| | |
|---|
| | | **kwargs, |
|---|
| | | ): |
|---|
| | | assert check_argument_types() |
|---|
| | | ncpu = kwargs.get("ncpu", 1) |
|---|
| | | torch.set_num_threads(ncpu) |
|---|
| | | |
|---|
| | | if batch_size > 1: |
|---|
| | | raise NotImplementedError("batch decoding is not implemented") |
|---|
| | | if ngpu > 1: |
|---|
| | | raise NotImplementedError("only single GPU decoding is supported") |
|---|
| | | |
|---|
| | | logging.basicConfig( |
|---|
| | | level=log_level, |
|---|
| | |
|---|
| | | device = "cuda" |
|---|
| | | else: |
|---|
| | | device = "cpu" |
|---|
| | | |
|---|
| | | batch_size = 1 |
|---|
| | | # 1. Set random-seed |
|---|
| | | set_all_random_seed(seed) |
|---|
| | | |
|---|
| | |
|---|
| | | |
|---|
| | | vad_results = [] |
|---|
| | | batch_in_cache = param_dict['in_cache'] if param_dict is not None else dict() |
|---|
| | | is_final = param_dict['is_final'] if param_dict is not None else False |
|---|
| | | max_end_sil = param_dict['max_end_sil'] if param_dict is not None else 800 |
|---|
| | | is_final = param_dict.get('is_final', False) if param_dict is not None else False |
|---|
| | | max_end_sil = param_dict.get('max_end_sil', 800) if param_dict is not None else 800 |
|---|
| | | for keys, batch in loader: |
|---|
| | | assert isinstance(batch, dict), type(batch) |
|---|
| | | assert all(isinstance(s, str) for s in keys), keys |
|---|
| | |
|---|
| | | item = {'key': keys[i], 'value': results[i]} |
|---|
| | | vad_results.append(item) |
|---|
| | | if writer is not None: |
|---|
| | | results[i] = json.loads(results[i]) |
|---|
| | | ibest_writer["text"][keys[i]] = "{}".format(results[i]) |
|---|
| | | |
|---|
| | | return vad_results |
|---|
