Merge pull request #129 from alibaba-damo-academy/dev_zly

zhifu gao

2023-02-17 00f5ea6244384b2338b99984a55bf3f9e08dcc9c

Merge pull request #129 from alibaba-damo-academy/dev_zly

Dev zly

 egs_modelscope/vad/speech_fsmn_vad_zh-cn-16k-common/README.md

 egs_modelscope/vad/speech_fsmn_vad_zh-cn-16k-common/infer.py

old mode 100755
new mode 100644
File was renamed from egs_modelscope/vad/speech_fsmn_vad_zh-cn-16k-common-pytorch/infer.py
@@ -5,7 +5,7 @@
    audio_in = 'https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav'
    output_dir = None
    inference_pipline = pipeline(
        task=Tasks.auto_speech_recognition,
        task=Tasks.voice_activity_detection,
        model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
        model_revision=None,
        output_dir=output_dir,

 egs_modelscope/vad/speech_fsmn_vad_zh-cn-8k-common/README.md

copy from egs_modelscope/vad/speech_fsmn_vad_zh-cn-16k-common-pytorch/README.md
copy to egs_modelscope/vad/speech_fsmn_vad_zh-cn-8k-common/README.md

 egs_modelscope/vad/speech_fsmn_vad_zh-cn-8k-common/infer.py

old mode 100755
new mode 100644
copy from egs_modelscope/vad/speech_fsmn_vad_zh-cn-16k-common-pytorch/infer.py
copy to egs_modelscope/vad/speech_fsmn_vad_zh-cn-8k-common/infer.py
File was copied from egs_modelscope/vad/speech_fsmn_vad_zh-cn-16k-common-pytorch/infer.py
@@ -2,13 +2,13 @@
from modelscope.utils.constant import Tasks

if __name__ == '__main__':
    audio_in = 'https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav'
    audio_in = 'https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example_8k.wav'
    output_dir = None
    inference_pipline = pipeline(
        task=Tasks.auto_speech_recognition,
        model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
        model_revision=None,
        output_dir=output_dir,
        task=Tasks.voice_activity_detection,
        model="damo/speech_fsmn_vad_zh-cn-8k-common",
        model_revision='v1.1.1',
        output_dir='./output_dir',
        batch_size=1,
    )
    segments_result = inference_pipline(audio_in=audio_in)

 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)
@@ -260,33 +260,36 @@
                    [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:
                    text = None


                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))


        # assert check_return_type(results)
        return results


class Speech2VadSegment:
    """Speech2VadSegment class
@@ -329,6 +332,7 @@
        self.device = device
        self.dtype = dtype
        self.frontend = frontend
        self.batch_size = batch_size

    @torch.no_grad()
    def __call__(
@@ -357,56 +361,69 @@
            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,
@@ -445,63 +462,64 @@
    )
    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,
@@ -512,7 +530,7 @@
    )
    # logging.info("speech2vadsegment_kwargs: {}".format(speech2vadsegment_kwargs))
    speech2vadsegment = Speech2VadSegment(**speech2vadsegment_kwargs)
    

    # 3. Build speech2text
    speech2text_kwargs = dict(
        asr_train_config=asr_train_config,
@@ -535,14 +553,14 @@
    )
    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,
@@ -571,7 +589,7 @@
            use_timestamp = param_dict.get('use_timestamp', True)
        else:
            use_timestamp = True
    

        finish_count = 0
        file_count = 1
        lfr_factor = 6
@@ -582,13 +600,13 @@
        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):
@@ -602,17 +620,19 @@
                    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: 
                    postprocessed_result = postprocess_utils.sentence_postprocess(token, time_stamp)
@@ -631,13 +651,13 @@
                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)
@@ -650,11 +670,13 @@
                    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

@@ -81,6 +81,7 @@
        self.device = device
        self.dtype = dtype
        self.frontend = frontend
        self.batch_size = batch_size

    @torch.no_grad()
    def __call__(
@@ -106,13 +107,11 @@
            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
        segments = []
        step = 6000
        # 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
@@ -128,9 +127,8 @@
            batch = to_device(batch, device=self.device)
            segments_part = self.vad_model(**batch)
            if segments_part:
                segments += segments_part
        #print(segments)

                for batch_num in range(0, self.batch_size):
                    segments[batch_num] += segments_part[batch_num]
        return segments


@@ -254,7 +252,6 @@
            assert all(isinstance(s, str) for s in keys), keys
            _bs = len(next(iter(batch.values())))
            assert len(keys) == _bs, f"{len(keys)} != {_bs}"
            # batch = {k: v[0] for k, v in batch.items() if not k.endswith("_lengths")}

            # do vad segment
            results = speech2vadsegment(**batch)

 funasr/models/e2e_vad.py

@@ -192,7 +192,7 @@


class E2EVadModel(nn.Module):
    def __init__(self, encoder: FSMN, vad_post_args: Dict[str, Any], streaming=False):
    def __init__(self, encoder: FSMN, vad_post_args: Dict[str, Any]):
        super(E2EVadModel, self).__init__()
        self.vad_opts = VADXOptions(**vad_post_args)
        self.windows_detector = WindowDetector(self.vad_opts.window_size_ms,
@@ -227,7 +227,6 @@
        self.data_buf = None
        self.data_buf_all = None
        self.waveform = None
        self.streaming = streaming
        self.ResetDetection()

    def AllResetDetection(self):
@@ -451,11 +450,7 @@
        if not is_final_send:
            self.DetectCommonFrames()
        else:
            if self.streaming:
                self.DetectLastFrames()
            else:
                self.AllResetDetection()
                self.DetectAllFrames()  # offline decode and is_final_send == True
            self.DetectLastFrames()
        segments = []
        for batch_num in range(0, feats.shape[0]):  # only support batch_size = 1 now
            segment_batch = []
@@ -468,7 +463,8 @@
                        self.output_data_buf_offset += 1  # need update this parameter
            if segment_batch:
                segments.append(segment_batch)

        if is_final_send:
            self.AllResetDetection() 
        return segments

    def DetectCommonFrames(self) -> int:
@@ -492,18 +488,6 @@
            else:
                self.DetectOneFrame(frame_state, self.frm_cnt - 1, True)

        return 0

    def DetectAllFrames(self) -> int:
        if self.vad_state_machine == VadStateMachine.kVadInStateEndPointDetected:
            return 0
        if self.vad_opts.nn_eval_block_size != self.vad_opts.dcd_block_size:
            frame_state = FrameState.kFrameStateInvalid
            for t in range(0, self.frm_cnt):
                frame_state = self.GetFrameState(t)
                self.DetectOneFrame(frame_state, t, t == self.frm_cnt - 1)
        else:
            pass
        return 0

    def DetectOneFrame(self, cur_frm_state: FrameState, cur_frm_idx: int, is_final_frame: bool) -> None:

 funasr/tasks/vad.py

@@ -291,8 +291,7 @@
            model_class = model_choices.get_class(args.model)
        except AttributeError:
            model_class = model_choices.get_class("e2evad")
        model = model_class(encoder=encoder, vad_post_args=args.vad_post_conf,
                            streaming=args.encoder_conf.get('streaming', False))
        model = model_class(encoder=encoder, vad_post_args=args.vad_post_conf)

        return model