Merge pull request #500 from alibaba-damo-academy/dev_clipvideo

zhifu gao

2023-05-11 33f6e77c11a80c067c880f57fa061f92b6ff88a6

Merge pull request #500 from alibaba-damo-academy/dev_clipvideo

Dev clipvideo

 egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/demo.py

@@ -3,7 +3,9 @@

inference_pipeline = pipeline(
    task=Tasks.auto_speech_recognition,
    model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch')

rec_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav')
    model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch',
    batch_size=64,
)
audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav'
rec_result = inference_pipeline(audio_in=audio_in)
print(rec_result)

 egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/demo.py

@@ -2,14 +2,15 @@
from modelscope.utils.constant import Tasks

if __name__ == '__main__':
    audio_in = 'https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav'
    audio_in = 'https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav'
    output_dir = None
    inference_pipeline = pipeline(
        task=Tasks.auto_speech_recognition,
        model='damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch',
        vad_model='damo/speech_fsmn_vad_zh-cn-16k-common-pytorch',
        punc_model='damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch',
        output_dir=output_dir
        output_dir=output_dir,
        batch_size=64,
    )
    rec_result = inference_pipeline(audio_in=audio_in)
    print(rec_result)

 egs_modelscope/tp/speech_timestamp_prediction-v1-16k-offline/README.md

@@ -1 +1 @@
../../TEMPLATE/README.md
../TEMPLATE/README.md

 egs_modelscope/tp/speech_timestamp_prediction-v1-16k-offline/infer.py

@@ -1 +1 @@
../../TEMPLATE/infer.py
../TEMPLATE/infer.py

 egs_modelscope/tp/speech_timestamp_prediction-v1-16k-offline/infer.sh

@@ -1 +1 @@
../../TEMPLATE/infer.sh
../TEMPLATE/infer.sh

 funasr/bin/asr_inference_launch.py

@@ -254,27 +254,15 @@
    elif mode == "uniasr":
        from funasr.bin.asr_inference_uniasr import inference_modelscope
        return inference_modelscope(**kwargs)
    elif mode == "uniasr_vad":
        from funasr.bin.asr_inference_uniasr_vad import inference_modelscope
        return inference_modelscope(**kwargs)
    elif mode == "paraformer":
        from funasr.bin.asr_inference_paraformer import inference_modelscope
        return inference_modelscope(**kwargs)
    elif mode == "paraformer_streaming":
        from funasr.bin.asr_inference_paraformer_streaming import inference_modelscope
        return inference_modelscope(**kwargs)
    elif mode == "paraformer_vad":
        from funasr.bin.asr_inference_paraformer_vad import inference_modelscope
        return inference_modelscope(**kwargs)
    elif mode == "paraformer_punc":
        logging.info("Unknown decoding mode: {}".format(mode))
        return None
    elif mode == "paraformer_vad_punc":
        from funasr.bin.asr_inference_paraformer_vad_punc import inference_modelscope
        return inference_modelscope(**kwargs)
    elif mode == "vad":
        from funasr.bin.vad_inference import inference_modelscope
        return inference_modelscope(**kwargs)
    elif mode.startswith("paraformer_vad"):
        from funasr.bin.asr_inference_paraformer import inference_modelscope_vad_punc
        return inference_modelscope_vad_punc(**kwargs)
    elif mode == "mfcca":
        from funasr.bin.asr_inference_mfcca import inference_modelscope
        return inference_modelscope(**kwargs)
@@ -301,14 +289,13 @@
        from funasr.bin.asr_inference_uniasr import inference
        return inference(**kwargs)
    elif mode == "paraformer":
        from funasr.bin.asr_inference_paraformer import inference
        return inference(**kwargs)
    elif mode == "paraformer_vad_punc":
        from funasr.bin.asr_inference_paraformer_vad_punc import inference
        return inference(**kwargs)
    elif mode == "vad":
        from funasr.bin.vad_inference import inference
        return inference(**kwargs)
        from funasr.bin.asr_inference_paraformer import inference_modelscope
        inference_pipeline = inference_modelscope(**kwargs)
        return inference_pipeline(kwargs["data_path_and_name_and_type"], hotword=kwargs.get("hotword", None))
    elif mode.startswith("paraformer_vad"):
        from funasr.bin.asr_inference_paraformer import inference_modelscope_vad_punc
        inference_pipeline = inference_modelscope_vad_punc(**kwargs)
        return inference_pipeline(kwargs["data_path_and_name_and_type"], hotword=kwargs.get("hotword", None))
    elif mode == "mfcca":
        from funasr.bin.asr_inference_mfcca import inference_modelscope
        return inference_modelscope(**kwargs)

 funasr/bin/asr_inference_paraformer.py

@@ -45,7 +45,11 @@
from funasr.export.models.e2e_asr_paraformer import Paraformer as Paraformer_export
from funasr.utils.timestamp_tools import ts_prediction_lfr6_standard
from funasr.bin.tp_inference import SpeechText2Timestamp

from funasr.bin.vad_inference import Speech2VadSegment
from funasr.bin.punctuation_infer import Text2Punc
from funasr.utils.vad_utils import slice_padding_fbank
from funasr.tasks.vad import VADTask
from funasr.utils.timestamp_tools import time_stamp_sentence, ts_prediction_lfr6_standard

class Speech2Text:
    """Speech2Text class
@@ -291,15 +295,14 @@
                    text = self.tokenizer.tokens2text(token)
                else:
                    text = None

                timestamp = []
                if isinstance(self.asr_model, BiCifParaformer):
                    _, timestamp = ts_prediction_lfr6_standard(us_alphas[i], 
                                                            us_peaks[i], 
                    _, timestamp = ts_prediction_lfr6_standard(us_alphas[i][:enc_len[i]*3], 
                                                            us_peaks[i][:enc_len[i]*3], 
                                                            copy.copy(token), 
                                                            vad_offset=begin_time)
                    results.append((text, token, token_int, hyp, timestamp, enc_len_batch_total, lfr_factor))
                else:
                    results.append((text, token, token_int, hyp, enc_len_batch_total, lfr_factor))
                results.append((text, token, token_int, hyp, timestamp, enc_len_batch_total, lfr_factor))


        # assert check_return_type(results)
        return results
@@ -358,226 +361,6 @@
            hotword_list = None
        return hotword_list

class Speech2TextExport:
    """Speech2TextExport class

    """

    def __init__(
            self,
            asr_train_config: Union[Path, str] = None,
            asr_model_file: Union[Path, str] = None,
            cmvn_file: Union[Path, str] = None,
            lm_train_config: Union[Path, str] = None,
            lm_file: Union[Path, str] = None,
            token_type: str = None,
            bpemodel: str = None,
            device: str = "cpu",
            maxlenratio: float = 0.0,
            minlenratio: float = 0.0,
            dtype: str = "float32",
            beam_size: int = 20,
            ctc_weight: float = 0.5,
            lm_weight: float = 1.0,
            ngram_weight: float = 0.9,
            penalty: float = 0.0,
            nbest: int = 1,
            frontend_conf: dict = None,
            hotword_list_or_file: str = None,
            **kwargs,
    ):

        # 1. Build ASR model
        asr_model, asr_train_args = ASRTask.build_model_from_file(
            asr_train_config, asr_model_file, cmvn_file, device
        )
        frontend = None
        if asr_train_args.frontend is not None and asr_train_args.frontend_conf is not None:
            frontend = WavFrontend(cmvn_file=cmvn_file, **asr_train_args.frontend_conf)

        logging.info("asr_model: {}".format(asr_model))
        logging.info("asr_train_args: {}".format(asr_train_args))
        asr_model.to(dtype=getattr(torch, dtype)).eval()

        token_list = asr_model.token_list



        logging.info(f"Decoding device={device}, dtype={dtype}")

        # 5. [Optional] Build Text converter: e.g. bpe-sym -> Text
        if token_type is None:
            token_type = asr_train_args.token_type
        if bpemodel is None:
            bpemodel = asr_train_args.bpemodel

        if token_type is None:
            tokenizer = None
        elif token_type == "bpe":
            if bpemodel is not None:
                tokenizer = build_tokenizer(token_type=token_type, bpemodel=bpemodel)
            else:
                tokenizer = None
        else:
            tokenizer = build_tokenizer(token_type=token_type)
        converter = TokenIDConverter(token_list=token_list)
        logging.info(f"Text tokenizer: {tokenizer}")

        # self.asr_model = asr_model
        self.asr_train_args = asr_train_args
        self.converter = converter
        self.tokenizer = tokenizer

        self.device = device
        self.dtype = dtype
        self.nbest = nbest
        self.frontend = frontend

        model = Paraformer_export(asr_model, onnx=False)
        self.asr_model = model
        
    @torch.no_grad()
    def __call__(
            self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None
    ):
        """Inference

        Args:
                speech: Input speech data
        Returns:
                text, token, token_int, hyp

        """
        assert check_argument_types()

        # Input as audio signal
        if isinstance(speech, np.ndarray):
            speech = torch.tensor(speech)

        if self.frontend is not None:
            feats, feats_len = self.frontend.forward(speech, speech_lengths)
            feats = to_device(feats, device=self.device)
            feats_len = feats_len.int()
            self.asr_model.frontend = None
        else:
            feats = speech
            feats_len = speech_lengths

        enc_len_batch_total = feats_len.sum()
        lfr_factor = max(1, (feats.size()[-1] // 80) - 1)
        batch = {"speech": feats, "speech_lengths": feats_len}

        # a. To device
        batch = to_device(batch, device=self.device)

        decoder_outs = self.asr_model(**batch)
        decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
        
        results = []
        b, n, d = decoder_out.size()
        for i in range(b):
            am_scores = decoder_out[i, :ys_pad_lens[i], :]

            yseq = am_scores.argmax(dim=-1)
            score = am_scores.max(dim=-1)[0]
            score = torch.sum(score, dim=-1)
            # pad with mask tokens to ensure compatibility with sos/eos tokens
            yseq = torch.tensor(
                yseq.tolist(), device=yseq.device
            )
            nbest_hyps = [Hypothesis(yseq=yseq, score=score)]

            for hyp in nbest_hyps:
                assert isinstance(hyp, (Hypothesis)), type(hyp)

                # remove sos/eos and get results
                last_pos = -1
                if isinstance(hyp.yseq, list):
                    token_int = hyp.yseq[1:last_pos]
                else:
                    token_int = hyp.yseq[1:last_pos].tolist()

                # remove blank symbol id, which is assumed to be 0
                token_int = list(filter(lambda x: x != 0 and x != 2, token_int))

                # Change integer-ids to tokens
                token = self.converter.ids2tokens(token_int)

                if self.tokenizer is not None:
                    text = self.tokenizer.tokens2text(token)
                else:
                    text = None

                results.append((text, token, token_int, hyp, enc_len_batch_total, lfr_factor))

        return results


def inference(
        maxlenratio: float,
        minlenratio: float,
        batch_size: int,
        beam_size: int,
        ngpu: int,
        ctc_weight: float,
        lm_weight: float,
        penalty: float,
        log_level: Union[int, str],
        data_path_and_name_and_type,
        asr_train_config: Optional[str],
        asr_model_file: Optional[str],
        cmvn_file: Optional[str] = None,
        raw_inputs: Union[np.ndarray, torch.Tensor] = None,
        lm_train_config: Optional[str] = None,
        lm_file: Optional[str] = None,
        token_type: Optional[str] = None,
        key_file: Optional[str] = None,
        word_lm_train_config: Optional[str] = None,
        bpemodel: Optional[str] = None,
        allow_variable_data_keys: bool = False,
        streaming: bool = False,
        output_dir: Optional[str] = None,
        dtype: str = "float32",
        seed: int = 0,
        ngram_weight: float = 0.9,
        nbest: int = 1,
        num_workers: int = 1,
        timestamp_infer_config: Union[Path, str] = None,
        timestamp_model_file: Union[Path, str] = None,
        **kwargs,
):
    inference_pipeline = inference_modelscope(
        maxlenratio=maxlenratio,
        minlenratio=minlenratio,
        batch_size=batch_size,
        beam_size=beam_size,
        ngpu=ngpu,
        ctc_weight=ctc_weight,
        lm_weight=lm_weight,
        penalty=penalty,
        log_level=log_level,
        asr_train_config=asr_train_config,
        asr_model_file=asr_model_file,
        cmvn_file=cmvn_file,
        raw_inputs=raw_inputs,
        lm_train_config=lm_train_config,
        lm_file=lm_file,
        token_type=token_type,
        key_file=key_file,
        word_lm_train_config=word_lm_train_config,
        bpemodel=bpemodel,
        allow_variable_data_keys=allow_variable_data_keys,
        streaming=streaming,
        output_dir=output_dir,
        dtype=dtype,
        seed=seed,
        ngram_weight=ngram_weight,
        nbest=nbest,
        num_workers=num_workers,

        **kwargs,
    )
    return inference_pipeline(data_path_and_name_and_type, raw_inputs)


def inference_modelscope(
@@ -665,10 +448,8 @@
        nbest=nbest,
        hotword_list_or_file=hotword_list_or_file,
    )
    if export_mode:
        speech2text = Speech2TextExport(**speech2text_kwargs)
    else:
        speech2text = Speech2Text(**speech2text_kwargs)

    speech2text = Speech2Text(**speech2text_kwargs)

    if timestamp_model_file is not None:
        speechtext2timestamp = SpeechText2Timestamp(
@@ -691,7 +472,7 @@
        hotword_list_or_file = None
        if param_dict is not None:
            hotword_list_or_file = param_dict.get('hotword')
        if 'hotword' in kwargs:
        if 'hotword' in kwargs and kwargs['hotword'] is not None:
            hotword_list_or_file = kwargs['hotword']
        if hotword_list_or_file is not None or 'hotword' in kwargs:
            speech2text.hotword_list = speech2text.generate_hotwords_list(hotword_list_or_file)
@@ -762,7 +543,7 @@
                key = keys[batch_id]
                for n, result in zip(range(1, nbest + 1), result):
                    text, token, token_int, hyp = result[0], result[1], result[2], result[3]
                    timestamp = None if len(result) < 5 else result[4]
                    timestamp = result[4] if len(result[4]) > 0 else None
                    # conduct timestamp prediction here
                    # timestamp inference requires token length
                    # thus following inference cannot be conducted in batch
@@ -811,6 +592,257 @@
            ibest_writer["rtf"]["rtf_avf"] = rtf_avg
        return asr_result_list

    return _forward


def inference_modelscope_vad_punc(
    maxlenratio: float,
    minlenratio: float,
    batch_size: int,
    beam_size: int,
    ngpu: int,
    ctc_weight: float,
    lm_weight: float,
    penalty: float,
    log_level: Union[int, str],
    # data_path_and_name_and_type,
    asr_train_config: Optional[str],
    asr_model_file: Optional[str],
    cmvn_file: Optional[str] = None,
    lm_train_config: Optional[str] = None,
    lm_file: Optional[str] = None,
    token_type: Optional[str] = None,
    key_file: Optional[str] = None,
    word_lm_train_config: Optional[str] = None,
    bpemodel: Optional[str] = None,
    allow_variable_data_keys: bool = False,
    output_dir: Optional[str] = None,
    dtype: str = "float32",
    seed: int = 0,
    ngram_weight: float = 0.9,
    nbest: int = 1,
    num_workers: int = 1,
    vad_infer_config: Optional[str] = None,
    vad_model_file: Optional[str] = None,
    vad_cmvn_file: Optional[str] = None,
    time_stamp_writer: bool = True,
    punc_infer_config: Optional[str] = None,
    punc_model_file: Optional[str] = None,
    outputs_dict: Optional[bool] = True,
    param_dict: dict = None,
    **kwargs,
):
    assert check_argument_types()
    ncpu = kwargs.get("ncpu", 1)
    torch.set_num_threads(ncpu)
    
    if word_lm_train_config is not None:
        raise NotImplementedError("Word LM is not implemented")
    if ngpu > 1:
        raise NotImplementedError("only single GPU decoding is supported")
    
    logging.basicConfig(
        level=log_level,
        format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
    )
    
    if param_dict is not None:
        hotword_list_or_file = param_dict.get('hotword')
    else:
        hotword_list_or_file = None
    
    if ngpu >= 1 and torch.cuda.is_available():
        device = "cuda"
    else:
        device = "cpu"
    
    # 1. Set random-seed
    set_all_random_seed(seed)
    
    # 2. Build speech2vadsegment
    speech2vadsegment_kwargs = dict(
        vad_infer_config=vad_infer_config,
        vad_model_file=vad_model_file,
        vad_cmvn_file=vad_cmvn_file,
        device=device,
        dtype=dtype,
    )
    # logging.info("speech2vadsegment_kwargs: {}".format(speech2vadsegment_kwargs))
    speech2vadsegment = Speech2VadSegment(**speech2vadsegment_kwargs)
    
    # 3. Build speech2text
    speech2text_kwargs = dict(
        asr_train_config=asr_train_config,
        asr_model_file=asr_model_file,
        cmvn_file=cmvn_file,
        lm_train_config=lm_train_config,
        lm_file=lm_file,
        token_type=token_type,
        bpemodel=bpemodel,
        device=device,
        maxlenratio=maxlenratio,
        minlenratio=minlenratio,
        dtype=dtype,
        beam_size=beam_size,
        ctc_weight=ctc_weight,
        lm_weight=lm_weight,
        ngram_weight=ngram_weight,
        penalty=penalty,
        nbest=nbest,
        hotword_list_or_file=hotword_list_or_file,
    )
    speech2text = Speech2Text(**speech2text_kwargs)
    text2punc = None
    if punc_model_file is not None:
        text2punc = Text2Punc(punc_infer_config, punc_model_file, device=device, dtype=dtype)
    
    if output_dir is not None:
        writer = DatadirWriter(output_dir)
        ibest_writer = writer[f"1best_recog"]
        ibest_writer["token_list"][""] = " ".join(speech2text.asr_train_args.token_list)
    
    def _forward(data_path_and_name_and_type,
                 raw_inputs: Union[np.ndarray, torch.Tensor] = None,
                 output_dir_v2: Optional[str] = None,
                 fs: dict = None,
                 param_dict: dict = None,
                 **kwargs,
                 ):
        
        hotword_list_or_file = None
        if param_dict is not None:
            hotword_list_or_file = param_dict.get('hotword')
        
        if 'hotword' in kwargs:
            hotword_list_or_file = kwargs['hotword']
        
        if speech2text.hotword_list is None:
            speech2text.hotword_list = speech2text.generate_hotwords_list(hotword_list_or_file)
        
        # 3. Build data-iterator
        if data_path_and_name_and_type is None and raw_inputs is not None:
            if isinstance(raw_inputs, torch.Tensor):
                raw_inputs = raw_inputs.numpy()
            data_path_and_name_and_type = [raw_inputs, "speech", "waveform"]
        loader = ASRTask.build_streaming_iterator(
            data_path_and_name_and_type,
            dtype=dtype,
            fs=fs,
            batch_size=1,
            key_file=key_file,
            num_workers=num_workers,
            preprocess_fn=VADTask.build_preprocess_fn(speech2vadsegment.vad_infer_args, False),
            collate_fn=VADTask.build_collate_fn(speech2vadsegment.vad_infer_args, False),
            allow_variable_data_keys=allow_variable_data_keys,
            inference=True,
        )
        
        if param_dict is not None:
            use_timestamp = param_dict.get('use_timestamp', True)
        else:
            use_timestamp = True
        
        finish_count = 0
        file_count = 1
        lfr_factor = 6
        # 7 .Start for-loop
        asr_result_list = []
        output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
        writer = None
        if output_path is not None:
            writer = DatadirWriter(output_path)
            ibest_writer = writer[f"1best_recog"]
        
        for keys, batch in loader:
            assert isinstance(batch, dict), type(batch)
            assert all(isinstance(s, str) for s in keys), keys
            _bs = len(next(iter(batch.values())))
            assert len(keys) == _bs, f"{len(keys)} != {_bs}"
            
            vad_results = speech2vadsegment(**batch)
            _, vadsegments = vad_results[0], vad_results[1][0]
            
            speech, speech_lengths = batch["speech"], batch["speech_lengths"]
            
            n = len(vadsegments)
            data_with_index = [(vadsegments[i], i) for i in range(n)]
            sorted_data = sorted(data_with_index, key=lambda x: x[0][1] - x[0][0])
            results_sorted = []
            for j, beg_idx in enumerate(range(0, n, batch_size)):
                end_idx = min(n, beg_idx + batch_size)
                speech_j, speech_lengths_j = slice_padding_fbank(speech, speech_lengths, sorted_data[beg_idx:end_idx])
                
                batch = {"speech": speech_j, "speech_lengths": speech_lengths_j}
                batch = to_device(batch, device=device)
                results = speech2text(**batch)
                
                if len(results) < 1:
                    results = [["", [], [], [], [], [], []]]
                results_sorted.extend(results)
            restored_data = [0] * n
            for j in range(n):
                index = sorted_data[j][1]
                restored_data[index] = results_sorted[j]
            result = ["", [], [], [], [], [], []]
            for j in range(n):
                result[0] += restored_data[j][0]
                result[1] += restored_data[j][1]
                result[2] += restored_data[j][2]
                if len(restored_data[j][4]) > 0:
                    for t in restored_data[j][4]:
                        t[0] += vadsegments[j][0]
                        t[1] += vadsegments[j][0]
                    result[4] += restored_data[j][4]
                # result = [result[k]+restored_data[j][k] for k in range(len(result[:-2]))]
            
            key = keys[0]
            # result = result_segments[0]
            text, token, token_int = result[0], result[1], result[2]
            time_stamp = result[4] if len(result[4]) > 0 else None
            
            if use_timestamp and time_stamp is not None:
                postprocessed_result = postprocess_utils.sentence_postprocess(token, time_stamp)
            else:
                postprocessed_result = postprocess_utils.sentence_postprocess(token)
            text_postprocessed = ""
            time_stamp_postprocessed = ""
            text_postprocessed_punc = postprocessed_result
            if len(postprocessed_result) == 3:
                text_postprocessed, time_stamp_postprocessed, word_lists = postprocessed_result[0], \
                                                                           postprocessed_result[1], \
                                                                           postprocessed_result[2]
            else:
                text_postprocessed, word_lists = postprocessed_result[0], postprocessed_result[1]
            
            text_postprocessed_punc = text_postprocessed
            punc_id_list = []
            if len(word_lists) > 0 and text2punc is not None:
                text_postprocessed_punc, punc_id_list = text2punc(word_lists, 20)
            
            item = {'key': key, 'value': text_postprocessed_punc}
            if text_postprocessed != "":
                item['text_postprocessed'] = text_postprocessed
            if time_stamp_postprocessed != "":
                item['time_stamp'] = time_stamp_postprocessed
            
            item['sentences'] = time_stamp_sentence(punc_id_list, time_stamp_postprocessed, text_postprocessed)
            
            asr_result_list.append(item)
            finish_count += 1
            # asr_utils.print_progress(finish_count / file_count)
            if writer is not None:
                # Write the result to each file
                ibest_writer["token"][key] = " ".join(token)
                ibest_writer["token_int"][key] = " ".join(map(str, token_int))
                ibest_writer["vad"][key] = "{}".format(vadsegments)
                ibest_writer["text"][key] = " ".join(word_lists)
                ibest_writer["text_with_punc"][key] = text_postprocessed_punc
                if time_stamp_postprocessed is not None:
                    ibest_writer["time_stamp"][key] = "{}".format(time_stamp_postprocessed)
            
            logging.info("decoding, utt: {}, predictions: {}".format(key, text_postprocessed_punc))
        return asr_result_list
    
    return _forward


@@ -987,18 +1019,9 @@
    kwargs = vars(args)
    kwargs.pop("config", None)
    kwargs['param_dict'] = param_dict
    inference(**kwargs)
    inference_pipeline = inference_modelscope(**kwargs)
    return inference_pipeline(kwargs["data_path_and_name_and_type"], param_dict=param_dict)


if __name__ == "__main__":
    main()

    # from modelscope.pipelines import pipeline
    # from modelscope.utils.constant import Tasks
    #
    # inference_16k_pipline = pipeline(
    #     task=Tasks.auto_speech_recognition,
    #     model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch')
    #
    # rec_result = inference_16k_pipline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav')
    # print(rec_result)

 funasr/bin/asr_inference_paraformer_vad.py

File was deleted

 funasr/bin/asr_inference_paraformer_vad_punc.py

File was deleted

 funasr/bin/asr_inference_uniasr_vad.py

File was deleted

 funasr/utils/vad_utils.py

New file
@@ -0,0 +1,18 @@
import torch
from torch.nn.utils.rnn import pad_sequence

def slice_padding_fbank(speech, speech_lengths, vad_segments):
    speech_list = []
    speech_lengths_list = []
    for i, segment in enumerate(vad_segments):
		
        bed_idx = int(segment[0][0]*16)
        end_idx = min(int(segment[0][1]*16), speech_lengths[0])
        speech_i = speech[0, bed_idx: end_idx]
        speech_lengths_i = end_idx-bed_idx
        speech_list.append(speech_i)
        speech_lengths_list.append(speech_lengths_i)
    feats_pad = pad_sequence(speech_list, batch_first=True, padding_value=0.0)
    speech_lengths_pad = torch.Tensor(speech_lengths_list).int()
    return feats_pad, speech_lengths_pad
	

 funasr/version.txt

@@ -1 +1 @@
0.5.0
0.5.1

 modelscope

New file
@@ -0,0 +1 @@
../MaaS-lib/modelscope