| | |
|---|
| | | asr_result = speech2text(cache, raw_inputs[:, sample_offset: sample_offset + stride_size], input_lens) |
|---|
| | | if len(asr_result) != 0: |
|---|
| | | final_result += " ".join(asr_result) + " " |
|---|
| | | item = {'key': "utt", 'value': [final_result.strip()]} |
|---|
| | | item = {'key': "utt", 'value': final_result.strip()} |
|---|
| | | else: |
|---|
| | | input_lens = torch.tensor([raw_inputs.shape[1]]) |
|---|
| | | cache["encoder"]["is_final"] = is_final |
|---|
| | | asr_result = speech2text(cache, raw_inputs, input_lens) |
|---|
| | | item = {'key': "utt", 'value': asr_result} |
|---|
| | | item = {'key': "utt", 'value': " ".join(asr_result)} |
|---|
| | | |
|---|
| | | asr_result_list.append(item) |
|---|
| | | if is_final: |
|---|
