_dict: |
|---|
| | | if param_dict["decoding_model"] == "fast": |
|---|
| | | decoding_ind = 0 |
|---|
| | | decoding_mode = "model1" |
|---|
| | | elif param_dict["decoding_model"] == "normal": |
|---|
| | | decoding_ind = 0 |
|---|
| | | decoding_mode = "model2" |
|---|
| | | elif param_dict["decoding_model"] == "offline": |
|---|
| | | decoding_ind = 1 |
|---|
| | | decoding_mode = "model2" |
|---|
| | | else: |
|---|
| | | raise NotImplementedError("unsupported decoding model {}".format(param_dict["decoding_model"])) |
|---|
| | | |
|---|
| | | # 1. Set random-seed |
|---|
| | | set_all_random_seed(seed) |
|---|
| | |
|---|
| | | output_dir_v2: Optional[str] = None, |
|---|
| | | fs: dict = None, |
|---|
| | | param_dict: dict = None, |
|---|
| | | **kwargs, |
|---|
| | | ): |
|---|
| | | # 3. Build data-iterator |
|---|
| | | if data_path_and_name_and_type is None and raw_inputs is not None: |
|---|
| | |
|---|
| | | ibest_writer["score"][key] = str(hyp.score) |
|---|
| | | |
|---|
| | | if text is not None: |
|---|
| | | text_postprocessed = postprocess_utils.sentence_postprocess(token) |
|---|
| | | text_postprocessed, _ = postprocess_utils.sentence_postprocess(token) |
|---|
| | | item = {'key': key, 'value': text_postprocessed} |
|---|
| | | asr_result_list.append(item) |
|---|
| | | finish_count += 1 |
|---|
