| | |
|---|
| | | # FIXME(kamo): The output format should be discussed about |
|---|
| | | raw_inputs = torch.unsqueeze(raw_inputs, axis=0) |
|---|
| | | asr_result_list = [] |
|---|
| | | cache = _prepare_cache(cache, chunk_size=chunk_size, batch_size=1) |
|---|
| | | cache = _prepare_cache(cache, chunk_size=chunk_size, encoder_chunk_look_back=encoder_chunk_look_back, |
|---|
| | | decoder_chunk_look_back=decoder_chunk_look_back, batch_size=1) |
|---|
| | | item = {} |
|---|
| | | if data_path_and_name_and_type is not None and data_path_and_name_and_type[2] == "sound": |
|---|
| | | sample_offset = 0 |
|---|
| | | speech_length = raw_inputs.shape[1] |
|---|
| | | stride_size = chunk_size[1] * 960 |
|---|
| | | cache = _prepare_cache(cache, chunk_size=chunk_size, batch_size=1, |
|---|
| | | encoder_chunk_look_back=encoder_chunk_look_back, decoder_chunk_look_back=decoder_chunk_look_back) |
|---|
| | | cache = _prepare_cache(cache, chunk_size=chunk_size, encoder_chunk_look_back=encoder_chunk_look_back, |
|---|
| | | decoder_chunk_look_back=decoder_chunk_look_back, batch_size=1) |
|---|
| | | final_result = "" |
|---|
| | | for sample_offset in range(0, speech_length, min(stride_size, speech_length - sample_offset)): |
|---|
| | | if sample_offset + stride_size >= speech_length - 1: |
|---|
