| | |
|---|
| | | kwargs.update(cfg) |
|---|
| | | model = self.model if model is None else model |
|---|
| | | model.eval() |
|---|
| | | pdb.set_trace() |
|---|
| | | |
|---|
| | | batch_size = kwargs.get("batch_size", 1) |
|---|
| | | # if kwargs.get("device", "cpu") == "cpu": |
|---|
| | | # batch_size = 1 |
|---|
| | | |
|---|
| | | key_list, data_list = prepare_data_iterator(input, input_len=input_len, data_type=kwargs.get("data_type", None), key=key) |
|---|
| | | pdb.set_trace() |
|---|
| | | |
|---|
| | | speed_stats = {} |
|---|
| | | asr_result_list = [] |
|---|
| | |
|---|
| | | pbar = tqdm(colour="blue", total=num_samples, dynamic_ncols=True) if not disable_pbar else None |
|---|
| | | time_speech_total = 0.0 |
|---|
| | | time_escape_total = 0.0 |
|---|
| | | pdb.set_trace() |
|---|
| | | for beg_idx in range(0, num_samples, batch_size): |
|---|
| | | pdb.set_trace() |
|---|
| | | end_idx = min(num_samples, beg_idx + batch_size) |
|---|
| | | data_batch = data_list[beg_idx:end_idx] |
|---|
| | | key_batch = key_list[beg_idx:end_idx] |
|---|
| | | batch = {"data_in": data_batch, "key": key_batch} |
|---|
| | | pdb.set_trace() |
|---|
| | | |
|---|
| | | if (end_idx - beg_idx) == 1 and kwargs.get("data_type", None) == "fbank": # fbank |
|---|
| | | batch["data_in"] = data_batch[0] |
|---|
| | | batch["data_lengths"] = input_len |
|---|
