| | |
|---|
| | | set_all_random_seed(kwargs.get("seed", 0)) |
|---|
| | | |
|---|
| | | device = kwargs.get("device", "cuda") |
|---|
| | | if not torch.cuda.is_available() or kwargs.get("ngpu", 0): |
|---|
| | | if not torch.cuda.is_available() or kwargs.get("ngpu", 0) == 0: |
|---|
| | | device = "cpu" |
|---|
| | | kwargs["batch_size"] = 1 |
|---|
| | | kwargs["device"] = device |
|---|
| | |
|---|
| | | result[k] = restored_data[j][k] |
|---|
| | | else: |
|---|
| | | result[k] = torch.cat([result[k], restored_data[j][k]], dim=0) |
|---|
| | | elif k == 'text': |
|---|
| | | elif k == 'raw_text': |
|---|
| | | if k not in result: |
|---|
| | | result[k] = restored_data[j][k] |
|---|
| | | else: |
|---|
| | |
|---|
| | | else: # punc_segment |
|---|
| | | sentence_list = timestamp_sentence(punc_res[0]['punc_array'], \ |
|---|
| | | result['timestamp'], \ |
|---|
| | | result['text']) |
|---|
| | | result['raw_text']) |
|---|
| | | distribute_spk(sentence_list, sv_output) |
|---|
| | | result['sentence_info'] = sentence_list |
|---|
| | | |
|---|
