support asr_inference_paraformer_vad_punc

凌匀

2023-02-16 8689fb676d6cd28894f55c8cac43409e7bf4cd38

support asr_inference_paraformer_vad_punc

 funasr/bin/asr_inference_paraformer_vad_punc.py

@@ -144,7 +144,7 @@
        for scorer in scorers.values():
            if isinstance(scorer, torch.nn.Module):
                scorer.to(device=device, dtype=getattr(torch, dtype)).eval()
        

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

        # 5. [Optional] Build Text converter: e.g. bpe-sym -> Text
@@ -184,12 +184,11 @@
        self.encoder_downsampling_factor = 1
        if asr_train_args.encoder_conf["input_layer"] == "conv2d":
            self.encoder_downsampling_factor = 4
        
            

    @torch.no_grad()
    def __call__(
            self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None, begin_time: int = 0, end_time: int = None, 
            self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None,
            begin_time: int = 0, end_time: int = None,
    ):
        """Inference

@@ -215,7 +214,7 @@
        else:
            feats = speech
            feats_len = speech_lengths
        lfr_factor = max(1, (feats.size()[-1]//80)-1)
        lfr_factor = max(1, (feats.size()[-1] // 80) - 1)
        batch = {"speech": feats, "speech_lengths": feats_len}

        # a. To device
@@ -229,7 +228,8 @@
        enc_len_batch_total = torch.sum(enc_len).item() * self.encoder_downsampling_factor

        predictor_outs = self.asr_model.calc_predictor(enc, enc_len)
        pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = predictor_outs[0], predictor_outs[1], predictor_outs[2], predictor_outs[3]
        pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = predictor_outs[0], predictor_outs[1], \
                                                                        predictor_outs[2], predictor_outs[3]
        pre_token_length = pre_token_length.round().long()
        if torch.max(pre_token_length) < 1:
            return []
@@ -249,7 +249,7 @@
                nbest_hyps = self.beam_search(
                    x=x, am_scores=am_scores, maxlenratio=self.maxlenratio, minlenratio=self.minlenratio
                )
    

                nbest_hyps = nbest_hyps[: self.nbest]
            else:
                yseq = am_scores.argmax(dim=-1)
@@ -260,23 +260,23 @@
                    [self.asr_model.sos] + yseq.tolist() + [self.asr_model.eos], 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:
@@ -286,11 +286,13 @@
                    timestamp = time_stamp_lfr6_pl(us_alphas[i], us_cif_peak[i], copy.copy(token), begin_time, end_time)
                    results.append((text, token, token_int, timestamp, enc_len_batch_total, lfr_factor))
                else:
                    time_stamp = time_stamp_lfr6(alphas[i:i + 1, ], enc_len[i:i + 1, ], copy.copy(token), begin_time, end_time)
                    time_stamp = time_stamp_lfr6(alphas[i:i + 1, ], enc_len[i:i + 1, ], copy.copy(token), begin_time,
                                                 end_time)
                    results.append((text, token, token_int, time_stamp, enc_len_batch_total, lfr_factor))

        # assert check_return_type(results)
        return results


class Speech2VadSegment:
    """Speech2VadSegment class
@@ -333,6 +335,7 @@
        self.device = device
        self.dtype = dtype
        self.frontend = frontend
        self.batch_size = batch_size

    @torch.no_grad()
    def __call__(
@@ -361,56 +364,69 @@
            feats_len = feats_len.int()
        else:
            raise Exception("Need to extract feats first, please configure frontend configuration")
        batch = {"feats": feats, "feats_lengths": feats_len, "waveform": speech}

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

        # b. Forward Encoder
        segments = self.vad_model(**batch)
        # b. Forward Encoder streaming
        t_offset = 0
        step = min(feats_len, 6000)
        segments = [[]] * self.batch_size
        for t_offset in range(0, feats_len, min(step, feats_len - t_offset)):
            if t_offset + step >= feats_len - 1:
                step = feats_len - t_offset
                is_final_send = True
            else:
                is_final_send = False
            batch = {
                "feats": feats[:, t_offset:t_offset + step, :],
                "waveform": speech[:, t_offset * 160:min(speech.shape[-1], (t_offset + step - 1) * 160 + 400)],
                "is_final_send": is_final_send
            }
            # a. To device
            batch = to_device(batch, device=self.device)
            segments_part = self.vad_model(**batch)
            if segments_part:
                for batch_num in range(0, self.batch_size):
                    segments[batch_num] += segments_part[batch_num]

        return fbanks, segments



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,
    vad_infer_config: Optional[str] = None,
    vad_model_file: Optional[str] = None,
    vad_cmvn_file: Optional[str] = None,
    time_stamp_writer: bool = False,
    punc_infer_config: Optional[str] = None,
    punc_model_file: Optional[str] = None,
    **kwargs,
        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,
        vad_infer_config: Optional[str] = None,
        vad_model_file: Optional[str] = None,
        vad_cmvn_file: Optional[str] = None,
        time_stamp_writer: bool = False,
        punc_infer_config: Optional[str] = None,
        punc_model_file: Optional[str] = None,
        **kwargs,
):

    inference_pipeline = inference_modelscope(
        maxlenratio=maxlenratio,
        minlenratio=minlenratio,
@@ -449,63 +465,64 @@
    )
    return inference_pipeline(data_path_and_name_and_type, raw_inputs)


def inference_modelscope(
    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,
        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()
    

    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 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,
@@ -516,7 +533,7 @@
    )
    # logging.info("speech2vadsegment_kwargs: {}".format(speech2vadsegment_kwargs))
    speech2vadsegment = Speech2VadSegment(**speech2vadsegment_kwargs)
    

    # 3. Build speech2text
    speech2text_kwargs = dict(
        asr_train_config=asr_train_config,
@@ -539,14 +556,14 @@
    )
    speech2text = Speech2Text(**speech2text_kwargs)
    text2punc = None
    if punc_model_file is not 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,
@@ -575,7 +592,7 @@
            use_timestamp = param_dict.get('use_timestamp', True)
        else:
            use_timestamp = True
    

        finish_count = 0
        file_count = 1
        lfr_factor = 6
@@ -586,13 +603,13 @@
        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)
            fbanks, vadsegments = vad_results[0], vad_results[1]
            for i, segments in enumerate(vadsegments):
@@ -606,19 +623,20 @@
                    results = speech2text(**batch)
                    if len(results) < 1:
                        continue
    

                    result_cur = [results[0][:-2]]
                    if j == 0:
                        result_segments = result_cur
                    else:
                        result_segments = [[result_segments[0][i] + result_cur[0][i] for i in range(len(result_cur[0]))]]
    
                        result_segments = [
                            [result_segments[0][i] + result_cur[0][i] for i in range(len(result_cur[0]))]]

                key = keys[0]
                result = result_segments[0]
                text, token, token_int = result[0], result[1], result[2]
                time_stamp = None if len(result) < 4 else result[3]
   
                if use_timestamp and time_stamp is not 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)
@@ -635,13 +653,13 @@
                text_postprocessed_punc = text_postprocessed
                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
    

                asr_result_list.append(item)
                finish_count += 1
                # asr_utils.print_progress(finish_count / file_count)
@@ -654,11 +672,13 @@
                    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


def get_parser():
    parser = config_argparse.ArgumentParser(
        description="ASR Decoding",

 funasr/bin/vad_inference.py

@@ -107,10 +107,8 @@
            feats_len = feats_len.int()
        else:
            raise Exception("Need to extract feats first, please configure frontend configuration")
        # batch = {"feats": feats, "waveform": speech, "is_final_send": True}
        # segments = self.vad_model(**batch)

        # b. Forward Encoder sreaming
        # b. Forward Encoder streaming
        t_offset = 0
        step = min(feats_len, 6000)
        segments = [[]] * self.batch_size

 vad_inference.py

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