Merge branch 'main' of https://github.com/alibaba-damo-academy/FunASR

nichongjia-2007

2023-07-20 17e8f5b889be2ad31608b5203dc5fbc5fd5c0f8a

 funasr/bin/asr_inference_launch.py

@@ -370,7 +370,7 @@
            results = speech2text(**batch)
            if len(results) < 1:
                hyp = Hypothesis(score=0.0, scores={}, states={}, yseq=[])
                results = [[" ", ["sil"], [2], hyp, 10, 6]] * nbest
                results = [[" ", ["sil"], [2], hyp, 10, 6, []]] * nbest
            time_end = time.time()
            forward_time = time_end - time_beg
            lfr_factor = results[0][-1]
@@ -439,6 +439,7 @@
        logging.info(rtf_avg)
        if writer is not None:
            ibest_writer["rtf"]["rtf_avf"] = rtf_avg
        torch.cuda.empty_cache()
        return asr_result_list

    return _forward
@@ -564,6 +565,7 @@
        if 'hotword' in kwargs:
            hotword_list_or_file = kwargs['hotword']

        speech2vadsegment.vad_model.vad_opts.max_single_segment_time = kwargs.get("max_single_segment_time", 60000)
        batch_size_token = kwargs.get("batch_size_token", 6000)
        print("batch_size_token: ", batch_size_token)

@@ -646,8 +648,7 @@
            beg_idx = 0
            for j, _ in enumerate(range(0, n)):
                batch_size_token_ms_cum += (sorted_data[j][0][1] - sorted_data[j][0][0])
                if j < n - 1 and (batch_size_token_ms_cum + sorted_data[j + 1][0][1] - sorted_data[j + 1][0][
                    0]) < batch_size_token_ms:
                if j < n - 1 and (batch_size_token_ms_cum + sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) < batch_size_token_ms and (sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) < speech2vadsegment.vad_model.vad_opts.max_single_segment_time:
                    continue
                batch_size_token_ms_cum = 0
                end_idx = j + 1
@@ -730,6 +731,7 @@
                    ibest_writer["time_stamp"][key] = "{}".format(time_stamp_postprocessed)

            logging.info("decoding, utt: {}, predictions: {}".format(key, text_postprocessed_punc))
        torch.cuda.empty_cache()
        return asr_result_list

    return _forward
@@ -1327,7 +1329,6 @@
        right_context: Number of frames in right context AFTER subsampling.
        display_partial_hypotheses: Whether to display partial hypotheses.
    """
    # assert check_argument_types()

    if batch_size > 1:
        raise NotImplementedError("batch decoding is not implemented")
@@ -1339,7 +1340,7 @@
        format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
    )

    if ngpu >= 1:
    if ngpu >= 1 and torch.cuda.is_available():
        device = "cuda"
    else:
        device = "cpu"
@@ -1370,10 +1371,7 @@
        left_context=left_context,
        right_context=right_context,
    )
    speech2text = Speech2TextTransducer.from_pretrained(
        model_tag=model_tag,
        **speech2text_kwargs,
    )
    speech2text = Speech2TextTransducer(**speech2text_kwargs)

    def _forward(data_path_and_name_and_type,
                 raw_inputs: Union[np.ndarray, torch.Tensor] = None,