update

Dockerfile

@@ -0,0 +1,4 @@
+FROM nvcr.io/nvidia/pytorch:22.12-py3
+COPY ./ /workspace/
+WORKDIR /workspace/
+RUN pip3 install -r requirements.txt

README.md

@@ -0,0 +1,22 @@
+
+### Usage
+
+```
+apt-get install git-lfs
+git lfs install
+git clone https://huggingface.co/yuekai/paraformerX
+cd paraformerX
+
+pip install -r ./requirements.txt
+
+python3 transcribe.py --wav-path test_wavs/vad_example.wav
+```
+
+
+### Using Docker
+```
+docker build -f Dockerfile -t soar97/torch-paraformer:22.12 .
+# docker pull soar97/torch-paraformer:22.12
+
+docker run -it --name "paraformerX" --net host soar97/torch-paraformer:22.12
+```

funasr_onnx/__init__.py

@@ -0,0 +1,5 @@
+# -*- encoding: utf-8 -*-
+from .paraformer_bin import Paraformer
+from .vad_bin import Fsmn_vad
+from .punc_bin import CT_Transformer
+from .punc_bin import CT_Transformer_VadRealtime

funasr_onnx/paraformer_bin.py

@@ -0,0 +1,190 @@
+# -*- encoding: utf-8 -*-
+
+import os.path
+from pathlib import Path
+from typing import List, Union, Tuple
+
+import copy
+import librosa
+import numpy as np
+
+from .utils.utils import (CharTokenizer, Hypothesis, ONNXRuntimeError,
+                          OrtInferSession, TokenIDConverter, get_logger,
+                          read_yaml)
+from .utils.postprocess_utils import sentence_postprocess
+from .utils.frontend import WavFrontend
+from .utils.timestamp_utils import time_stamp_lfr6_onnx
+
+logging = get_logger()
+
+
+class Paraformer():
+    def __init__(self, model_dir: Union[str, Path] = None,
+                 batch_size: int = 1,
+                 device_id: Union[str, int] = "-1",
+                 plot_timestamp_to: str = "",
+                 pred_bias: int = 1,
+                 quantize: bool = False,
+                 intra_op_num_threads: int = 4,
+                 ):
+
+        if not Path(model_dir).exists():
+            raise FileNotFoundError(f'{model_dir} does not exist.')
+
+        model_file = os.path.join(model_dir, 'model.onnx')
+        if quantize:
+            model_file = os.path.join(model_dir, 'model_quant.onnx')
+        config_file = os.path.join(model_dir, 'config.yaml')
+        cmvn_file = os.path.join(model_dir, 'am.mvn')
+        config = read_yaml(config_file)
+
+        self.converter = TokenIDConverter(config['token_list'])
+        self.tokenizer = CharTokenizer()
+        self.frontend = WavFrontend(
+            cmvn_file=cmvn_file,
+            **config['frontend_conf']
+        )
+        self.ort_infer = OrtInferSession(model_file, device_id, intra_op_num_threads=intra_op_num_threads)
+        self.batch_size = batch_size
+        self.plot_timestamp_to = plot_timestamp_to
+        self.pred_bias = pred_bias
+
+    def __call__(self, wav_content: Union[str, np.ndarray, List[str]], **kwargs) -> List:
+        waveform_list = self.load_data(wav_content, self.frontend.opts.frame_opts.samp_freq)
+        waveform_nums = len(waveform_list)
+        asr_res = []
+        for beg_idx in range(0, waveform_nums, self.batch_size):
+            
+            end_idx = min(waveform_nums, beg_idx + self.batch_size)
+            feats, feats_len = self.extract_feat(waveform_list[beg_idx:end_idx])
+            try:
+                outputs = self.infer(feats, feats_len)
+                am_scores, valid_token_lens = outputs[0], outputs[1]
+                if len(outputs) == 4:
+                    # for BiCifParaformer Inference
+                    us_alphas, us_peaks = outputs[2], outputs[3]
+                else:
+                    us_alphas, us_peaks = None, None
+            except ONNXRuntimeError:
+                #logging.warning(traceback.format_exc())
+                logging.warning("input wav is silence or noise")
+                preds = ['']
+            else:
+                preds = self.decode(am_scores, valid_token_lens)
+                if us_peaks is None:
+                    for pred in preds:
+                        pred = sentence_postprocess(pred)
+                        asr_res.append({'preds': pred})
+                else:
+                    for pred, us_peaks_ in zip(preds, us_peaks):
+                        raw_tokens = pred
+                        timestamp, timestamp_raw = time_stamp_lfr6_onnx(us_peaks_, copy.copy(raw_tokens))
+                        text_proc, timestamp_proc, _ = sentence_postprocess(raw_tokens, timestamp_raw)
+                        # logging.warning(timestamp)
+                        if len(self.plot_timestamp_to):
+                            self.plot_wave_timestamp(waveform_list[0], timestamp, self.plot_timestamp_to)
+                        asr_res.append({'preds': text_proc, 'timestamp': timestamp_proc, "raw_tokens": raw_tokens})
+        return asr_res
+
+    def plot_wave_timestamp(self, wav, text_timestamp, dest):
+        # TODO: Plot the wav and timestamp results with matplotlib
+        import matplotlib
+        matplotlib.use('Agg')
+        matplotlib.rc("font", family='Alibaba PuHuiTi')  # set it to a font that your system supports
+        import matplotlib.pyplot as plt
+        fig, ax1 = plt.subplots(figsize=(11, 3.5), dpi=320)
+        ax2 = ax1.twinx()
+        ax2.set_ylim([0, 2.0])
+        # plot waveform
+        ax1.set_ylim([-0.3, 0.3])
+        time = np.arange(wav.shape[0]) / 16000
+        ax1.plot(time, wav/wav.max()*0.3, color='gray', alpha=0.4)
+        # plot lines and text
+        for (char, start, end) in text_timestamp:
+            ax1.vlines(start, -0.3, 0.3, ls='--')
+            ax1.vlines(end, -0.3, 0.3, ls='--')
+            x_adj = 0.045 if char != '<sil>' else 0.12
+            ax1.text((start + end) * 0.5 - x_adj, 0, char)
+        # plt.legend()
+        plotname = "{}/timestamp.png".format(dest)
+        plt.savefig(plotname, bbox_inches='tight')
+
+    def load_data(self,
+                  wav_content: Union[str, np.ndarray, List[str]], fs: int = None) -> List:
+        def load_wav(path: str) -> np.ndarray:
+            waveform, _ = librosa.load(path, sr=fs)
+            return waveform
+
+        if isinstance(wav_content, np.ndarray):
+            return [wav_content]
+
+        if isinstance(wav_content, str):
+            return [load_wav(wav_content)]
+
+        if isinstance(wav_content, list):
+            if isinstance(wav_content[0], np.ndarray):
+                return wav_content
+            else:
+                return [load_wav(path) for path in wav_content]
+
+        raise TypeError(
+            f'The type of {wav_content} is not in [str, np.ndarray, list]')
+
+    def extract_feat(self,
+                     waveform_list: List[np.ndarray]
+                     ) -> Tuple[np.ndarray, np.ndarray]:
+        feats, feats_len = [], []
+        for waveform in waveform_list:
+            speech, _ = self.frontend.fbank(waveform)
+            feat, feat_len = self.frontend.lfr_cmvn(speech)
+            feats.append(feat)
+            feats_len.append(feat_len)
+
+        feats = self.pad_feats(feats, np.max(feats_len))
+        feats_len = np.array(feats_len).astype(np.int32)
+        return feats, feats_len
+
+    @staticmethod
+    def pad_feats(feats: List[np.ndarray], max_feat_len: int) -> np.ndarray:
+        def pad_feat(feat: np.ndarray, cur_len: int) -> np.ndarray:
+            pad_width = ((0, max_feat_len - cur_len), (0, 0))
+            return np.pad(feat, pad_width, 'constant', constant_values=0)
+
+        feat_res = [pad_feat(feat, feat.shape[0]) for feat in feats]
+        feats = np.array(feat_res).astype(np.float32)
+        return feats
+
+    def infer(self, feats: np.ndarray,
+              feats_len: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+        outputs = self.ort_infer([feats, feats_len])
+        return outputs
+
+    def decode(self, am_scores: np.ndarray, token_nums: int) -> List[str]:
+        return [self.decode_one(am_score, token_num)
+                for am_score, token_num in zip(am_scores, token_nums)]
+
+    def decode_one(self,
+                   am_score: np.ndarray,
+                   valid_token_num: int) -> List[str]:
+        yseq = am_score.argmax(axis=-1)
+        score = am_score.max(axis=-1)
+        score = np.sum(score, axis=-1)
+
+        # pad with mask tokens to ensure compatibility with sos/eos tokens
+        # asr_model.sos:1  asr_model.eos:2
+        yseq = np.array([1] + yseq.tolist() + [2])
+        hyp = Hypothesis(yseq=yseq, score=score)
+
+        # remove sos/eos and get results
+        last_pos = -1
+        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 not in (0, 2), token_int))
+
+        # Change integer-ids to tokens
+        token = self.converter.ids2tokens(token_int)
+        token = token[:valid_token_num-self.pred_bias]
+        # texts = sentence_postprocess(token)
+        return token
+

funasr_onnx/punc_bin.py

@@ -0,0 +1,249 @@
+# -*- encoding: utf-8 -*-
+
+import os.path
+from pathlib import Path
+from typing import List, Union, Tuple
+import numpy as np
+
+from .utils.utils import (ONNXRuntimeError,
+                          OrtInferSession, get_logger,
+                          read_yaml)
+from .utils.utils import (TokenIDConverter, split_to_mini_sentence,code_mix_split_words)
+logging = get_logger()
+
+
+class CT_Transformer():
+    def __init__(self, model_dir: Union[str, Path] = None,
+                 batch_size: int = 1,
+                 device_id: Union[str, int] = "-1",
+                 quantize: bool = False,
+                 intra_op_num_threads: int = 4
+                 ):
+
+        if not Path(model_dir).exists():
+            raise FileNotFoundError(f'{model_dir} does not exist.')
+
+        model_file = os.path.join(model_dir, 'model.onnx')
+        if quantize:
+            model_file = os.path.join(model_dir, 'model_quant.onnx')
+        config_file = os.path.join(model_dir, 'punc.yaml')
+        config = read_yaml(config_file)
+
+        self.converter = TokenIDConverter(config['token_list'])
+        self.ort_infer = OrtInferSession(model_file, device_id, intra_op_num_threads=intra_op_num_threads)
+        self.batch_size = 1
+        self.punc_list = config['punc_list']
+        self.period = 0
+        for i in range(len(self.punc_list)):
+            if self.punc_list[i] == ",":
+                self.punc_list[i] = "，"
+            elif self.punc_list[i] == "?":
+                self.punc_list[i] = "？"
+            elif self.punc_list[i] == "。":
+                self.period = i
+
+    def __call__(self, text: Union[list, str], split_size=20):
+        split_text = code_mix_split_words(text)
+        split_text_id = self.converter.tokens2ids(split_text)
+        mini_sentences = split_to_mini_sentence(split_text, split_size)
+        mini_sentences_id = split_to_mini_sentence(split_text_id, split_size)
+        assert len(mini_sentences) == len(mini_sentences_id)
+        cache_sent = []
+        cache_sent_id = []
+        new_mini_sentence = ""
+        new_mini_sentence_punc = []
+        cache_pop_trigger_limit = 200
+        for mini_sentence_i in range(len(mini_sentences)):
+            mini_sentence = mini_sentences[mini_sentence_i]
+            mini_sentence_id = mini_sentences_id[mini_sentence_i]
+            mini_sentence = cache_sent + mini_sentence
+            mini_sentence_id = np.array(cache_sent_id + mini_sentence_id, dtype='int64')
+            data = {
+                "text": mini_sentence_id[None,:],
+                "text_lengths": np.array([len(mini_sentence_id)], dtype='int32'),
+            }
+            try:
+                outputs = self.infer(data['text'], data['text_lengths'])
+                y = outputs[0]
+                punctuations = np.argmax(y,axis=-1)[0]
+                assert punctuations.size == len(mini_sentence)
+            except ONNXRuntimeError:
+                logging.warning("error")
+
+            # Search for the last Period/QuestionMark as cache
+            if mini_sentence_i < len(mini_sentences) - 1:
+                sentenceEnd = -1
+                last_comma_index = -1
+                for i in range(len(punctuations) - 2, 1, -1):
+                    if self.punc_list[punctuations[i]] == "。" or self.punc_list[punctuations[i]] == "？":
+                        sentenceEnd = i
+                        break
+                    if last_comma_index < 0 and self.punc_list[punctuations[i]] == "，":
+                        last_comma_index = i
+
+                if sentenceEnd < 0 and len(mini_sentence) > cache_pop_trigger_limit and last_comma_index >= 0:
+                    # The sentence it too long, cut off at a comma.
+                    sentenceEnd = last_comma_index
+                    punctuations[sentenceEnd] = self.period
+                cache_sent = mini_sentence[sentenceEnd + 1:]
+                cache_sent_id = mini_sentence_id[sentenceEnd + 1:].tolist()
+                mini_sentence = mini_sentence[0:sentenceEnd + 1]
+                punctuations = punctuations[0:sentenceEnd + 1]
+
+            new_mini_sentence_punc += [int(x) for x in punctuations]
+            words_with_punc = []
+            for i in range(len(mini_sentence)):
+                if i > 0:
+                    if len(mini_sentence[i][0].encode()) == 1 and len(mini_sentence[i - 1][0].encode()) == 1:
+                        mini_sentence[i] = " " + mini_sentence[i]
+                words_with_punc.append(mini_sentence[i])
+                if self.punc_list[punctuations[i]] != "_":
+                    words_with_punc.append(self.punc_list[punctuations[i]])
+            new_mini_sentence += "".join(words_with_punc)
+            # Add Period for the end of the sentence
+            new_mini_sentence_out = new_mini_sentence
+            new_mini_sentence_punc_out = new_mini_sentence_punc
+            if mini_sentence_i == len(mini_sentences) - 1:
+                if new_mini_sentence[-1] == "，" or new_mini_sentence[-1] == "、":
+                    new_mini_sentence_out = new_mini_sentence[:-1] + "。"
+                    new_mini_sentence_punc_out = new_mini_sentence_punc[:-1] + [self.period]
+                elif new_mini_sentence[-1] != "。" and new_mini_sentence[-1] != "？":
+                    new_mini_sentence_out = new_mini_sentence + "。"
+                    new_mini_sentence_punc_out = new_mini_sentence_punc[:-1] + [self.period]
+        return new_mini_sentence_out, new_mini_sentence_punc_out
+
+    def infer(self, feats: np.ndarray,
+              feats_len: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+        outputs = self.ort_infer([feats, feats_len])
+        return outputs
+
+
+class CT_Transformer_VadRealtime(CT_Transformer):
+    def __init__(self, model_dir: Union[str, Path] = None,
+                 batch_size: int = 1,
+                 device_id: Union[str, int] = "-1",
+                 quantize: bool = False,
+                 intra_op_num_threads: int = 4
+                 ):
+        super(CT_Transformer_VadRealtime, self).__init__(model_dir, batch_size, device_id, quantize, intra_op_num_threads)
+
+    def __call__(self, text: str, param_dict: map, split_size=20):
+        cache_key = "cache"
+        assert cache_key in param_dict
+        cache = param_dict[cache_key]
+        if cache is not None and len(cache) > 0:
+            precache = "".join(cache)
+        else:
+            precache = ""
+            cache = []
+        full_text = precache + text
+        split_text = code_mix_split_words(full_text)
+        split_text_id = self.converter.tokens2ids(split_text)
+        mini_sentences = split_to_mini_sentence(split_text, split_size)
+        mini_sentences_id = split_to_mini_sentence(split_text_id, split_size)
+        new_mini_sentence_punc = []
+        assert len(mini_sentences) == len(mini_sentences_id)
+
+        cache_sent = []
+        cache_sent_id = np.array([], dtype='int32')
+        sentence_punc_list = []
+        sentence_words_list = []
+        cache_pop_trigger_limit = 200
+        skip_num = 0
+        for mini_sentence_i in range(len(mini_sentences)):
+            mini_sentence = mini_sentences[mini_sentence_i]
+            mini_sentence_id = mini_sentences_id[mini_sentence_i]
+            mini_sentence = cache_sent + mini_sentence
+            mini_sentence_id = np.concatenate((cache_sent_id, mini_sentence_id), axis=0)
+            text_length = len(mini_sentence_id)
+            data = {
+                "input": mini_sentence_id[None,:],
+                "text_lengths": np.array([text_length], dtype='int32'),
+                "vad_mask": self.vad_mask(text_length, len(cache) - 1)[None, None, :, :].astype(np.float32),
+                "sub_masks": np.tril(np.ones((text_length, text_length), dtype=np.float32))[None, None, :, :].astype(np.float32)
+            }
+            try:
+                outputs = self.infer(data['input'], data['text_lengths'], data['vad_mask'], data["sub_masks"])
+                y = outputs[0]
+                punctuations = np.argmax(y,axis=-1)[0]
+                assert punctuations.size == len(mini_sentence)
+            except ONNXRuntimeError:
+                logging.warning("error")
+
+            # Search for the last Period/QuestionMark as cache
+            if mini_sentence_i < len(mini_sentences) - 1:
+                sentenceEnd = -1
+                last_comma_index = -1
+                for i in range(len(punctuations) - 2, 1, -1):
+                    if self.punc_list[punctuations[i]] == "。" or self.punc_list[punctuations[i]] == "？":
+                        sentenceEnd = i
+                        break
+                    if last_comma_index < 0 and self.punc_list[punctuations[i]] == "，":
+                        last_comma_index = i
+
+                if sentenceEnd < 0 and len(mini_sentence) > cache_pop_trigger_limit and last_comma_index >= 0:
+                    # The sentence it too long, cut off at a comma.
+                    sentenceEnd = last_comma_index
+                    punctuations[sentenceEnd] = self.period
+                cache_sent = mini_sentence[sentenceEnd + 1:]
+                cache_sent_id = mini_sentence_id[sentenceEnd + 1:]
+                mini_sentence = mini_sentence[0:sentenceEnd + 1]
+                punctuations = punctuations[0:sentenceEnd + 1]
+
+            punctuations_np = [int(x) for x in punctuations]
+            new_mini_sentence_punc += punctuations_np
+            sentence_punc_list += [self.punc_list[int(x)] for x in punctuations_np]
+            sentence_words_list += mini_sentence
+
+        assert len(sentence_punc_list) == len(sentence_words_list)
+        words_with_punc = []
+        sentence_punc_list_out = []
+        for i in range(0, len(sentence_words_list)):
+            if i > 0:
+                if len(sentence_words_list[i][0].encode()) == 1 and len(sentence_words_list[i - 1][-1].encode()) == 1:
+                    sentence_words_list[i] = " " + sentence_words_list[i]
+            if skip_num < len(cache):
+                skip_num += 1
+            else:
+                words_with_punc.append(sentence_words_list[i])
+            if skip_num >= len(cache):
+                sentence_punc_list_out.append(sentence_punc_list[i])
+                if sentence_punc_list[i] != "_":
+                    words_with_punc.append(sentence_punc_list[i])
+        sentence_out = "".join(words_with_punc)
+
+        sentenceEnd = -1
+        for i in range(len(sentence_punc_list) - 2, 1, -1):
+            if sentence_punc_list[i] == "。" or sentence_punc_list[i] == "？":
+                sentenceEnd = i
+                break
+        cache_out = sentence_words_list[sentenceEnd + 1:]
+        if sentence_out[-1] in self.punc_list:
+            sentence_out = sentence_out[:-1]
+            sentence_punc_list_out[-1] = "_"
+        param_dict[cache_key] = cache_out
+        return sentence_out, sentence_punc_list_out, cache_out
+
+    def vad_mask(self, size, vad_pos, dtype=np.bool):
+        """Create mask for decoder self-attention.
+
+        :param int size: size of mask
+        :param int vad_pos: index of vad index
+        :param torch.dtype dtype: result dtype
+        :rtype: torch.Tensor (B, Lmax, Lmax)
+        """
+        ret = np.ones((size, size), dtype=dtype)
+        if vad_pos <= 0 or vad_pos >= size:
+            return ret
+        sub_corner = np.zeros(
+            (vad_pos - 1, size - vad_pos), dtype=dtype)
+        ret[0:vad_pos - 1, vad_pos:] = sub_corner
+        return ret
+
+    def infer(self, feats: np.ndarray,
+              feats_len: np.ndarray,
+              vad_mask: np.ndarray,
+              sub_masks: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+        outputs = self.ort_infer([feats, feats_len, vad_mask, sub_masks])
+        return outputs
+

funasr_onnx/utils/e2e_vad.py

@@ -0,0 +1,607 @@
+from enum import Enum
+from typing import List, Tuple, Dict, Any
+
+import math
+import numpy as np
+
+class VadStateMachine(Enum):
+    kVadInStateStartPointNotDetected = 1
+    kVadInStateInSpeechSegment = 2
+    kVadInStateEndPointDetected = 3
+
+
+class FrameState(Enum):
+    kFrameStateInvalid = -1
+    kFrameStateSpeech = 1
+    kFrameStateSil = 0
+
+
+# final voice/unvoice state per frame
+class AudioChangeState(Enum):
+    kChangeStateSpeech2Speech = 0
+    kChangeStateSpeech2Sil = 1
+    kChangeStateSil2Sil = 2
+    kChangeStateSil2Speech = 3
+    kChangeStateNoBegin = 4
+    kChangeStateInvalid = 5
+
+
+class VadDetectMode(Enum):
+    kVadSingleUtteranceDetectMode = 0
+    kVadMutipleUtteranceDetectMode = 1
+
+
+class VADXOptions:
+    def __init__(
+            self,
+            sample_rate: int = 16000,
+            detect_mode: int = VadDetectMode.kVadMutipleUtteranceDetectMode.value,
+            snr_mode: int = 0,
+            max_end_silence_time: int = 800,
+            max_start_silence_time: int = 3000,
+            do_start_point_detection: bool = True,
+            do_end_point_detection: bool = True,
+            window_size_ms: int = 200,
+            sil_to_speech_time_thres: int = 150,
+            speech_to_sil_time_thres: int = 150,
+            speech_2_noise_ratio: float = 1.0,
+            do_extend: int = 1,
+            lookback_time_start_point: int = 200,
+            lookahead_time_end_point: int = 100,
+            max_single_segment_time: int = 60000,
+            nn_eval_block_size: int = 8,
+            dcd_block_size: int = 4,
+            snr_thres: int = -100.0,
+            noise_frame_num_used_for_snr: int = 100,
+            decibel_thres: int = -100.0,
+            speech_noise_thres: float = 0.6,
+            fe_prior_thres: float = 1e-4,
+            silence_pdf_num: int = 1,
+            sil_pdf_ids: List[int] = [0],
+            speech_noise_thresh_low: float = -0.1,
+            speech_noise_thresh_high: float = 0.3,
+            output_frame_probs: bool = False,
+            frame_in_ms: int = 10,
+            frame_length_ms: int = 25,
+    ):
+        self.sample_rate = sample_rate
+        self.detect_mode = detect_mode
+        self.snr_mode = snr_mode
+        self.max_end_silence_time = max_end_silence_time
+        self.max_start_silence_time = max_start_silence_time
+        self.do_start_point_detection = do_start_point_detection
+        self.do_end_point_detection = do_end_point_detection
+        self.window_size_ms = window_size_ms
+        self.sil_to_speech_time_thres = sil_to_speech_time_thres
+        self.speech_to_sil_time_thres = speech_to_sil_time_thres
+        self.speech_2_noise_ratio = speech_2_noise_ratio
+        self.do_extend = do_extend
+        self.lookback_time_start_point = lookback_time_start_point
+        self.lookahead_time_end_point = lookahead_time_end_point
+        self.max_single_segment_time = max_single_segment_time
+        self.nn_eval_block_size = nn_eval_block_size
+        self.dcd_block_size = dcd_block_size
+        self.snr_thres = snr_thres
+        self.noise_frame_num_used_for_snr = noise_frame_num_used_for_snr
+        self.decibel_thres = decibel_thres
+        self.speech_noise_thres = speech_noise_thres
+        self.fe_prior_thres = fe_prior_thres
+        self.silence_pdf_num = silence_pdf_num
+        self.sil_pdf_ids = sil_pdf_ids
+        self.speech_noise_thresh_low = speech_noise_thresh_low
+        self.speech_noise_thresh_high = speech_noise_thresh_high
+        self.output_frame_probs = output_frame_probs
+        self.frame_in_ms = frame_in_ms
+        self.frame_length_ms = frame_length_ms
+
+
+class E2EVadSpeechBufWithDoa(object):
+    def __init__(self):
+        self.start_ms = 0
+        self.end_ms = 0
+        self.buffer = []
+        self.contain_seg_start_point = False
+        self.contain_seg_end_point = False
+        self.doa = 0
+
+    def Reset(self):
+        self.start_ms = 0
+        self.end_ms = 0
+        self.buffer = []
+        self.contain_seg_start_point = False
+        self.contain_seg_end_point = False
+        self.doa = 0
+
+
+class E2EVadFrameProb(object):
+    def __init__(self):
+        self.noise_prob = 0.0
+        self.speech_prob = 0.0
+        self.score = 0.0
+        self.frame_id = 0
+        self.frm_state = 0
+
+
+class WindowDetector(object):
+    def __init__(self, window_size_ms: int, sil_to_speech_time: int,
+                 speech_to_sil_time: int, frame_size_ms: int):
+        self.window_size_ms = window_size_ms
+        self.sil_to_speech_time = sil_to_speech_time
+        self.speech_to_sil_time = speech_to_sil_time
+        self.frame_size_ms = frame_size_ms
+
+        self.win_size_frame = int(window_size_ms / frame_size_ms)
+        self.win_sum = 0
+        self.win_state = [0] * self.win_size_frame  # 初始化窗
+
+        self.cur_win_pos = 0
+        self.pre_frame_state = FrameState.kFrameStateSil
+        self.cur_frame_state = FrameState.kFrameStateSil
+        self.sil_to_speech_frmcnt_thres = int(sil_to_speech_time / frame_size_ms)
+        self.speech_to_sil_frmcnt_thres = int(speech_to_sil_time / frame_size_ms)
+
+        self.voice_last_frame_count = 0
+        self.noise_last_frame_count = 0
+        self.hydre_frame_count = 0
+
+    def Reset(self) -> None:
+        self.cur_win_pos = 0
+        self.win_sum = 0
+        self.win_state = [0] * self.win_size_frame
+        self.pre_frame_state = FrameState.kFrameStateSil
+        self.cur_frame_state = FrameState.kFrameStateSil
+        self.voice_last_frame_count = 0
+        self.noise_last_frame_count = 0
+        self.hydre_frame_count = 0
+
+    def GetWinSize(self) -> int:
+        return int(self.win_size_frame)
+
+    def DetectOneFrame(self, frameState: FrameState, frame_count: int) -> AudioChangeState:
+        cur_frame_state = FrameState.kFrameStateSil
+        if frameState == FrameState.kFrameStateSpeech:
+            cur_frame_state = 1
+        elif frameState == FrameState.kFrameStateSil:
+            cur_frame_state = 0
+        else:
+            return AudioChangeState.kChangeStateInvalid
+        self.win_sum -= self.win_state[self.cur_win_pos]
+        self.win_sum += cur_frame_state
+        self.win_state[self.cur_win_pos] = cur_frame_state
+        self.cur_win_pos = (self.cur_win_pos + 1) % self.win_size_frame
+
+        if self.pre_frame_state == FrameState.kFrameStateSil and self.win_sum >= self.sil_to_speech_frmcnt_thres:
+            self.pre_frame_state = FrameState.kFrameStateSpeech
+            return AudioChangeState.kChangeStateSil2Speech
+
+        if self.pre_frame_state == FrameState.kFrameStateSpeech and self.win_sum <= self.speech_to_sil_frmcnt_thres:
+            self.pre_frame_state = FrameState.kFrameStateSil
+            return AudioChangeState.kChangeStateSpeech2Sil
+
+        if self.pre_frame_state == FrameState.kFrameStateSil:
+            return AudioChangeState.kChangeStateSil2Sil
+        if self.pre_frame_state == FrameState.kFrameStateSpeech:
+            return AudioChangeState.kChangeStateSpeech2Speech
+        return AudioChangeState.kChangeStateInvalid
+
+    def FrameSizeMs(self) -> int:
+        return int(self.frame_size_ms)
+
+
+class E2EVadModel():
+    def __init__(self, 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,
+                                               self.vad_opts.sil_to_speech_time_thres,
+                                               self.vad_opts.speech_to_sil_time_thres,
+                                               self.vad_opts.frame_in_ms)
+        # self.encoder = encoder
+        # init variables
+        self.is_final = False
+        self.data_buf_start_frame = 0
+        self.frm_cnt = 0
+        self.latest_confirmed_speech_frame = 0
+        self.lastest_confirmed_silence_frame = -1
+        self.continous_silence_frame_count = 0
+        self.vad_state_machine = VadStateMachine.kVadInStateStartPointNotDetected
+        self.confirmed_start_frame = -1
+        self.confirmed_end_frame = -1
+        self.number_end_time_detected = 0
+        self.sil_frame = 0
+        self.sil_pdf_ids = self.vad_opts.sil_pdf_ids
+        self.noise_average_decibel = -100.0
+        self.pre_end_silence_detected = False
+        self.next_seg = True
+
+        self.output_data_buf = []
+        self.output_data_buf_offset = 0
+        self.frame_probs = []
+        self.max_end_sil_frame_cnt_thresh = self.vad_opts.max_end_silence_time - self.vad_opts.speech_to_sil_time_thres
+        self.speech_noise_thres = self.vad_opts.speech_noise_thres
+        self.scores = None
+        self.max_time_out = False
+        self.decibel = []
+        self.data_buf = None
+        self.data_buf_all = None
+        self.waveform = None
+        self.ResetDetection()
+
+    def AllResetDetection(self):
+        self.is_final = False
+        self.data_buf_start_frame = 0
+        self.frm_cnt = 0
+        self.latest_confirmed_speech_frame = 0
+        self.lastest_confirmed_silence_frame = -1
+        self.continous_silence_frame_count = 0
+        self.vad_state_machine = VadStateMachine.kVadInStateStartPointNotDetected
+        self.confirmed_start_frame = -1
+        self.confirmed_end_frame = -1
+        self.number_end_time_detected = 0
+        self.sil_frame = 0
+        self.sil_pdf_ids = self.vad_opts.sil_pdf_ids
+        self.noise_average_decibel = -100.0
+        self.pre_end_silence_detected = False
+        self.next_seg = True
+
+        self.output_data_buf = []
+        self.output_data_buf_offset = 0
+        self.frame_probs = []
+        self.max_end_sil_frame_cnt_thresh = self.vad_opts.max_end_silence_time - self.vad_opts.speech_to_sil_time_thres
+        self.speech_noise_thres = self.vad_opts.speech_noise_thres
+        self.scores = None
+        self.max_time_out = False
+        self.decibel = []
+        self.data_buf = None
+        self.data_buf_all = None
+        self.waveform = None
+        self.ResetDetection()
+
+    def ResetDetection(self):
+        self.continous_silence_frame_count = 0
+        self.latest_confirmed_speech_frame = 0
+        self.lastest_confirmed_silence_frame = -1
+        self.confirmed_start_frame = -1
+        self.confirmed_end_frame = -1
+        self.vad_state_machine = VadStateMachine.kVadInStateStartPointNotDetected
+        self.windows_detector.Reset()
+        self.sil_frame = 0
+        self.frame_probs = []
+
+    def ComputeDecibel(self) -> None:
+        frame_sample_length = int(self.vad_opts.frame_length_ms * self.vad_opts.sample_rate / 1000)
+        frame_shift_length = int(self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000)
+        if self.data_buf_all is None:
+            self.data_buf_all = self.waveform[0]  # self.data_buf is pointed to self.waveform[0]
+            self.data_buf = self.data_buf_all
+        else:
+            self.data_buf_all = np.concatenate((self.data_buf_all, self.waveform[0]))
+        for offset in range(0, self.waveform.shape[1] - frame_sample_length + 1, frame_shift_length):
+            self.decibel.append(
+                10 * math.log10(np.square((self.waveform[0][offset: offset + frame_sample_length])).sum() + \
+                                0.000001))
+
+    def ComputeScores(self, scores: np.ndarray) -> None:
+        # scores = self.encoder(feats, in_cache)  # return B * T * D
+        self.vad_opts.nn_eval_block_size = scores.shape[1]
+        self.frm_cnt += scores.shape[1]  # count total frames
+        if self.scores is None:
+            self.scores = scores  # the first calculation
+        else:
+            self.scores = np.concatenate((self.scores, scores), axis=1)
+
+    def PopDataBufTillFrame(self, frame_idx: int) -> None:  # need check again
+        while self.data_buf_start_frame < frame_idx:
+            if len(self.data_buf) >= int(self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000):
+                self.data_buf_start_frame += 1
+                self.data_buf = self.data_buf_all[self.data_buf_start_frame * int(
+                    self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000):]
+
+    def PopDataToOutputBuf(self, start_frm: int, frm_cnt: int, first_frm_is_start_point: bool,
+                           last_frm_is_end_point: bool, end_point_is_sent_end: bool) -> None:
+        self.PopDataBufTillFrame(start_frm)
+        expected_sample_number = int(frm_cnt * self.vad_opts.sample_rate * self.vad_opts.frame_in_ms / 1000)
+        if last_frm_is_end_point:
+            extra_sample = max(0, int(self.vad_opts.frame_length_ms * self.vad_opts.sample_rate / 1000 - \
+                                      self.vad_opts.sample_rate * self.vad_opts.frame_in_ms / 1000))
+            expected_sample_number += int(extra_sample)
+        if end_point_is_sent_end:
+            expected_sample_number = max(expected_sample_number, len(self.data_buf))
+        if len(self.data_buf) < expected_sample_number:
+            print('error in calling pop data_buf\n')
+
+        if len(self.output_data_buf) == 0 or first_frm_is_start_point:
+            self.output_data_buf.append(E2EVadSpeechBufWithDoa())
+            self.output_data_buf[-1].Reset()
+            self.output_data_buf[-1].start_ms = start_frm * self.vad_opts.frame_in_ms
+            self.output_data_buf[-1].end_ms = self.output_data_buf[-1].start_ms
+            self.output_data_buf[-1].doa = 0
+        cur_seg = self.output_data_buf[-1]
+        if cur_seg.end_ms != start_frm * self.vad_opts.frame_in_ms:
+            print('warning\n')
+        out_pos = len(cur_seg.buffer)  # cur_seg.buff现在没做任何操作
+        data_to_pop = 0
+        if end_point_is_sent_end:
+            data_to_pop = expected_sample_number
+        else:
+            data_to_pop = int(frm_cnt * self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000)
+        if data_to_pop > len(self.data_buf):
+            print('VAD data_to_pop is bigger than self.data_buf.size()!!!\n')
+            data_to_pop = len(self.data_buf)
+            expected_sample_number = len(self.data_buf)
+
+        cur_seg.doa = 0
+        for sample_cpy_out in range(0, data_to_pop):
+            # cur_seg.buffer[out_pos ++] = data_buf_.back();
+            out_pos += 1
+        for sample_cpy_out in range(data_to_pop, expected_sample_number):
+            # cur_seg.buffer[out_pos++] = data_buf_.back()
+            out_pos += 1
+        if cur_seg.end_ms != start_frm * self.vad_opts.frame_in_ms:
+            print('Something wrong with the VAD algorithm\n')
+        self.data_buf_start_frame += frm_cnt
+        cur_seg.end_ms = (start_frm + frm_cnt) * self.vad_opts.frame_in_ms
+        if first_frm_is_start_point:
+            cur_seg.contain_seg_start_point = True
+        if last_frm_is_end_point:
+            cur_seg.contain_seg_end_point = True
+
+    def OnSilenceDetected(self, valid_frame: int):
+        self.lastest_confirmed_silence_frame = valid_frame
+        if self.vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
+            self.PopDataBufTillFrame(valid_frame)
+        # silence_detected_callback_
+        # pass
+
+    def OnVoiceDetected(self, valid_frame: int) -> None:
+        self.latest_confirmed_speech_frame = valid_frame
+        self.PopDataToOutputBuf(valid_frame, 1, False, False, False)
+
+    def OnVoiceStart(self, start_frame: int, fake_result: bool = False) -> None:
+        if self.vad_opts.do_start_point_detection:
+            pass
+        if self.confirmed_start_frame != -1:
+            print('not reset vad properly\n')
+        else:
+            self.confirmed_start_frame = start_frame
+
+        if not fake_result and self.vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
+            self.PopDataToOutputBuf(self.confirmed_start_frame, 1, True, False, False)
+
+    def OnVoiceEnd(self, end_frame: int, fake_result: bool, is_last_frame: bool) -> None:
+        for t in range(self.latest_confirmed_speech_frame + 1, end_frame):
+            self.OnVoiceDetected(t)
+        if self.vad_opts.do_end_point_detection:
+            pass
+        if self.confirmed_end_frame != -1:
+            print('not reset vad properly\n')
+        else:
+            self.confirmed_end_frame = end_frame
+        if not fake_result:
+            self.sil_frame = 0
+            self.PopDataToOutputBuf(self.confirmed_end_frame, 1, False, True, is_last_frame)
+        self.number_end_time_detected += 1
+
+    def MaybeOnVoiceEndIfLastFrame(self, is_final_frame: bool, cur_frm_idx: int) -> None:
+        if is_final_frame:
+            self.OnVoiceEnd(cur_frm_idx, False, True)
+            self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
+
+    def GetLatency(self) -> int:
+        return int(self.LatencyFrmNumAtStartPoint() * self.vad_opts.frame_in_ms)
+
+    def LatencyFrmNumAtStartPoint(self) -> int:
+        vad_latency = self.windows_detector.GetWinSize()
+        if self.vad_opts.do_extend:
+            vad_latency += int(self.vad_opts.lookback_time_start_point / self.vad_opts.frame_in_ms)
+        return vad_latency
+
+    def GetFrameState(self, t: int) -> FrameState:
+        frame_state = FrameState.kFrameStateInvalid
+        cur_decibel = self.decibel[t]
+        cur_snr = cur_decibel - self.noise_average_decibel
+        # for each frame, calc log posterior probability of each state
+        if cur_decibel < self.vad_opts.decibel_thres:
+            frame_state = FrameState.kFrameStateSil
+            self.DetectOneFrame(frame_state, t, False)
+            return frame_state
+
+        sum_score = 0.0
+        noise_prob = 0.0
+        assert len(self.sil_pdf_ids) == self.vad_opts.silence_pdf_num
+        if len(self.sil_pdf_ids) > 0:
+            assert len(self.scores) == 1  # 只支持batch_size = 1的测试
+            sil_pdf_scores = [self.scores[0][t][sil_pdf_id] for sil_pdf_id in self.sil_pdf_ids]
+            sum_score = sum(sil_pdf_scores)
+            noise_prob = math.log(sum_score) * self.vad_opts.speech_2_noise_ratio
+            total_score = 1.0
+            sum_score = total_score - sum_score
+        speech_prob = math.log(sum_score)
+        if self.vad_opts.output_frame_probs:
+            frame_prob = E2EVadFrameProb()
+            frame_prob.noise_prob = noise_prob
+            frame_prob.speech_prob = speech_prob
+            frame_prob.score = sum_score
+            frame_prob.frame_id = t
+            self.frame_probs.append(frame_prob)
+        if math.exp(speech_prob) >= math.exp(noise_prob) + self.speech_noise_thres:
+            if cur_snr >= self.vad_opts.snr_thres and cur_decibel >= self.vad_opts.decibel_thres:
+                frame_state = FrameState.kFrameStateSpeech
+            else:
+                frame_state = FrameState.kFrameStateSil
+        else:
+            frame_state = FrameState.kFrameStateSil
+            if self.noise_average_decibel < -99.9:
+                self.noise_average_decibel = cur_decibel
+            else:
+                self.noise_average_decibel = (cur_decibel + self.noise_average_decibel * (
+                        self.vad_opts.noise_frame_num_used_for_snr
+                        - 1)) / self.vad_opts.noise_frame_num_used_for_snr
+
+        return frame_state
+     
+
+    def __call__(self, score: np.ndarray, waveform: np.ndarray,
+                is_final: bool = False, max_end_sil: int = 800
+                ):
+        self.max_end_sil_frame_cnt_thresh = max_end_sil - self.vad_opts.speech_to_sil_time_thres
+        self.waveform = waveform  # compute decibel for each frame
+        self.ComputeDecibel()
+        self.ComputeScores(score)
+        if not is_final:
+            self.DetectCommonFrames()
+        else:
+            self.DetectLastFrames()
+        segments = []
+        for batch_num in range(0, score.shape[0]):  # only support batch_size = 1 now
+            segment_batch = []
+            if len(self.output_data_buf) > 0:
+                for i in range(self.output_data_buf_offset, len(self.output_data_buf)):
+                    if not self.output_data_buf[i].contain_seg_start_point:
+                        continue
+                    if not self.next_seg and not self.output_data_buf[i].contain_seg_end_point:
+                        continue
+                    start_ms = self.output_data_buf[i].start_ms if self.next_seg else -1
+                    if self.output_data_buf[i].contain_seg_end_point:
+                        end_ms = self.output_data_buf[i].end_ms
+                        self.next_seg = True
+                        self.output_data_buf_offset += 1
+                    else:
+                        end_ms = -1
+                        self.next_seg = False
+                    segment = [start_ms, end_ms]
+                    segment_batch.append(segment)
+            if segment_batch:
+                segments.append(segment_batch)
+        if is_final:
+            # reset class variables and clear the dict for the next query
+            self.AllResetDetection()
+        return segments
+
+    def DetectCommonFrames(self) -> int:
+        if self.vad_state_machine == VadStateMachine.kVadInStateEndPointDetected:
+            return 0
+        for i in range(self.vad_opts.nn_eval_block_size - 1, -1, -1):
+            frame_state = FrameState.kFrameStateInvalid
+            frame_state = self.GetFrameState(self.frm_cnt - 1 - i)
+            self.DetectOneFrame(frame_state, self.frm_cnt - 1 - i, False)
+
+        return 0
+
+    def DetectLastFrames(self) -> int:
+        if self.vad_state_machine == VadStateMachine.kVadInStateEndPointDetected:
+            return 0
+        for i in range(self.vad_opts.nn_eval_block_size - 1, -1, -1):
+            frame_state = FrameState.kFrameStateInvalid
+            frame_state = self.GetFrameState(self.frm_cnt - 1 - i)
+            if i != 0:
+                self.DetectOneFrame(frame_state, self.frm_cnt - 1 - i, False)
+            else:
+                self.DetectOneFrame(frame_state, self.frm_cnt - 1, True)
+
+        return 0
+
+    def DetectOneFrame(self, cur_frm_state: FrameState, cur_frm_idx: int, is_final_frame: bool) -> None:
+        tmp_cur_frm_state = FrameState.kFrameStateInvalid
+        if cur_frm_state == FrameState.kFrameStateSpeech:
+            if math.fabs(1.0) > self.vad_opts.fe_prior_thres:
+                tmp_cur_frm_state = FrameState.kFrameStateSpeech
+            else:
+                tmp_cur_frm_state = FrameState.kFrameStateSil
+        elif cur_frm_state == FrameState.kFrameStateSil:
+            tmp_cur_frm_state = FrameState.kFrameStateSil
+        state_change = self.windows_detector.DetectOneFrame(tmp_cur_frm_state, cur_frm_idx)
+        frm_shift_in_ms = self.vad_opts.frame_in_ms
+        if AudioChangeState.kChangeStateSil2Speech == state_change:
+            silence_frame_count = self.continous_silence_frame_count
+            self.continous_silence_frame_count = 0
+            self.pre_end_silence_detected = False
+            start_frame = 0
+            if self.vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
+                start_frame = max(self.data_buf_start_frame, cur_frm_idx - self.LatencyFrmNumAtStartPoint())
+                self.OnVoiceStart(start_frame)
+                self.vad_state_machine = VadStateMachine.kVadInStateInSpeechSegment
+                for t in range(start_frame + 1, cur_frm_idx + 1):
+                    self.OnVoiceDetected(t)
+            elif self.vad_state_machine == VadStateMachine.kVadInStateInSpeechSegment:
+                for t in range(self.latest_confirmed_speech_frame + 1, cur_frm_idx):
+                    self.OnVoiceDetected(t)
+                if cur_frm_idx - self.confirmed_start_frame + 1 > \
+                        self.vad_opts.max_single_segment_time / frm_shift_in_ms:
+                    self.OnVoiceEnd(cur_frm_idx, False, False)
+                    self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
+                elif not is_final_frame:
+                    self.OnVoiceDetected(cur_frm_idx)
+                else:
+                    self.MaybeOnVoiceEndIfLastFrame(is_final_frame, cur_frm_idx)
+            else:
+                pass
+        elif AudioChangeState.kChangeStateSpeech2Sil == state_change:
+            self.continous_silence_frame_count = 0
+            if self.vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
+                pass
+            elif self.vad_state_machine == VadStateMachine.kVadInStateInSpeechSegment:
+                if cur_frm_idx - self.confirmed_start_frame + 1 > \
+                        self.vad_opts.max_single_segment_time / frm_shift_in_ms:
+                    self.OnVoiceEnd(cur_frm_idx, False, False)
+                    self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
+                elif not is_final_frame:
+                    self.OnVoiceDetected(cur_frm_idx)
+                else:
+                    self.MaybeOnVoiceEndIfLastFrame(is_final_frame, cur_frm_idx)
+            else:
+                pass
+        elif AudioChangeState.kChangeStateSpeech2Speech == state_change:
+            self.continous_silence_frame_count = 0
+            if self.vad_state_machine == VadStateMachine.kVadInStateInSpeechSegment:
+                if cur_frm_idx - self.confirmed_start_frame + 1 > \
+                        self.vad_opts.max_single_segment_time / frm_shift_in_ms:
+                    self.max_time_out = True
+                    self.OnVoiceEnd(cur_frm_idx, False, False)
+                    self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
+                elif not is_final_frame:
+                    self.OnVoiceDetected(cur_frm_idx)
+                else:
+                    self.MaybeOnVoiceEndIfLastFrame(is_final_frame, cur_frm_idx)
+            else:
+                pass
+        elif AudioChangeState.kChangeStateSil2Sil == state_change:
+            self.continous_silence_frame_count += 1
+            if self.vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
+                # silence timeout, return zero length decision
+                if ((self.vad_opts.detect_mode == VadDetectMode.kVadSingleUtteranceDetectMode.value) and (
+                        self.continous_silence_frame_count * frm_shift_in_ms > self.vad_opts.max_start_silence_time)) \
+                        or (is_final_frame and self.number_end_time_detected == 0):
+                    for t in range(self.lastest_confirmed_silence_frame + 1, cur_frm_idx):
+                        self.OnSilenceDetected(t)
+                    self.OnVoiceStart(0, True)
+                    self.OnVoiceEnd(0, True, False);
+                    self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
+                else:
+                    if cur_frm_idx >= self.LatencyFrmNumAtStartPoint():
+                        self.OnSilenceDetected(cur_frm_idx - self.LatencyFrmNumAtStartPoint())
+            elif self.vad_state_machine == VadStateMachine.kVadInStateInSpeechSegment:
+                if self.continous_silence_frame_count * frm_shift_in_ms >= self.max_end_sil_frame_cnt_thresh:
+                    lookback_frame = int(self.max_end_sil_frame_cnt_thresh / frm_shift_in_ms)
+                    if self.vad_opts.do_extend:
+                        lookback_frame -= int(self.vad_opts.lookahead_time_end_point / frm_shift_in_ms)
+                        lookback_frame -= 1
+                        lookback_frame = max(0, lookback_frame)
+                    self.OnVoiceEnd(cur_frm_idx - lookback_frame, False, False)
+                    self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
+                elif cur_frm_idx - self.confirmed_start_frame + 1 > \
+                        self.vad_opts.max_single_segment_time / frm_shift_in_ms:
+                    self.OnVoiceEnd(cur_frm_idx, False, False)
+                    self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
+                elif self.vad_opts.do_extend and not is_final_frame:
+                    if self.continous_silence_frame_count <= int(
+                            self.vad_opts.lookahead_time_end_point / frm_shift_in_ms):
+                        self.OnVoiceDetected(cur_frm_idx)
+                else:
+                    self.MaybeOnVoiceEndIfLastFrame(is_final_frame, cur_frm_idx)
+            else:
+                pass
+
+        if self.vad_state_machine == VadStateMachine.kVadInStateEndPointDetected and \
+                self.vad_opts.detect_mode == VadDetectMode.kVadMutipleUtteranceDetectMode.value:
+            self.ResetDetection()

funasr_onnx/utils/frontend.py

@@ -0,0 +1,191 @@
+# -*- encoding: utf-8 -*-
+from pathlib import Path
+from typing import Any, Dict, Iterable, List, NamedTuple, Set, Tuple, Union
+
+import numpy as np
+from typeguard import check_argument_types
+import kaldi_native_fbank as knf
+
+root_dir = Path(__file__).resolve().parent
+
+logger_initialized = {}
+
+
+class WavFrontend():
+    """Conventional frontend structure for ASR.
+    """
+
+    def __init__(
+            self,
+            cmvn_file: str = None,
+            fs: int = 16000,
+            window: str = 'hamming',
+            n_mels: int = 80,
+            frame_length: int = 25,
+            frame_shift: int = 10,
+            lfr_m: int = 1,
+            lfr_n: int = 1,
+            dither: float = 1.0,
+            **kwargs,
+    ) -> None:
+        check_argument_types()
+
+        opts = knf.FbankOptions()
+        opts.frame_opts.samp_freq = fs
+        opts.frame_opts.dither = dither
+        opts.frame_opts.window_type = window
+        opts.frame_opts.frame_shift_ms = float(frame_shift)
+        opts.frame_opts.frame_length_ms = float(frame_length)
+        opts.mel_opts.num_bins = n_mels
+        opts.energy_floor = 0
+        opts.frame_opts.snip_edges = True
+        opts.mel_opts.debug_mel = False
+        self.opts = opts
+
+        self.lfr_m = lfr_m
+        self.lfr_n = lfr_n
+        self.cmvn_file = cmvn_file
+
+        if self.cmvn_file:
+            self.cmvn = self.load_cmvn()
+        self.fbank_fn = None
+        self.fbank_beg_idx = 0
+        self.reset_status()
+
+    def fbank(self,
+              waveform: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+        waveform = waveform * (1 << 15)
+        self.fbank_fn = knf.OnlineFbank(self.opts)
+        self.fbank_fn.accept_waveform(self.opts.frame_opts.samp_freq, waveform.tolist())
+        frames = self.fbank_fn.num_frames_ready
+        mat = np.empty([frames, self.opts.mel_opts.num_bins])
+        for i in range(frames):
+            mat[i, :] = self.fbank_fn.get_frame(i)
+        feat = mat.astype(np.float32)
+        feat_len = np.array(mat.shape[0]).astype(np.int32)
+        return feat, feat_len
+
+    def fbank_online(self,
+              waveform: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+        waveform = waveform * (1 << 15)
+        # self.fbank_fn = knf.OnlineFbank(self.opts)
+        self.fbank_fn.accept_waveform(self.opts.frame_opts.samp_freq, waveform.tolist())
+        frames = self.fbank_fn.num_frames_ready
+        mat = np.empty([frames, self.opts.mel_opts.num_bins])
+        for i in range(self.fbank_beg_idx, frames):
+            mat[i, :] = self.fbank_fn.get_frame(i)
+        # self.fbank_beg_idx += (frames-self.fbank_beg_idx)
+        feat = mat.astype(np.float32)
+        feat_len = np.array(mat.shape[0]).astype(np.int32)
+        return feat, feat_len
+
+    def reset_status(self):
+        self.fbank_fn = knf.OnlineFbank(self.opts)
+        self.fbank_beg_idx = 0
+
+    def lfr_cmvn(self, feat: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+        if self.lfr_m != 1 or self.lfr_n != 1:
+            feat = self.apply_lfr(feat, self.lfr_m, self.lfr_n)
+
+        if self.cmvn_file:
+            feat = self.apply_cmvn(feat)
+
+        feat_len = np.array(feat.shape[0]).astype(np.int32)
+        return feat, feat_len
+
+    @staticmethod
+    def apply_lfr(inputs: np.ndarray, lfr_m: int, lfr_n: int) -> np.ndarray:
+        LFR_inputs = []
+
+        T = inputs.shape[0]
+        T_lfr = int(np.ceil(T / lfr_n))
+        left_padding = np.tile(inputs[0], ((lfr_m - 1) // 2, 1))
+        inputs = np.vstack((left_padding, inputs))
+        T = T + (lfr_m - 1) // 2
+        for i in range(T_lfr):
+            if lfr_m <= T - i * lfr_n:
+                LFR_inputs.append(
+                    (inputs[i * lfr_n:i * lfr_n + lfr_m]).reshape(1, -1))
+            else:
+                # process last LFR frame
+                num_padding = lfr_m - (T - i * lfr_n)
+                frame = inputs[i * lfr_n:].reshape(-1)
+                for _ in range(num_padding):
+                    frame = np.hstack((frame, inputs[-1]))
+
+                LFR_inputs.append(frame)
+        LFR_outputs = np.vstack(LFR_inputs).astype(np.float32)
+        return LFR_outputs
+
+    def apply_cmvn(self, inputs: np.ndarray) -> np.ndarray:
+        """
+        Apply CMVN with mvn data
+        """
+        frame, dim = inputs.shape
+        means = np.tile(self.cmvn[0:1, :dim], (frame, 1))
+        vars = np.tile(self.cmvn[1:2, :dim], (frame, 1))
+        inputs = (inputs + means) * vars
+        return inputs
+
+    def load_cmvn(self,) -> np.ndarray:
+        with open(self.cmvn_file, 'r', encoding='utf-8') as f:
+            lines = f.readlines()
+
+        means_list = []
+        vars_list = []
+        for i in range(len(lines)):
+            line_item = lines[i].split()
+            if line_item[0] == '<AddShift>':
+                line_item = lines[i + 1].split()
+                if line_item[0] == '<LearnRateCoef>':
+                    add_shift_line = line_item[3:(len(line_item) - 1)]
+                    means_list = list(add_shift_line)
+                    continue
+            elif line_item[0] == '<Rescale>':
+                line_item = lines[i + 1].split()
+                if line_item[0] == '<LearnRateCoef>':
+                    rescale_line = line_item[3:(len(line_item) - 1)]
+                    vars_list = list(rescale_line)
+                    continue
+
+        means = np.array(means_list).astype(np.float64)
+        vars = np.array(vars_list).astype(np.float64)
+        cmvn = np.array([means, vars])
+        return cmvn
+
+def load_bytes(input):
+    middle_data = np.frombuffer(input, dtype=np.int16)
+    middle_data = np.asarray(middle_data)
+    if middle_data.dtype.kind not in 'iu':
+        raise TypeError("'middle_data' must be an array of integers")
+    dtype = np.dtype('float32')
+    if dtype.kind != 'f':
+        raise TypeError("'dtype' must be a floating point type")
+
+    i = np.iinfo(middle_data.dtype)
+    abs_max = 2 ** (i.bits - 1)
+    offset = i.min + abs_max
+    array = np.frombuffer((middle_data.astype(dtype) - offset) / abs_max, dtype=np.float32)
+    return array
+
+
+def test():
+    path = "/nfs/zhifu.gzf/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/example/asr_example.wav"
+    import librosa
+    cmvn_file = "/nfs/zhifu.gzf/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/am.mvn"
+    config_file = "/nfs/zhifu.gzf/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/config.yaml"
+    from funasr.runtime.python.onnxruntime.rapid_paraformer.utils.utils import read_yaml
+    config = read_yaml(config_file)
+    waveform, _ = librosa.load(path, sr=None)
+    frontend = WavFrontend(
+        cmvn_file=cmvn_file,
+        **config['frontend_conf'],
+    )
+    speech, _ = frontend.fbank_online(waveform)  #1d, (sample,), numpy
+    feat, feat_len = frontend.lfr_cmvn(speech) # 2d, (frame, 450), np.float32 -> torch, torch.from_numpy(), dtype, (1, frame, 450)
+    
+    frontend.reset_status() # clear cache
+    return feat, feat_len
+
+if __name__ == '__main__':
+    test()

funasr_onnx/utils/postprocess_utils.py

@@ -0,0 +1,240 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+
+import string
+import logging
+from typing import Any, List, Union
+
+
+def isChinese(ch: str):
+    if '\u4e00' <= ch <= '\u9fff' or '\u0030' <= ch <= '\u0039':
+        return True
+    return False
+
+
+def isAllChinese(word: Union[List[Any], str]):
+    word_lists = []
+    for i in word:
+        cur = i.replace(' ', '')
+        cur = cur.replace('</s>', '')
+        cur = cur.replace('<s>', '')
+        word_lists.append(cur)
+
+    if len(word_lists) == 0:
+        return False
+
+    for ch in word_lists:
+        if isChinese(ch) is False:
+            return False
+    return True
+
+
+def isAllAlpha(word: Union[List[Any], str]):
+    word_lists = []
+    for i in word:
+        cur = i.replace(' ', '')
+        cur = cur.replace('</s>', '')
+        cur = cur.replace('<s>', '')
+        word_lists.append(cur)
+
+    if len(word_lists) == 0:
+        return False
+
+    for ch in word_lists:
+        if ch.isalpha() is False and ch != "'":
+            return False
+        elif ch.isalpha() is True and isChinese(ch) is True:
+            return False
+
+    return True
+
+
+# def abbr_dispose(words: List[Any]) -> List[Any]:
+def abbr_dispose(words: List[Any], time_stamp: List[List] = None) -> List[Any]:
+    words_size = len(words)
+    word_lists = []
+    abbr_begin = []
+    abbr_end = []
+    last_num = -1
+    ts_lists = []
+    ts_nums = []
+    ts_index = 0
+    for num in range(words_size):
+        if num <= last_num:
+            continue
+
+        if len(words[num]) == 1 and words[num].encode('utf-8').isalpha():
+            if num + 1 < words_size and words[
+                    num + 1] == ' ' and num + 2 < words_size and len(
+                        words[num +
+                              2]) == 1 and words[num +
+                                                 2].encode('utf-8').isalpha():
+                # found the begin of abbr
+                abbr_begin.append(num)
+                num += 2
+                abbr_end.append(num)
+                # to find the end of abbr
+                while True:
+                    num += 1
+                    if num < words_size and words[num] == ' ':
+                        num += 1
+                        if num < words_size and len(
+                                words[num]) == 1 and words[num].encode(
+                                    'utf-8').isalpha():
+                            abbr_end.pop()
+                            abbr_end.append(num)
+                            last_num = num
+                        else:
+                            break
+                    else:
+                        break
+
+    for num in range(words_size):
+        if words[num] == ' ':
+            ts_nums.append(ts_index)
+        else:
+            ts_nums.append(ts_index)
+            ts_index += 1 
+    last_num = -1
+    for num in range(words_size):
+        if num <= last_num:
+            continue
+
+        if num in abbr_begin:
+            if time_stamp is not None:
+                begin = time_stamp[ts_nums[num]][0]
+            word_lists.append(words[num].upper())
+            num += 1
+            while num < words_size:
+                if num in abbr_end:
+                    word_lists.append(words[num].upper())
+                    last_num = num
+                    break
+                else:
+                    if words[num].encode('utf-8').isalpha():
+                        word_lists.append(words[num].upper())
+                num += 1
+            if time_stamp is not None:
+                end = time_stamp[ts_nums[num]][1]
+                ts_lists.append([begin, end])
+        else:
+            word_lists.append(words[num])
+            if time_stamp is not None and words[num] != ' ':
+                begin = time_stamp[ts_nums[num]][0]
+                end = time_stamp[ts_nums[num]][1]
+                ts_lists.append([begin, end])
+                begin = end
+
+    if time_stamp is not None:
+        return word_lists, ts_lists
+    else:
+        return word_lists
+
+
+def sentence_postprocess(words: List[Any], time_stamp: List[List] = None):
+    middle_lists = []
+    word_lists = []
+    word_item = ''
+    ts_lists = []
+
+    # wash words lists
+    for i in words:
+        word = ''
+        if isinstance(i, str):
+            word = i
+        else:
+            word = i.decode('utf-8')
+
+        if word in ['<s>', '</s>', '<unk>']:
+            continue
+        else:
+            middle_lists.append(word)
+
+    # all chinese characters
+    if isAllChinese(middle_lists):
+        for i, ch in enumerate(middle_lists):
+            word_lists.append(ch.replace(' ', ''))
+        if time_stamp is not None:
+            ts_lists = time_stamp
+
+    # all alpha characters
+    elif isAllAlpha(middle_lists):
+        ts_flag = True
+        for i, ch in enumerate(middle_lists):
+            if ts_flag and time_stamp is not None:
+                begin = time_stamp[i][0]
+                end = time_stamp[i][1]
+            word = ''
+            if '@@' in ch:
+                word = ch.replace('@@', '')
+                word_item += word
+                if time_stamp is not None:
+                    ts_flag = False
+                    end = time_stamp[i][1]
+            else:
+                word_item += ch
+                word_lists.append(word_item)
+                word_lists.append(' ')
+                word_item = ''
+                if time_stamp is not None:
+                    ts_flag = True
+                    end = time_stamp[i][1]
+                    ts_lists.append([begin, end])
+                    begin = end
+
+    # mix characters
+    else:
+        alpha_blank = False
+        ts_flag = True
+        begin = -1
+        end = -1
+        for i, ch in enumerate(middle_lists):
+            if ts_flag and time_stamp is not None:
+                begin = time_stamp[i][0]
+                end = time_stamp[i][1]
+            word = ''
+            if isAllChinese(ch):
+                if alpha_blank is True:
+                    word_lists.pop()
+                word_lists.append(ch)
+                alpha_blank = False
+                if time_stamp is not None:
+                    ts_flag = True
+                    ts_lists.append([begin, end])
+                    begin = end
+            elif '@@' in ch:
+                word = ch.replace('@@', '')
+                word_item += word
+                alpha_blank = False
+                if time_stamp is not None:
+                    ts_flag = False
+                    end = time_stamp[i][1]
+            elif isAllAlpha(ch):
+                word_item += ch
+                word_lists.append(word_item)
+                word_lists.append(' ')
+                word_item = ''
+                alpha_blank = True
+                if time_stamp is not None:
+                    ts_flag = True
+                    end = time_stamp[i][1] 
+                    ts_lists.append([begin, end])
+                    begin = end
+            else:
+                raise ValueError('invalid character: {}'.format(ch))
+
+    if time_stamp is not None: 
+        word_lists, ts_lists = abbr_dispose(word_lists, ts_lists)
+        real_word_lists = []
+        for ch in word_lists:
+            if ch != ' ':
+                real_word_lists.append(ch)
+        sentence = ' '.join(real_word_lists).strip()
+        return sentence, ts_lists, real_word_lists
+    else:
+        word_lists = abbr_dispose(word_lists)
+        real_word_lists = []
+        for ch in word_lists:
+            if ch != ' ':
+                real_word_lists.append(ch)
+        sentence = ''.join(word_lists).strip()
+        return sentence, real_word_lists

funasr_onnx/utils/timestamp_utils.py

@@ -0,0 +1,59 @@
+import numpy as np
+
+
+def time_stamp_lfr6_onnx(us_cif_peak, char_list, begin_time=0.0, total_offset=-1.5):
+    if not len(char_list):
+        return []
+    START_END_THRESHOLD = 5
+    MAX_TOKEN_DURATION = 30
+    TIME_RATE = 10.0 * 6 / 1000 / 3  #  3 times upsampled
+    cif_peak = us_cif_peak.reshape(-1)
+    num_frames = cif_peak.shape[-1]
+    if char_list[-1] == '</s>':
+        char_list = char_list[:-1]
+    # char_list = [i for i in text]
+    timestamp_list = []
+    new_char_list = []
+    # for bicif model trained with large data, cif2 actually fires when a character starts
+    # so treat the frames between two peaks as the duration of the former token
+    fire_place = np.where(cif_peak>1.0-1e-4)[0] + total_offset  # np format
+    num_peak = len(fire_place)
+    assert num_peak == len(char_list) + 1 # number of peaks is supposed to be number of tokens + 1
+    # begin silence
+    if fire_place[0] > START_END_THRESHOLD:
+        # char_list.insert(0, '<sil>')
+        timestamp_list.append([0.0, fire_place[0]*TIME_RATE])
+        new_char_list.append('<sil>')
+    # tokens timestamp
+    for i in range(len(fire_place)-1):
+        new_char_list.append(char_list[i])
+        if i == len(fire_place)-2 or MAX_TOKEN_DURATION < 0 or fire_place[i+1] - fire_place[i] < MAX_TOKEN_DURATION:
+            timestamp_list.append([fire_place[i]*TIME_RATE, fire_place[i+1]*TIME_RATE])
+        else:
+            # cut the duration to token and sil of the 0-weight frames last long
+            _split = fire_place[i] + MAX_TOKEN_DURATION
+            timestamp_list.append([fire_place[i]*TIME_RATE, _split*TIME_RATE])
+            timestamp_list.append([_split*TIME_RATE, fire_place[i+1]*TIME_RATE])
+            new_char_list.append('<sil>')
+    # tail token and end silence
+    if num_frames - fire_place[-1] > START_END_THRESHOLD:
+        _end = (num_frames + fire_place[-1]) / 2
+        timestamp_list[-1][1] = _end*TIME_RATE
+        timestamp_list.append([_end*TIME_RATE, num_frames*TIME_RATE])
+        new_char_list.append("<sil>")
+    else:
+        timestamp_list[-1][1] = num_frames*TIME_RATE
+    if begin_time:  # add offset time in model with vad
+        for i in range(len(timestamp_list)):
+            timestamp_list[i][0] = timestamp_list[i][0] + begin_time / 1000.0
+            timestamp_list[i][1] = timestamp_list[i][1] + begin_time / 1000.0
+    assert len(new_char_list) == len(timestamp_list)
+    res_str = ""
+    for char, timestamp in zip(new_char_list, timestamp_list):
+        res_str += "{} {} {};".format(char, timestamp[0], timestamp[1])
+    res = []
+    for char, timestamp in zip(new_char_list, timestamp_list):
+        if char != '<sil>':
+            res.append([int(timestamp[0] * 1000), int(timestamp[1] * 1000)])
+    return res_str, res
+    

funasr_onnx/utils/utils.py

@@ -0,0 +1,289 @@
+# -*- encoding: utf-8 -*-
+
+import functools
+import logging
+import pickle
+from pathlib import Path
+from typing import Any, Dict, Iterable, List, NamedTuple, Set, Tuple, Union
+
+import numpy as np
+import yaml
+from onnxruntime import (GraphOptimizationLevel, InferenceSession,
+                         SessionOptions, get_available_providers, get_device)
+from typeguard import check_argument_types
+
+import warnings
+
+root_dir = Path(__file__).resolve().parent
+
+logger_initialized = {}
+
+
+class TokenIDConverter():
+    def __init__(self, token_list: Union[List, str],
+                 ):
+        check_argument_types()
+
+        # self.token_list = self.load_token(token_path)
+        self.token_list = token_list
+        self.unk_symbol = token_list[-1]
+
+    # @staticmethod
+    # def load_token(file_path: Union[Path, str]) -> List:
+    #     if not Path(file_path).exists():
+    #         raise TokenIDConverterError(f'The {file_path} does not exist.')
+    #
+    #     with open(str(file_path), 'rb') as f:
+    #         token_list = pickle.load(f)
+    #
+    #     if len(token_list) != len(set(token_list)):
+    #         raise TokenIDConverterError('The Token exists duplicated symbol.')
+    #     return token_list
+
+    def get_num_vocabulary_size(self) -> int:
+        return len(self.token_list)
+
+    def ids2tokens(self,
+                   integers: Union[np.ndarray, Iterable[int]]) -> List[str]:
+        if isinstance(integers, np.ndarray) and integers.ndim != 1:
+            raise TokenIDConverterError(
+                f"Must be 1 dim ndarray, but got {integers.ndim}")
+        return [self.token_list[i] for i in integers]
+
+    def tokens2ids(self, tokens: Iterable[str]) -> List[int]:
+        token2id = {v: i for i, v in enumerate(self.token_list)}
+        if self.unk_symbol not in token2id:
+            raise TokenIDConverterError(
+                f"Unknown symbol '{self.unk_symbol}' doesn't exist in the token_list"
+            )
+        unk_id = token2id[self.unk_symbol]
+        return [token2id.get(i, unk_id) for i in tokens]
+
+
+class CharTokenizer():
+    def __init__(
+        self,
+        symbol_value: Union[Path, str, Iterable[str]] = None,
+        space_symbol: str = "<space>",
+        remove_non_linguistic_symbols: bool = False,
+    ):
+        check_argument_types()
+
+        self.space_symbol = space_symbol
+        self.non_linguistic_symbols = self.load_symbols(symbol_value)
+        self.remove_non_linguistic_symbols = remove_non_linguistic_symbols
+
+    @staticmethod
+    def load_symbols(value: Union[Path, str, Iterable[str]] = None) -> Set:
+        if value is None:
+            return set()
+
+        if isinstance(value, Iterable[str]):
+            return set(value)
+
+        file_path = Path(value)
+        if not file_path.exists():
+            logging.warning("%s doesn't exist.", file_path)
+            return set()
+
+        with file_path.open("r", encoding="utf-8") as f:
+            return set(line.rstrip() for line in f)
+
+    def text2tokens(self, line: Union[str, list]) -> List[str]:
+        tokens = []
+        while len(line) != 0:
+            for w in self.non_linguistic_symbols:
+                if line.startswith(w):
+                    if not self.remove_non_linguistic_symbols:
+                        tokens.append(line[: len(w)])
+                    line = line[len(w):]
+                    break
+            else:
+                t = line[0]
+                if t == " ":
+                    t = "<space>"
+                tokens.append(t)
+                line = line[1:]
+        return tokens
+
+    def tokens2text(self, tokens: Iterable[str]) -> str:
+        tokens = [t if t != self.space_symbol else " " for t in tokens]
+        return "".join(tokens)
+
+    def __repr__(self):
+        return (
+            f"{self.__class__.__name__}("
+            f'space_symbol="{self.space_symbol}"'
+            f'non_linguistic_symbols="{self.non_linguistic_symbols}"'
+            f")"
+        )
+
+
+
+class Hypothesis(NamedTuple):
+    """Hypothesis data type."""
+
+    yseq: np.ndarray
+    score: Union[float, np.ndarray] = 0
+    scores: Dict[str, Union[float, np.ndarray]] = dict()
+    states: Dict[str, Any] = dict()
+
+    def asdict(self) -> dict:
+        """Convert data to JSON-friendly dict."""
+        return self._replace(
+            yseq=self.yseq.tolist(),
+            score=float(self.score),
+            scores={k: float(v) for k, v in self.scores.items()},
+        )._asdict()
+
+
+class TokenIDConverterError(Exception):
+    pass
+
+
+class ONNXRuntimeError(Exception):
+    pass
+
+
+class OrtInferSession():
+    def __init__(self, model_file, device_id=-1, intra_op_num_threads=4):
+        device_id = str(device_id)
+        sess_opt = SessionOptions()
+        sess_opt.intra_op_num_threads = intra_op_num_threads
+        sess_opt.log_severity_level = 4
+        sess_opt.enable_cpu_mem_arena = False
+        sess_opt.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_ALL
+
+        cuda_ep = 'CUDAExecutionProvider'
+        cuda_provider_options = {
+            "device_id": device_id,
+            "arena_extend_strategy": "kNextPowerOfTwo",
+            "cudnn_conv_algo_search": "EXHAUSTIVE",
+            "do_copy_in_default_stream": "true",
+        }
+        cpu_ep = 'CPUExecutionProvider'
+        cpu_provider_options = {
+            "arena_extend_strategy": "kSameAsRequested",
+        }
+
+        EP_list = []
+        if device_id != "-1" and get_device() == 'GPU' \
+                and cuda_ep in get_available_providers():
+            EP_list = [(cuda_ep, cuda_provider_options)]
+        EP_list.append((cpu_ep, cpu_provider_options))
+
+        self._verify_model(model_file)
+        self.session = InferenceSession(model_file,
+                                        sess_options=sess_opt,
+                                        providers=EP_list)
+
+        if device_id != "-1" and cuda_ep not in self.session.get_providers():
+            warnings.warn(f'{cuda_ep} is not avaiable for current env, the inference part is automatically shifted to be executed under {cpu_ep}.\n'
+                          'Please ensure the installed onnxruntime-gpu version matches your cuda and cudnn version, '
+                          'you can check their relations from the offical web site: '
+                          'https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html',
+                          RuntimeWarning)
+
+    def __call__(self,
+                 input_content: List[Union[np.ndarray, np.ndarray]]) -> np.ndarray:
+        input_dict = dict(zip(self.get_input_names(), input_content))
+        try:
+            return self.session.run(self.get_output_names(), input_dict)
+        except Exception as e:
+            raise ONNXRuntimeError('ONNXRuntime inferece failed.') from e
+
+    def get_input_names(self, ):
+        return [v.name for v in self.session.get_inputs()]
+
+    def get_output_names(self,):
+        return [v.name for v in self.session.get_outputs()]
+
+    def get_character_list(self, key: str = 'character'):
+        return self.meta_dict[key].splitlines()
+
+    def have_key(self, key: str = 'character') -> bool:
+        self.meta_dict = self.session.get_modelmeta().custom_metadata_map
+        if key in self.meta_dict.keys():
+            return True
+        return False
+
+    @staticmethod
+    def _verify_model(model_path):
+        model_path = Path(model_path)
+        if not model_path.exists():
+            raise FileNotFoundError(f'{model_path} does not exists.')
+        if not model_path.is_file():
+            raise FileExistsError(f'{model_path} is not a file.')
+
+def split_to_mini_sentence(words: list, word_limit: int = 20):
+    assert word_limit > 1
+    if len(words) <= word_limit:
+        return [words]
+    sentences = []
+    length = len(words)
+    sentence_len = length // word_limit
+    for i in range(sentence_len):
+        sentences.append(words[i * word_limit:(i + 1) * word_limit])
+    if length % word_limit > 0:
+        sentences.append(words[sentence_len * word_limit:])
+    return sentences
+
+def code_mix_split_words(text: str):
+    words = []
+    segs = text.split()
+    for seg in segs:
+        # There is no space in seg.
+        current_word = ""
+        for c in seg:
+            if len(c.encode()) == 1:
+                # This is an ASCII char.
+                current_word += c
+            else:
+                # This is a Chinese char.
+                if len(current_word) > 0:
+                    words.append(current_word)
+                    current_word = ""
+                words.append(c)
+        if len(current_word) > 0:
+            words.append(current_word)
+    return words
+
+def read_yaml(yaml_path: Union[str, Path]) -> Dict:
+    if not Path(yaml_path).exists():
+        raise FileExistsError(f'The {yaml_path} does not exist.')
+
+    with open(str(yaml_path), 'rb') as f:
+        data = yaml.load(f, Loader=yaml.Loader)
+    return data
+
+
+@functools.lru_cache()
+def get_logger(name='funasr_onnx'):
+    """Initialize and get a logger by name.
+    If the logger has not been initialized, this method will initialize the
+    logger by adding one or two handlers, otherwise the initialized logger will
+    be directly returned. During initialization, a StreamHandler will always be
+    added.
+    Args:
+        name (str): Logger name.
+    Returns:
+        logging.Logger: The expected logger.
+    """
+    logger = logging.getLogger(name)
+    if name in logger_initialized:
+        return logger
+
+    for logger_name in logger_initialized:
+        if name.startswith(logger_name):
+            return logger
+
+    formatter = logging.Formatter(
+        '[%(asctime)s] %(name)s %(levelname)s: %(message)s',
+        datefmt="%Y/%m/%d %H:%M:%S")
+
+    sh = logging.StreamHandler()
+    sh.setFormatter(formatter)
+    logger.addHandler(sh)
+    logger_initialized[name] = True
+    logger.propagate = False
+    return logger

funasr_onnx/vad_bin.py

@@ -0,0 +1,143 @@
+# -*- encoding: utf-8 -*-
+
+import os.path
+from pathlib import Path
+from typing import List, Union, Tuple
+
+import copy
+import librosa
+import numpy as np
+
+from .utils.utils import (ONNXRuntimeError,
+                          OrtInferSession, get_logger,
+                          read_yaml)
+from .utils.frontend import WavFrontend
+from .utils.e2e_vad import E2EVadModel
+
+logging = get_logger()
+
+
+class Fsmn_vad():
+	def __init__(self, model_dir: Union[str, Path] = None,
+	             batch_size: int = 1,
+	             device_id: Union[str, int] = "-1",
+	             quantize: bool = False,
+	             intra_op_num_threads: int = 4,
+	             max_end_sil: int = None,
+	             ):
+		
+		if not Path(model_dir).exists():
+			raise FileNotFoundError(f'{model_dir} does not exist.')
+		
+		model_file = os.path.join(model_dir, 'model.onnx')
+		if quantize:
+			model_file = os.path.join(model_dir, 'model_quant.onnx')
+		config_file = os.path.join(model_dir, 'vad.yaml')
+		cmvn_file = os.path.join(model_dir, 'vad.mvn')
+		config = read_yaml(config_file)
+		
+		self.frontend = WavFrontend(
+			cmvn_file=cmvn_file,
+			**config['frontend_conf']
+		)
+		self.ort_infer = OrtInferSession(model_file, device_id, intra_op_num_threads=intra_op_num_threads)
+		self.batch_size = batch_size
+		self.vad_scorer = E2EVadModel(config["vad_post_conf"])
+		self.max_end_sil = max_end_sil if max_end_sil is not None else config["vad_post_conf"]["max_end_silence_time"]
+		self.encoder_conf = config["encoder_conf"]
+	
+	def prepare_cache(self, in_cache: list = []):
+		if len(in_cache) > 0:
+			return in_cache
+		fsmn_layers = self.encoder_conf["fsmn_layers"]
+		proj_dim = self.encoder_conf["proj_dim"]
+		lorder = self.encoder_conf["lorder"]
+		for i in range(fsmn_layers):
+			cache = np.zeros((1, proj_dim, lorder-1, 1)).astype(np.float32)
+			in_cache.append(cache)
+		return in_cache
+		
+	
+	def __call__(self, audio_in: Union[str, np.ndarray, List[str]], **kwargs) -> List:
+		# waveform_list = self.load_data(audio_in, self.frontend.opts.frame_opts.samp_freq)
+		
+		param_dict = kwargs.get('param_dict', dict())
+		is_final = param_dict.get('is_final', False)
+		audio_in_cache = param_dict.get('audio_in_cache', None)
+		audio_in_cum = audio_in
+		if audio_in_cache is not None:
+			audio_in_cum = np.concatenate((audio_in_cache, audio_in_cum))
+		param_dict['audio_in_cache'] = audio_in_cum
+		feats, feats_len = self.extract_feat([audio_in_cum])
+		
+		in_cache = param_dict.get('in_cache', list())
+		in_cache = self.prepare_cache(in_cache)
+		beg_idx = param_dict.get('beg_idx',0)
+		# feats = feats[:, beg_idx:beg_idx+8, :]
+		param_dict['beg_idx'] = beg_idx + feats.shape[1]
+		try:
+			inputs = [feats]
+			inputs.extend(in_cache)
+			scores, out_caches = self.infer(inputs)
+			param_dict['in_cache'] = out_caches
+			segments = self.vad_scorer(scores, audio_in[None, :], is_final=is_final, max_end_sil=self.max_end_sil)
+			# print(segments)
+			if len(segments) == 1 and segments[0][0][1] != -1:
+				self.frontend.reset_status()
+			
+			
+		except ONNXRuntimeError:
+			logging.warning(traceback.format_exc())
+			logging.warning("input wav is silence or noise")
+			segments = []
+	
+		return segments
+
+	def load_data(self,
+	              wav_content: Union[str, np.ndarray, List[str]], fs: int = None) -> List:
+		def load_wav(path: str) -> np.ndarray:
+			waveform, _ = librosa.load(path, sr=fs)
+			return waveform
+		
+		if isinstance(wav_content, np.ndarray):
+			return [wav_content]
+		
+		if isinstance(wav_content, str):
+			return [load_wav(wav_content)]
+		
+		if isinstance(wav_content, list):
+			return [load_wav(path) for path in wav_content]
+		
+		raise TypeError(
+			f'The type of {wav_content} is not in [str, np.ndarray, list]')
+	
+	def extract_feat(self,
+	                 waveform_list: List[np.ndarray]
+	                 ) -> Tuple[np.ndarray, np.ndarray]:
+		feats, feats_len = [], []
+		for waveform in waveform_list:
+			speech, _ = self.frontend.fbank(waveform)
+			feat, feat_len = self.frontend.lfr_cmvn(speech)
+			feats.append(feat)
+			feats_len.append(feat_len)
+		
+		feats = self.pad_feats(feats, np.max(feats_len))
+		feats_len = np.array(feats_len).astype(np.int32)
+		return feats, feats_len
+	
+	@staticmethod
+	def pad_feats(feats: List[np.ndarray], max_feat_len: int) -> np.ndarray:
+		def pad_feat(feat: np.ndarray, cur_len: int) -> np.ndarray:
+			pad_width = ((0, max_feat_len - cur_len), (0, 0))
+			return np.pad(feat, pad_width, 'constant', constant_values=0)
+		
+		feat_res = [pad_feat(feat, feat.shape[0]) for feat in feats]
+		feats = np.array(feat_res).astype(np.float32)
+		return feats
+	
+	def infer(self, feats: List) -> Tuple[np.ndarray, np.ndarray]:
+		
+		outputs = self.ort_infer(feats)
+		scores, out_caches = outputs[0], outputs[1:]
+		return scores, out_caches
+	

models/.gitattributes

@@ -0,0 +1,3 @@
+punc_ct-transformer_zh-cn-common-vocab272727-pytorch/model.onnx filter=lfs diff=lfs merge=lfs -text
+speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/model.onnx filter=lfs diff=lfs merge=lfs -text
+speech_fsmn_vad_zh-cn-16k-common-pytorch/model.onnx filter=lfs diff=lfs merge=lfs -text

models/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/README.md

@@ -0,0 +1,241 @@
+---
+tasks:
+- punctuation
+domain:
+- audio
+model-type:
+- Classification
+frameworks:
+- pytorch
+metrics:
+- f1_score
+license: Apache License 2.0
+language: 
+- cn
+tags:
+- CT-Transformer
+- Alibaba
+- ICASSP 2020
+datasets:
+  train:
+  - 33M-samples online data
+  test:
+  - wikipedia data test
+  - 10000 industrial Mandarin sentences test
+widgets:
+  - task: punctuation
+    inputs:
+      - type: text
+        name: input
+        title: 文本
+    examples:
+      - name: 1
+        title: 示例1
+        inputs:
+          - name: input
+            data: 我们都是木头人不会讲话不会动
+    inferencespec:
+      cpu: 1 #CPU数量
+      memory: 4096
+---
+
+# Controllable Time-delay Transformer模型介绍
+
+[//]: # (Controllable Time-delay Transformer 模型是一种端到端标点分类模型。)
+
+[//]: # (常规的Transformer会依赖很远的未来信息，导致长时间结果不固定。Controllable Time-delay Transformer 在效果无损的情况下，有效控制标点的延时。)
+
+# Highlights
+- 中文标点通用模型：可用于语音识别模型输出文本的标点预测。
+  - 基于[Paraformer-large长音频模型](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary)场景的使用
+  - 基于[FunASR框架](https://github.com/alibaba-damo-academy/FunASR)，可进行ASR，VAD，标点的自由组合
+  - 基于纯文本输入的标点预测
+
+## Release Notes
+- 2023年3月（3月17号发布）：[funasr-0.2.0](https://github.com/alibaba-damo-academy/FunASR/tree/main)
+  - 功能完善：
+    - 优化[标点通用模型](https://www.modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/summary)，提升模型效果。
+
+- 2023年2月（2月17号发布）：[funasr-0.2.0](https://github.com/alibaba-damo-academy/FunASR/tree/main), modelscope-1.3.0
+  - 功能完善：
+    - 优化[标点通用模型](https://www.modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/summary)，增加标点召回和精度，修复缺少标点等问题。
+
+- 2023年1月（预计1月16号发布）：funasr-0.1.6, modelscope-1.1.4
+  - 模型功能完善：
+    - Modelscope模型推理pipeline，新增加多种输入音频方式，如wav.scp、音频bytes、音频采样点、WAV格式等。
+    - [Paraformer-large模型](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary)，新增加基于ModelScope微调定制模型，新增加batch级解码，加快推理速度。
+    - [AISHELL-1学术集Paraformer模型](https://modelscope.cn/models/damo/speech_paraformer_asr_nat-zh-cn-16k-aishell1-vocab4234-pytorch/summary)，
+    [AISHELL-1学术集ParaformerBert模型](https://modelscope.cn/models/damo/speech_paraformerbert_asr_nat-zh-cn-16k-aishell1-vocab4234-pytorch/summary)，
+    [AISHELL-1学术集Conformer模型](https://modelscope.cn/models/damo/speech_conformer_asr_nat-zh-cn-16k-aishell1-vocab4234-pytorch/summary)、
+    [AISHELL-2学术集Paraformer模型](https://www.modelscope.cn/models/damo/speech_paraformer_asr_nat-zh-cn-16k-aishell2-vocab5212-pytorch/summary)，
+    [AISHELL-2学术集ParaformerBert模型](https://www.modelscope.cn/models/damo/speech_paraformerbert_asr_nat-zh-cn-16k-aishell2-vocab5212-pytorch/summary)、
+    [AISHELL-2学术集Conformer模型](https://www.modelscope.cn/models/damo/speech_conformer_asr_nat-zh-cn-16k-aishell2-vocab5212-pytorch/summary)，
+    新增加基于ModelScope微调定制模型，其中，Paraformer与ParaformerBert模型新增加batch级解码，加快推理速度。
+  - 上线新模型：
+    - [Paraformer-large长音频模型](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary)，集成VAD、ASR、标点与时间戳功能，可直接对时长为数小时音频进行识别，并输出带标点文字与时间戳。
+    - [中文无监督预训练Data2vec模型](https://www.modelscope.cn/models/damo/speech_data2vec_pretrain-zh-cn-aishell2-16k-pytorch/summary)，采用Data2vec结构，基于AISHELL-2数据的中文无监督预训练模型，支持ASR或者下游任务微调模型。
+    - [语音端点检查VAD模型](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/summary)，可用于检测长语音片段中有效语音的起止时间点。
+    - [中文标点预测通用模型](https://www.modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/summary)，可用于语音识别模型输出文本的标点预测。
+    - [8K UniASR流式模型](https://www.modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/summary)，[8K UniASR模型](https://www.modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline/summary)，一种流式与离线一体化语音识别模型，进行流式语音识别的同时，能够以较低延时输出离线识别结果来纠正预测文本。
+    - Paraformer-large基于[AISHELL-1微调模型](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-aishell1-vocab8404-pytorch/summary)、[AISHELL-2微调模型](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-aishell2-vocab8404-pytorch/summary)，将Paraformer-large模型分别基于AISHELL-1与AISHELL-2数据微调。
+    - [说话人确认模型](https://www.modelscope.cn/models/damo/speech_xvector_sv-zh-cn-cnceleb-16k-spk3465-pytorch/summary) ，可用于说话人确认，也可以用来做说话人特征提取。
+    - [小尺寸设备端Paraformer指令词模型](https://www.modelscope.cn/models/damo/speech_paraformer-tiny-commandword_asr_nat-zh-cn-16k-vocab544-pytorch/summary)，Paraformer-tiny指令词版本，使用小参数量模型支持指令词识别。
+  - 将原TensorFlow模型升级为Pytorch模型，进行推理，并支持微调定制，包括：
+    - 16K 模型：[Paraformer中文](https://modelscope.cn/models/damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8358-tensorflow1/summary)、
+    [Paraformer-large中文](https://modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8358-tensorflow1/summary)、
+    [UniASR中文](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-16k-common-vocab8358-tensorflow1-offline/summary)、
+    [UniASR-large中文](https://modelscope.cn/models/damo/speech_UniASR-large_asr_2pass-zh-cn-16k-common-vocab8358-tensorflow1-offline/summary)、
+    [UniASR中文流式模型](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-16k-common-vocab8358-tensorflow1-online/summary)、
+    [UniASR方言](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-cn-dialect-16k-vocab8358-tensorflow1-offline/summary)、
+    [UniASR方言流式模型](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-cn-dialect-16k-vocab8358-tensorflow1-online/summary)、
+    [UniASR日语](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-ja-16k-common-vocab93-tensorflow1-offline/summary)、
+    [UniASR日语流式模型](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-ja-16k-common-vocab93-tensorflow1-online/summary)、
+    [UniASR印尼语](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-id-16k-common-vocab1067-tensorflow1-offline/summary)、
+    [UniASR印尼语流式模型](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-id-16k-common-vocab1067-tensorflow1-online/summary)、
+    [UniASR葡萄牙语](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-pt-16k-common-vocab1617-tensorflow1-offline/summary)、
+    [UniASR葡萄牙语流式模型](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-pt-16k-common-vocab1617-tensorflow1-online/summary)、
+    [UniASR英文](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-en-16k-common-vocab1080-tensorflow1-offline/summary)、
+    [UniASR英文流式模型](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-en-16k-common-vocab1080-tensorflow1-online/summary)、
+    [UniASR俄语](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-ru-16k-common-vocab1664-tensorflow1-offline/summary)、
+    [UniASR俄语流式模型](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-ru-16k-common-vocab1664-tensorflow1-online/summary)、
+    [UniASR韩语](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-ko-16k-common-vocab6400-tensorflow1-offline/summary)、
+    [UniASR韩语流式模型](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-ko-16k-common-vocab6400-tensorflow1-online/summary)、
+    [UniASR西班牙语](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-es-16k-common-vocab3445-tensorflow1-offline/summary)、
+    [UniASR西班牙语流式模型](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-es-16k-common-vocab3445-tensorflow1-online/summary)、
+    [UniASR粤语简体](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-cantonese-CHS-16k-common-vocab1468-tensorflow1-offline/files)、
+    [UniASR粤语简体流式模型](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-cantonese-CHS-16k-common-vocab1468-tensorflow1-online/files)、
+    - 8K 模型：[Paraformer中文](https://modelscope.cn/models/damo/speech_paraformer_asr_nat-zh-cn-8k-common-vocab8358-tensorflow1/summary)、
+    [UniASR中文](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab8358-tensorflow1-offline/summary)、
+    [UniASR中文流式模型](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab8358-tensorflow1-offline/summary)
+
+- 2022年11月：funasr-0.1.4, modelscope-1.1.3
+  - Paraformer-large非自回归模型上线，多个公开数据集上取得SOTA效果，FunASR框架：
+    - 支持基于ModelScope推理。
+    - 支持基于[FunASR框架](https://github.com/alibaba-damo-academy/FunASR)微调和推理。
+
+## 项目介绍
+
+Controllable Time-delay Transformer是达摩院语音团队提出的高效后处理框架中的标点模块���本项目为中文通用标点模型，模型可以被应用于文本类输入的标点预测，也可应用于语音识别结果的后处理步骤，协助语音识别模块输出具有可读性的文本结果。
+
+<p align="center">
+<img src="fig/struct.png" alt="Controllable Time-delay Transformer模型结构"  width="500" />
+
+Controllable Time-delay Transformer 模型结构如上图所示，由 Embedding、Encoder 和 Predictor 三部分组成。Embedding 是词向量叠加位置向量。Encoder可以采用不同的网络结构，例如self-attention，conformer，SAN-M等。Predictor 预测每个token后的标点类型。
+
+在模型的选择上采用了性能优越的Transformer模型。Transformer模型在获得良好性能的同时，由于模型自身序列化输入等特性，会给系统带来较大时延。常规的Transformer可以看到未来的全部信息，导致标点会依赖很远的未来信息。这会给用户带来一种标点一直在变化刷新，长时间结果不固定的不良感受。基于这一问题，我们创新性的提出了可控时延的Transformer模型（Controllable Time-Delay Transformer, CT-Transformer），在模型性能无损失的情况下，有效控制标点的延时。
+
+更详细的细节见：
+- 论文： [CONTROLLABLE TIME-DELAY TRANSFORMER FOR REAL-TIME PUNCTUATION PREDICTION AND DISFLUENCY DETECTION](https://arxiv.org/pdf/2003.01309.pdf)
+
+## 如何使用与训练自己的模型
+
+本项目提供的预训练模型是基于大数据训练的通用领域识别模型，开发者可以基于此模型进一步利用ModelScope的微调功能或者本项目对应的Github代码仓库[FunASR](https://github.com/alibaba-damo-academy/FunASR)进一步进行模型的领域定制化。
+
+### 在Notebook中开发
+
+对于有开发需求的使用者，特别推荐您使用Notebook进行离线处理。先登录ModelScope账号，点击模型页面右上角的“在Notebook中打开”按钮出现对话框，首次使用会提示您关联阿里云账号，按提示操作即可。关联账号后可进入选择启动实例界面，选择计算资源，建立实例，待实例创建完成后进入开发环境，进行调用。
+
+
+#### 基于ModelScope进行推理
+
+以下为三种支持格式及api调用方式参考如下范例：
+- text.scp文件路径，例如example/punc_example.txt，格式为： key + "\t" + value
+```sh
+cat example/punc_example.txt
+1       跨境河流是养育沿岸人民的生命之源
+2       从存储上来说仅仅是全景图片它就会是图片的四倍的容量
+3       那今天的会就到这里吧happy new year明年见
+```
+```python
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+inference_pipline = pipeline(
+    task=Tasks.punctuation,
+    model='damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch',
+    model_revision="v1.1.7")
+
+rec_result = inference_pipline(text_in='example/punc_example.txt')
+print(rec_result)
+```
+- text二进制数据，例如：用户直接从文件里读出bytes数据
+```python
+rec_result = inference_pipline(text_in='我们都是木头人不会讲话不会动')
+```
+- text文件url，例如：https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_text/punc_example.txt
+```python
+rec_result = inference_pipline(text_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_text/punc_example.txt')
+```
+
+
+#### 基于ModelScope进行微调
+待开发
+
+
+### 在本地机器中开发
+
+#### 基于ModelScope进行微调和推理
+
+支持基于ModelScope上数据集及私有数据集进行定制微调和推理，使用方式同Notebook中开发。
+
+#### 基于FunASR进行微调和推理
+
+FunASR框架支持魔搭社区开源的工业级的语音识别模型的training & finetuning，使得研究人员和开发者可以更加便捷的进行语音识别模型的研究和生产，目前已在Github开源：https://github.com/alibaba-damo-academy/FunASR
+
+#### FunASR框架安装
+
+- 安装FunASR和ModelScope
+
+```sh
+pip install "modelscope[audio]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
+git clone https://github.com/alibaba/FunASR.git
+cd FunASR
+pip install --editable ./
+```
+
+
+#### 基于FunASR进行推理
+
+接下来会以私有数据集为例，介绍如何在FunASR框架中使用本模型进行推理以及微调。
+
+```sh
+cd egs_modelscope/punctuation/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/
+python infer.py
+```
+
+#### 基于FunASR进行微调
+待开发
+
+## Benchmark
+中文标点预测通用模型在自采集的通用领域业务场景数据上有良好效果。训练数据大约33M个sample，每个sample可能包含1句或多句。
+
+### 自采集数据（20000+ samples）
+
+| precision                            | recall                                | f1_score                              |
+|:------------------------------------:|:-------------------------------------:|:-------------------------------------:|
+| <div style="width: 150pt">53.8</div> | <div style="width: 150pt">60.0</div>  | <div style="width: 150pt">56.5</div>  | 
+
+## 使用方式以及适用范围
+
+运行范围
+- 现阶段只能在Linux-x86_64运行，不支持Mac和Windows。
+
+使用方式
+- 直接推理：可以直接对输入文本进行计算，输出带有标点的目标文字。
+
+使用范围与目标场景
+- 适合对文本数据进行标点预测，文本长度不限。
+
+## 相关论文以及引用信息
+
+```BibTeX
+@inproceedings{chen2020controllable,
+  title={Controllable Time-Delay Transformer for Real-Time Punctuation Prediction and Disfluency Detection},
+  author={Chen, Qian and Chen, Mengzhe and Li, Bo and Wang, Wen},
+  booktitle={ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+  pages={8069--8073},
+  year={2020},
+  organization={IEEE}
+}
+```
+

models/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/configuration.json

@@ -0,0 +1,20 @@
+{
+  "framework": "pytorch",
+  "task" : "punctuation",
+  "model" : {
+    "type" : "generic-punc",
+    "punc_model_name" : "punc.pb",
+    "punc_model_config" : {
+      "type": "pytorch",
+      "code_base": "funasr",
+      "mode": "punc",
+      "lang": "zh-cn",
+      "batch_size": 1,
+      "punc_config": "punc.yaml",
+      "model": "damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch"
+    }
+  },
+  "pipeline": {
+    "type":"punc-inference"
+  }
+}

models/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/model.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a89e15b0cdc3426a1d692658bbf4b63d3aeeba7235e2dbf5b3369f9a6767182b
+size 292073789

models/speech_fsmn_vad_zh-cn-16k-common-pytorch/README.md

@@ -0,0 +1,260 @@
+---
+tasks:
+- voice-activity-detection
+domain:
+- audio
+model-type:
+- VAD model
+frameworks:
+- pytorch
+backbone:
+- fsmn
+metrics:
+- f1_score
+license: Apache License 2.0
+language: 
+- cn
+tags:
+- FSMN
+- Alibaba
+- Online
+datasets:
+  train:
+  - 20,000 hour industrial Mandarin task
+  test:
+  - 20,000 hour industrial Mandarin task
+widgets:
+  - task: voice-activity-detection
+    inputs:
+      - type: audio
+        name: input
+        title: 音频
+    examples:
+      - name: 1
+        title: 示例1
+        inputs:
+          - name: input
+            data: git://example/vad_example.wav 
+    inferencespec:
+      cpu: 1 #CPU数量
+      memory: 4096
+---
+
+# FSMN-Monophone VAD 模型介绍
+
+[//]: # (FSMN-Monophone VAD 模型)
+
+## Highlight
+- 16k中文通用VAD模型：可用于检测长语音片段中有效语音的起止时间点。
+  - 基于[Paraformer-large长音频模型](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary)场景的使用
+  - 基于[FunASR框架](https://github.com/alibaba-damo-academy/FunASR)，可进行ASR，VAD，[中文标点](https://www.modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/summary)的自由组合
+  - 基于音频数据的有效语音片段起止时间点检测
+
+## Release Notes
+
+
+- 2023年1月（预计1月16号发布）：funasr-0.1.6, modelscope-1.1.4
+  - 模型功能完善：
+    - Modelscope模型推理pipeline，新增加多种输入音频方式，如wav.scp、音频bytes、音频采样点、WAV格式等。
+    - [Paraformer-large模型](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary)，新增加基于ModelScope微调定制模型，新增加batch级解码，加快推理速度。
+    - [AISHELL-1学术集Paraformer模型](https://modelscope.cn/models/damo/speech_paraformer_asr_nat-zh-cn-16k-aishell1-vocab4234-pytorch/summary)，
+    [AISHELL-1学术集ParaformerBert模型](https://modelscope.cn/models/damo/speech_paraformerbert_asr_nat-zh-cn-16k-aishell1-vocab4234-pytorch/summary)，
+    [AISHELL-1学术集Conformer模型](https://modelscope.cn/models/damo/speech_conformer_asr_nat-zh-cn-16k-aishell1-vocab4234-pytorch/summary)、
+    [AISHELL-2学术集Paraformer模型](https://www.modelscope.cn/models/damo/speech_paraformer_asr_nat-zh-cn-16k-aishell2-vocab5212-pytorch/summary)，
+    [AISHELL-2学术集ParaformerBert模型](https://www.modelscope.cn/models/damo/speech_paraformerbert_asr_nat-zh-cn-16k-aishell2-vocab5212-pytorch/summary)、
+    [AISHELL-2学术集Conformer模型](https://www.modelscope.cn/models/damo/speech_conformer_asr_nat-zh-cn-16k-aishell2-vocab5212-pytorch/summary)，
+    新增加基于ModelScope微调定制模型，其中，Paraformer与ParaformerBert模型新增加batch级解码，加快推理速度。
+  - 上线新模型：
+    - [Paraformer-large长音频模型](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary)，集成VAD、ASR、标点与时间戳功能，可直接对时长为数小时音频进行识别，并输出带标点文字与时间戳。
+    - [中文无监督预训练Data2vec模型](https://www.modelscope.cn/models/damo/speech_data2vec_pretrain-zh-cn-aishell2-16k-pytorch/summary)，采用Data2vec结构，基于AISHELL-2数据的中文无监督预训练模型，支持ASR或者下游任务微调模型。
+    - [语音端点检查VAD模型](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/summary)，可用于检测长语音片段中有效语音的起止时间点。
+    - [中文标点预测通用模型](https://www.modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/summary)，可用于语音识别模型输出文本的标点预测。
+    - [8K UniASR流式模型](https://www.modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/summary)，[8K UniASR模型](https://www.modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline/summary)，一种流式与离线一体化语音识别模型，进行流式语音识别的同时，能够以较低延时输出离线识别结果来纠正预测文本。
+    - Paraformer-large基于[AISHELL-1微调模型](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-aishell1-vocab8404-pytorch/summary)、[AISHELL-2微调模型](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-aishell2-vocab8404-pytorch/summary)，将Paraformer-large模型分别基于AISHELL-1与AISHELL-2数据微调。
+    - [说话人确认模型](https://www.modelscope.cn/models/damo/speech_xvector_sv-zh-cn-cnceleb-16k-spk3465-pytorch/summary) ，可用于说话人确认，也可以用来做说话人特征提取。
+    - [小尺寸设备端Paraformer指令词模型](https://www.modelscope.cn/models/damo/speech_paraformer-tiny-commandword_asr_nat-zh-cn-16k-vocab544-pytorch/summary)，Paraformer-tiny指令词版本，使用小参数量模型支持指令词识别。
+  - 将原TensorFlow模型升级为Pytorch模型，进行推理，并支持微调定制，包括：
+    - 16K 模型：[Paraformer中文](https://modelscope.cn/models/damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8358-tensorflow1/summary)、
+    [Paraformer-large中文](https://modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8358-tensorflow1/summary)、
+    [UniASR中文](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-16k-common-vocab8358-tensorflow1-offline/summary)、
+    [UniASR-large中文](https://modelscope.cn/models/damo/speech_UniASR-large_asr_2pass-zh-cn-16k-common-vocab8358-tensorflow1-offline/summary)、
+    [UniASR中文流式模型](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-16k-common-vocab8358-tensorflow1-online/summary)、
+    [UniASR方言](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-cn-dialect-16k-vocab8358-tensorflow1-offline/summary)、
+    [UniASR方言流式模型](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-cn-dialect-16k-vocab8358-tensorflow1-online/summary)、
+    [UniASR日语](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-ja-16k-common-vocab93-tensorflow1-offline/summary)、
+    [UniASR日语流式模型](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-ja-16k-common-vocab93-tensorflow1-online/summary)、
+    [UniASR印尼语](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-id-16k-common-vocab1067-tensorflow1-offline/summary)、
+    [UniASR印尼语流式模型](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-id-16k-common-vocab1067-tensorflow1-online/summary)、
+    [UniASR葡萄牙语](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-pt-16k-common-vocab1617-tensorflow1-offline/summary)、
+    [UniASR葡萄牙语流式模型](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-pt-16k-common-vocab1617-tensorflow1-online/summary)、
+    [UniASR英文](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-en-16k-common-vocab1080-tensorflow1-offline/summary)、
+    [UniASR英文流式模型](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-en-16k-common-vocab1080-tensorflow1-online/summary)、
+    [UniASR俄语](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-ru-16k-common-vocab1664-tensorflow1-offline/summary)、
+    [UniASR俄语流式模型](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-ru-16k-common-vocab1664-tensorflow1-online/summary)、
+    [UniASR韩语](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-ko-16k-common-vocab6400-tensorflow1-offline/summary)、
+    [UniASR韩语流式模型](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-ko-16k-common-vocab6400-tensorflow1-online/summary)、
+    [UniASR西班牙语](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-es-16k-common-vocab3445-tensorflow1-offline/summary)、
+    [UniASR西班牙语流式模型](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-es-16k-common-vocab3445-tensorflow1-online/summary)、
+    [UniASR粤语简体](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-cantonese-CHS-16k-common-vocab1468-tensorflow1-offline/files)、
+    [UniASR粤语简体流式模型](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-cantonese-CHS-16k-common-vocab1468-tensorflow1-online/files)、
+    - 8K 模型：[Paraformer中文](https://modelscope.cn/models/damo/speech_paraformer_asr_nat-zh-cn-8k-common-vocab8358-tensorflow1/summary)、
+    [UniASR中文](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab8358-tensorflow1-offline/summary)、
+    [UniASR中文流式模型](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab8358-tensorflow1-offline/summary)
+
+- 2022年11月：funasr-0.1.4, modelscope-1.1.3
+  - Paraformer-large非自回归模型上线，多个公开数据集上取得SOTA效果，FunASR框架：
+    - 支持基于ModelScope推理。
+    - 支持基于[FunASR框架](https://github.com/alibaba-damo-academy/FunASR)微调和推理。
+
+## 项目介绍
+
+FSMN-Monophone VAD是达摩院语音团队提出的高效语音端点检测模型，用于检测输入音频中有效语音的起止时间点信息，并将检测出来的有效音频片段输入识别引擎进行识别，减少无效语音带来的识别错误。
+
+<p align="center">
+<img src="fig/struct.png" alt="VAD模型结构"  width="500" />
+
+FSMN-Monophone VAD模型结构如上图所示：模型结构层面，FSMN模型结构建模时可考虑上下文信息，训练和推理速度快，且时延可控；同时根据VAD模型size以及低时延的要求，对FSMN的网络结构、右看帧数进行了适配。在建模单元层面，speech信息比较丰富，仅用单类来表征学习能力有限，我们将单一speech类升级为Monophone。建模单元细分，可以避免参数平均，抽象学习能力增强，区分性更好。
+
+
+## 如何使用与训练自己的模型
+
+
+本项目提供的预训练模型是基于大数据训练的通用领域VAD模型，开发者可以基于此模型进一步利用ModelScope的微调功能或者本项目对应的Github代码仓库[FunASR](https://github.com/alibaba-damo-academy/FunASR)进一步进行模型的效果优化。
+
+### 在Notebook中开发
+
+对于有开发需求的使用者，特别推荐您使用Notebook进行离线处理。先登录ModelScope账号，点击模型页面右上角的“在Notebook中打开”按钮出现对话框，首次使用会提示您关联阿里云账号，按提示操作即可。关联账号后可进入选择启动实例界面，选择计算资源，建立实例，待实例创建完成后进入开发环境，输入api调用实例。
+
+#### 基于ModelScope进行推理
+
+- 推理支持音频格式如下：
+  - wav文件路径，例如：data/test/audios/vad_example.wav
+  - wav文件url，例如：https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav
+  - wav二进制数据，格式bytes，例如：用户直接从文件里读出bytes数据或者是麦克风录出bytes数据。
+  - 已解析的audio音频，例如：audio, rate = soundfile.read("vad_example_zh.wav")，类型为numpy.ndarray或者torch.Tensor。
+  - wav.scp文件，需符合如下要求：
+
+```sh
+cat wav.scp
+vad_example1  data/test/audios/vad_example1.wav
+vad_example2  data/test/audios/vad_example2.wav
+...
+```
+
+- 若输入格式wav文件url，api调用方式可参考如下范例：
+
+```python
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+inference_pipeline = pipeline(
+    task=Tasks.voice_activity_detection,
+    model='damo/speech_fsmn_vad_zh-cn-16k-common-pytorch',
+    model_revision='v1.1.8',
+)
+
+segments_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav')
+print(segments_result)
+```
+
+- 输入音频为pcm格式，调用api时需要传入音频采样率参数audio_fs，例如：
+
+```python
+segments_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.pcm', audio_fs=16000)
+```
+
+- 若输入格式为文件wav.scp(注：文件名需要以.scp结尾)，可添加 output_dir 参数将识别结果写入文件中，参考示例如下：
+
+```python
+inference_pipeline = pipeline(
+    task=Tasks.voice_activity_detection,
+    model='damo/speech_fsmn_vad_zh-cn-16k-common-pytorch',
+    model_revision='v1.1.8',
+    output_dir='./output_dir',
+)
+
+inference_pipeline(audio_in="wav.scp")
+```
+识别结果输出路径结构如下：
+
+```sh
+tree output_dir/
+output_dir/
+└── 1best_recog
+    └── text
+
+1 directory, 1 files
+```
+text：VAD检测语音起止时间点结果文件（单位：ms）
+
+- 若输入音频为已解析的audio音频，api调用方式可参考如下范例：
+
+```python
+import soundfile
+
+waveform, sample_rate = soundfile.read("vad_example_zh.wav")
+segments_result = inference_pipeline(audio_in=waveform)
+print(segments_result)
+```
+
+- VAD常用参数调整说明（参考：vad.yaml文件）：
+  - max_end_silence_time：尾部连续检测到多长时间静音进行尾点判停，参数范围500ms～6000ms，默认值800ms(该值过低容易出现语音提前截断的情况)。
+  - speech_noise_thres：speech的得分减去noise的得分大于此值则判断为speech，参数范围：（-1,1）
+    - 取值越趋于-1，噪音被误判定为语音的概率越大，FA越高
+    - 取值越趋于+1，语音被误判定为噪音的概率越大，Pmiss越高
+    - 通常情况下，该值会根据当前模型在长语音测试集上的效果取balance
+    
+#### 基于ModelScope进行微调
+
+待开发
+
+### 在本地机器中开发
+
+#### 基于ModelScope进行微调和推理
+
+支持基于ModelScope上数据集及私有数据集进行定制微调和推理，使用方式同Notebook中开发。
+
+#### 基于FunASR进行微调和推理
+
+FunASR框架支持魔搭社区开源的工业级的语音识别模型的training & finetuning，使得研究人员和开发者可以更加便捷的进行语音识别模型的研究和生产，目前已在Github开源：https://github.com/alibaba-damo-academy/FunASR
+
+#### FunASR框架安装
+
+- 安装FunASR和ModelScope
+
+```sh
+pip install "modelscope[audio_asr]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
+git clone https://github.com/alibaba/FunASR.git
+cd FunASR
+pip install --editable ./
+```
+
+#### 基于FunASR进行推理
+
+接下来会以私有数据集为例，介绍如何在FunASR框架中使用VAD上进行推理。
+
+```sh
+cd egs_modelscope/vad/speech_fsmn_vad_zh-cn-16k-common/
+python infer.py
+```
+
+## 使用方式以及适用范围
+
+运行范围
+- 现阶段只能在Linux-x86_64运行，不支持Mac和Windows。
+
+使用方式
+- 直接推理：可以直接对长语音数据进行计算，有效语音片段的起止时间点信息（单位：ms）。
+
+## 相关论文以及引用信息
+
+```BibTeX
+@inproceedings{zhang2018deep,
+  title={Deep-FSMN for large vocabulary continuous speech recognition},
+  author={Zhang, Shiliang and Lei, Ming and Yan, Zhijie and Dai, Lirong},
+  booktitle={2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+  pages={5869--5873},
+  year={2018},
+  organization={IEEE}
+}
+```

models/speech_fsmn_vad_zh-cn-16k-common-pytorch/configuration.json

@@ -0,0 +1,22 @@
+{
+  "framework": "pytorch",
+  "task" : "voice-activity-detection",
+  "model" : {
+    "type" : "generic-asr",
+    "am_model_name": "vad.pb",
+    "model_config" : {
+      "type": "pytorch",
+      "code_base": "funasr",
+      "mode": "offline",
+      "lang": "zh-cn",
+      "batch_size": 1,
+      "vad_model_name": "vad.pb",
+      "vad_model_config": "vad.yaml",
+      "vad_mvn_file": "vad.mvn",
+      "model": "damo/speech_fsmn_vad_zh-cn-16k-common-pytorch"
+    }
+  },
+  "pipeline": {
+      "type":"vad-inference"
+  }
+}

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+</Nnet>

models/speech_fsmn_vad_zh-cn-16k-common-pytorch/vad.yaml

@@ -0,0 +1,55 @@
+input_size: null 
+frontend: wav_frontend
+frontend_conf:
+    fs: 16000
+    window: hamming
+    n_mels: 80
+    frame_length: 25
+    frame_shift: 10
+    dither: 0.0
+    lfr_m: 5
+    lfr_n: 1
+
+model: e2evad 
+encoder: fsmn
+encoder_conf:
+    input_dim: 400
+    input_affine_dim: 140
+    fsmn_layers: 4
+    linear_dim: 250
+    proj_dim: 128
+    lorder: 20
+    rorder: 0
+    lstride: 1
+    rstride: 0
+    output_affine_dim: 140
+    output_dim: 248
+
+vad_post_conf:
+    sample_rate: 16000
+    detect_mode: 1 
+    snr_mode: 0
+    max_end_silence_time: 1000
+    max_start_silence_time: 3000
+    do_start_point_detection: True
+    do_end_point_detection: True
+    window_size_ms: 200
+    sil_to_speech_time_thres: 150
+    speech_to_sil_time_thres: 150
+    speech_2_noise_ratio: 1.0
+    do_extend: 1
+    lookback_time_start_point: 200
+    lookahead_time_end_point: 100
+    max_single_segment_time: 60000
+    snr_thres: -100.0
+    noise_frame_num_used_for_snr: 100
+    decibel_thres: -100.0
+    speech_noise_thres: 0.6
+    fe_prior_thres: 0.0001
+    silence_pdf_num: 1
+    sil_pdf_ids: [0]
+    speech_noise_thresh_low: -0.1
+    speech_noise_thresh_high: 0.3
+    output_frame_probs: False
+    frame_in_ms: 10
+    frame_length_ms: 25

models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/README.md

@@ -0,0 +1,503 @@
+---
+tasks:
+- auto-speech-recognition
+domain:
+- audio
+model-type:
+- Non-autoregressive
+frameworks:
+- pytorch
+backbone:
+- transformer/conformer
+metrics:
+- CER
+license: Apache License 2.0
+language: 
+- cn
+tags:
+- Paraformer
+- Alibaba
+- INTERSPEECH 2022
+datasets:
+  train:
+  - 60,000 hour industrial Mandarin task
+  test:
+  - AISHELL-1 dev/test
+  - AISHELL-2 dev_android/dev_ios/dev_mic/test_android/test_ios/test_mic
+  - WentSpeech dev/test_meeting/test_net
+  - SpeechIO TIOBE
+  - 60,000 hour industrial Mandarin task
+indexing:
+   results:
+   - task:
+       name: Automatic Speech Recognition
+     dataset:
+       name: 60,000 hour industrial Mandarin task
+       type: audio    # optional
+       args: 16k sampling rate, 8404 characters  # optional
+     metrics:
+       - type: CER
+         value: 8.53%  # float
+         description: greedy search, withou lm, avg.
+         args: default
+       - type: RTF
+         value: 0.0251  # float
+         description: GPU inference on V100
+         args: batch_size=1
+widgets:
+  - task: auto-speech-recognition
+    inputs:
+      - type: audio
+        name: input
+        title: 音频
+    examples:
+      - name: 1
+        title: 示例1
+        inputs:
+          - name: input
+            data: git://example/asr_example.wav
+    inferencespec:
+      cpu: 8 #CPU数量
+      memory: 4096
+finetune-support: True
+---
+
+
+# Paraformer-large模型介绍
+
+## Highlights
+- 热词版本：[Paraformer-large热词版模型](https://www.modelscope.cn/models/damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/summary)支持热词定制功能，基于提供的热词列表进行激励增强，提升热词的召回率和准确率。
+- 长音频版本：[Paraformer-large长音频模型](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary)，集成VAD、ASR、标点与时间戳功能，可直接对时长为数小时音频进行识别，并输出带标点文字与时间戳。
+
+## Release Notes
+
+- 2023年2月（2月17号发布）：[funasr-0.2.0](https://github.com/alibaba-damo-academy/FunASR/tree/main), modelscope-1.3.0
+  - 功能完善：
+    - 新增加模型导出功能，Modelscope中所有Paraformer模型与本地finetune模型，支持一键导出[onnx格式模型与torchscripts格式模型](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/export)，用于模型部署。
+    - 新增加Paraformer模型[onnxruntime部署功能](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime/python/onnxruntime/paraformer/rapid_paraformer)，无须安装Modelscope与FunASR，即可部署，cpu实测，onnxruntime推理速度提升近3倍(rtf: 0.110->0.038)。
+    - 新增加[grpc服务功能](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime/python/grpc)，支持对Modelscope推理pipeline进行服务部署，也支持对onnxruntime进行服务部署。
+    - 优化[Paraformer-large长音频模型](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary)时间戳，对badcase时间戳预测准确率有较大幅度提升，平均首尾时间戳偏移74.7ms，[详见论文](https://arxiv.org/abs/2301.12343)。
+    - 新增加任意VAD模型、ASR模型与标点模型自由组合功能，可以自由组合Modelscope中任意模型以及本地finetune后的模型进行推理，[用法示例](https://github.com/alibaba-damo-academy/FunASR/discussions/134)。
+    - 优化[标点通用模型](https://www.modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/summary)，增加标点召回和精度，修复缺少标点
+等问题。
+    - 新增加采样率自适应功能，任意输入采样率音频会自动匹配到模型采样率；新增加多种语音格式支持，如，mp3、flac、ogg、opus等。
+
+  - 上线新模型：
+    - [Paraformer-large热词模型](https://www.modelscope.cn/models/damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/summary)，可实现热词定制化，基于提供的热词列表，对热词进行激励增强，提升模型对热词的召回。
+    - [MFCCA多通道多说话人识别模型](https://www.modelscope.cn/models/NPU-ASLP/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/summary)，与西工大音频语音与语言处理研究组合作论文，一种基于多帧跨通道注意力机制的多通道语音识别模型。
+    - [8k语音端点检测VAD模型](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-8k-common/summary)，可用于检测长语音片段中有效语音的起止时间点，支持流式输入，最小支持10ms语音输入流。
+    - UniASR流式离线一体化模型: 
+    [16k UniASR闽南语](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825/summary)、
+    [16k UniASR法语](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-fr-16k-common-vocab3472-tensorflow1-online/summary)、
+    [16k UniASR德语](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-de-16k-common-vocab3690-tensorflow1-online/summary)、
+    [16k UniASR越南语](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-vi-16k-common-vocab1001-pytorch-online/summary)、
+    [16k UniASR波斯语](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-fa-16k-common-vocab1257-pytorch-online/summary)。
+    - [基于Data2vec结构无监督预训练Paraformer模型](https://www.modelscope.cn/models/damo/speech_data2vec_pretrain-paraformer-zh-cn-aishell2-16k/summary)，采用Data2vec无监督预训练初值模型，在AISHELL-1数据中finetune Paraformer模型。
+
+- 2023年1月（1月16号发布）：[funasr-0.1.6](https://github.com/alibaba-damo-academy/FunASR/tree/main), modelscope-1.2.0
+  - 上线新模型：
+    - [Paraformer-large长音频模型](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary)，集成VAD、ASR、标点与时间戳功能，可直接对时长为数小时音频进行识别，并输出带标点文字与时间戳。
+    - [中文无监督预训练Data2vec模型](https://www.modelscope.cn/models/damo/speech_data2vec_pretrain-zh-cn-aishell2-16k-pytorch/summary)，采用Data2vec结构，基于AISHELL-2数据的中文无监督预训练模型，支持ASR或者下游任务微调模型。
+    - [16k语音端点检测VAD模型](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/summary)，可用于检测长语音片段中有效语音的起止时间点。
+    - [中文标点预测通用模型](https://www.modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/summary)，可用于语音识别模型输出文本的标点预测。
+    - [8K UniASR流式模型](https://www.modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/summary)，[8K UniASR模型](https://www.modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline/summary)，一种流式与离线一体化语音识别模型，进行流式语音识别的同时，能够以较低延时输出离线识别结果来纠正预测文本。
+    - Paraformer-large基于[AISHELL-1微调模型](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-aishell1-vocab8404-pytorch/summary)、[AISHELL-2微调模型](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-aishell2-vocab8404-pytorch/summary)，将Paraformer-large模型分别基于AISHELL-1与AISHELL-2数据微调。
+    - [说话人确认模型](https://www.modelscope.cn/models/damo/speech_xvector_sv-zh-cn-cnceleb-16k-spk3465-pytorch/summary) ，可用于说话人确认，也可以用来做说话人特征提取。
+    - [小尺寸设备端Paraformer指令词模型](https://www.modelscope.cn/models/damo/speech_paraformer-tiny-commandword_asr_nat-zh-cn-16k-vocab544-pytorch/summary)，Paraformer-tiny指令词版本，使用小参数量模型支持指令词识别。
+  - 将原TensorFlow模型升级为Pytorch模型，进行推理，并支持微调定制，包括：
+    - 16K 模型：[Paraformer中文](https://modelscope.cn/models/damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8358-tensorflow1/summary)、
+    [Paraformer-large中文](https://modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8358-tensorflow1/summary)、
+    [UniASR中文](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-16k-common-vocab8358-tensorflow1-offline/summary)、
+    [UniASR-large中文](https://modelscope.cn/models/damo/speech_UniASR-large_asr_2pass-zh-cn-16k-common-vocab8358-tensorflow1-offline/summary)、
+    [UniASR中文流式模型](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-16k-common-vocab8358-tensorflow1-online/summary)、
+    [UniASR方言](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-cn-dialect-16k-vocab8358-tensorflow1-offline/summary)、
+    [UniASR方言流式模型](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-cn-dialect-16k-vocab8358-tensorflow1-online/summary)、
+    [UniASR日语](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-ja-16k-common-vocab93-tensorflow1-offline/summary)、
+    [UniASR日语流式模型](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-ja-16k-common-vocab93-tensorflow1-online/summary)、
+    [UniASR印尼语](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-id-16k-common-vocab1067-tensorflow1-offline/summary)、
+    [UniASR印尼语流式模型](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-id-16k-common-vocab1067-tensorflow1-online/summary)、
+    [UniASR葡萄牙语](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-pt-16k-common-vocab1617-tensorflow1-offline/summary)、
+    [UniASR葡萄牙语流式模型](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-pt-16k-common-vocab1617-tensorflow1-online/summary)、
+    [UniASR英文](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-en-16k-common-vocab1080-tensorflow1-offline/summary)、
+    [UniASR英文流式模型](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-en-16k-common-vocab1080-tensorflow1-online/summary)、
+    [UniASR俄语](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-ru-16k-common-vocab1664-tensorflow1-offline/summary)、
+    [UniASR俄语流式模型](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-ru-16k-common-vocab1664-tensorflow1-online/summary)、
+    [UniASR韩语](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-ko-16k-common-vocab6400-tensorflow1-offline/summary)、
+    [UniASR韩语流式模型](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-ko-16k-common-vocab6400-tensorflow1-online/summary)、
+    [UniASR西班牙语](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-es-16k-common-vocab3445-tensorflow1-offline/summary)、
+    [UniASR西班牙语流式模型](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-es-16k-common-vocab3445-tensorflow1-online/summary)、
+    [UniASR粤语简体](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-cantonese-CHS-16k-common-vocab1468-tensorflow1-offline/files)、
+    [UniASR粤语简体流式模型](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-cantonese-CHS-16k-common-vocab1468-tensorflow1-online/files)、
+    - 8K 模型：[Paraformer中文](https://modelscope.cn/models/damo/speech_paraformer_asr_nat-zh-cn-8k-common-vocab8358-tensorflow1/summary)、
+    [UniASR中文](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab8358-tensorflow1-offline/summary)、
+    [UniASR中文流式模型](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab8358-tensorflow1-offline/summary)
+
+[//]: # (  - 功能完善：)
+
+[//]: # (    - Modelscope模型推理pipeline，Paraformer模型新增加batch级解码；新增加多种输入音频方式，如wav.scp、音频bytes、音频采样点、WAV格式等。)
+
+[//]: # (    - 新增加基于ModelScope微调定制pipeline，其中，，加快推理速度。)
+
+[//]: # (    - [Paraformer-large模型](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary)，新增加基于ModelScope微调定制模型，新增加batch级解码，加快推理速度。)
+
+[//]: # (    - [AISHELL-1学术集Paraformer模型](https://modelscope.cn/models/damo/speech_paraformer_asr_nat-zh-cn-16k-aishell1-vocab4234-pytorch/summary)，)
+
+[//]: # (    [AISHELL-1学术集ParaformerBert模型](https://modelscope.cn/models/damo/speech_paraformerbert_asr_nat-zh-cn-16k-aishell1-vocab4234-pytorch/summary)，)
+
+[//]: # (    [AISHELL-1学术集Conformer模型](https://modelscope.cn/models/damo/speech_conformer_asr_nat-zh-cn-16k-aishell1-vocab4234-pytorch/summary)、)
+
+[//]: # (    [AISHELL-2学术集Paraformer模型](https://www.modelscope.cn/models/damo/speech_paraformer_asr_nat-zh-cn-16k-aishell2-vocab5212-pytorch/summary)，)
+
+[//]: # (    [AISHELL-2学术集ParaformerBert模型](https://www.modelscope.cn/models/damo/speech_paraformerbert_asr_nat-zh-cn-16k-aishell2-vocab5212-pytorch/summary)、)
+
+[//]: # (    [AISHELL-2学术集Conformer模型](https://www.modelscope.cn/models/damo/speech_conformer_asr_nat-zh-cn-16k-aishell2-vocab5212-pytorch/summary)，)
+[//]: # (    新增加基于ModelScope微调定制模型，其中，Paraformer与ParaformerBert模型新增加batch级解码，加快推理速度。)
+
+
+## 项目介绍
+
+Paraformer是达摩院语音团队提出的一种高效的非自回归端到端语音识别框架。本项目为Paraformer中文通用语音识别模型，采用工业级数万小时的标注音频进行模型训练，保证了模型的通用识别效果。模型可以被应用于语音输入法、语音导航、智能会议纪要等场景。
+
+<p align="center">
+<img src="fig/struct.png" alt="Paraformer模型结构"  width="500" />
+
+
+Paraformer模型结构如上图所示，由 Encoder、Predictor、Sampler、Decoder 与 Loss function 五部分组成。Encoder可以采用不同的网络结构，例如self-attention，conformer，SAN-M等。Predictor 为两层FFN，预测目标文字个数以及抽取目标文字对应的声学向量。Sampler 为无可学习参数模块，依据输入的声学向量和目标向量，生产含有语义的特征向量。Decoder 结构与自回归模型类似，为双向建模（自回归为单向建模）。Loss function 部分，除了交叉熵（CE）与 MWER 区分性优化目标，还包括了 Predictor 优化目标 MAE。
+
+
+其核心点主要有：  
+- Predictor 模块：基于 Continuous integrate-and-fire (CIF) 的 预测器 (Predictor) 来抽取目标文字对应的声学特征向量，可以更加准确的预测语音中目标文字个数。  
+- Sampler：通过采样，将声学特征向量与目标文字向量变换成含有语义信息的特征向量，配合双向的 Decoder 来增强模型对于上下文的建模能力。  
+- 基���负样本采样的 MWER 训练准则。  
+
+更详细的细节见：
+- 论文： [Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition](https://arxiv.org/abs/2206.08317)
+- 论文解读：[Paraformer: 高识别率、高计算效率的单轮非自回归端到端语音识别模型](https://mp.weixin.qq.com/s/xQ87isj5_wxWiQs4qUXtVw)
+
+
+## 如何使用与训练自己的模型
+
+本项目提供的预训练模型是基于大数据训练的通用领域识别模型，开发者可以基于此模型进一步利用ModelScope的微调功能或者本项目对应的Github代码仓库[FunASR](https://github.com/alibaba-damo-academy/FunASR)进一步进行模型的领域定制化。
+
+### 在Notebook中开发
+
+对于有开发需求的使用者，特别推荐您使用Notebook进行离线处理。先登录ModelScope账号，点击模型页面右上角的“在Notebook中打开”按钮出现对话框，首次使用会提示您关联阿里云账号，按提示操作即可。关联账号后可进入选择启动实例界面，选择计算资源，建立实例，待实例创建完成后进入开发环境，进行调用。
+
+#### 基于ModelScope进行推理
+
+- 推理支持音频格式如下：
+  - wav文件路径，例如：data/test/audios/asr_example.wav
+  - pcm文件路径，例如：data/test/audios/asr_example.pcm
+  - wav文件url，例如：https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav
+  - wav二进制数据，格式bytes，例如：用户直接从文件里读出bytes数据或者是麦克风录出bytes数据。
+  - 已解析的audio音频，例如：audio, rate = soundfile.read("asr_example_zh.wav")，类型为numpy.ndarray或者torch.Tensor。
+  - wav.scp文件，需符合如下要求：
+
+```sh
+cat wav.scp
+asr_example1  data/test/audios/asr_example1.wav
+asr_example2  data/test/audios/asr_example2.wav
+...
+```
+
+- 若输入格式wav文件url，api调用方式可参考如下范例：
+
+```python
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+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')
+print(rec_result)
+```
+
+- 输入音频为pcm格式，调用api时需要传入音频采样率参数audio_fs，例如：
+
+```python
+rec_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.pcm', audio_fs=16000)
+```
+
+- 输入音频为wav格式，api调用方式可参考如下范例:
+
+```python
+rec_result = inference_pipeline(audio_in='asr_example_zh.wav')
+```
+
+- 若输入格式为文件wav.scp(注：文件名需要以.scp结尾)，可添加 output_dir 参数将识别结果写入文件中，api调用方式可参考如下范例:
+
+```python
+inference_pipeline = pipeline(
+    task=Tasks.auto_speech_recognition,
+    model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch',
+    output_dir='./output_dir')
+
+inference_pipeline(audio_in="wav.scp")
+```
+识别结果输出路径结构如下：
+
+```sh
+tree output_dir/
+output_dir/
+└── 1best_recog
+    ├── rtf
+    ├── score
+    ├── text
+    └── time_stamp
+
+1 directory, 4 files
+```
+rtf：计算过程耗时统计
+
+score：识别路径得分
+
+text：语音识别结果文件
+
+time_stamp：时间戳结果文件
+
+- 若输入音频为已解析的audio音频，api调用方式可参考如下范例：
+
+```python
+import soundfile
+
+waveform, sample_rate = soundfile.read("asr_example_zh.wav")
+rec_result = inference_pipeline(audio_in=waveform)
+```
+
+- ASR、VAD、PUNC模型自由组合
+
+可根据使用需求对VAD和PUNC标点模型进行自由组合，使用方式如下：
+```python
+inference_pipeline = pipeline(
+    task=Tasks.auto_speech_recognition,
+    model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch',
+    vad_model='damo/speech_fsmn_vad_zh-cn-16k-common-pytorch',
+    vad_model_revision="v1.1.8",
+    punc_model='damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch',
+    punc_model_revision="v1.1.6",
+)
+```
+若不使用PUNC模型，可配置punc_model=""，或不传入punc_model参数，如需加入LM模型，可增加配置lm_model='damo/speech_transformer_lm_zh-cn-common-vocab8404-pytorch'。
+
+长音频版本模型默认开启时间戳，若不使用时间戳，可通过传入参数param_dict['use_timestamp'] = False关闭时间戳，使用方式如下：
+```python
+param_dict['use_timestamp'] = False
+rec_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav', param_dict=param_dict)
+)
+```
+
+
+#### 基于ModelScope进行微调
+
+- 基于ModelScope上数据集进行微调：
+
+以[AISHELL-1](https://www.modelscope.cn/datasets/speech_asr/speech_asr_aishell1_trainsets/summary)数据集为例，完整数据集已经上传ModelScope，可通过数据集英文名（speech_asr_aishell1_trainsets）搜索：
+
+
+```python
+import os
+from modelscope.metainfo import Trainers
+from modelscope.trainers import build_trainer
+from modelscope.msdatasets.audio.asr_dataset import ASRDataset
+
+def modelscope_finetune(params):
+    if not os.path.exists(params.output_dir):
+        os.makedirs(params.output_dir, exist_ok=True)
+    # dataset split ["train", "validation"]
+    ds_dict = ASRDataset.load(params.data_path, namespace='speech_asr')
+    kwargs = dict(
+        model=params.model,
+        data_dir=ds_dict,
+        dataset_type=params.dataset_type,
+        work_dir=params.output_dir,
+        batch_bins=params.batch_bins,
+        max_epoch=params.max_epoch,
+        lr=params.lr)
+    trainer = build_trainer(Trainers.speech_asr_trainer, default_args=kwargs)
+    trainer.train()
+
+
+if __name__ == '__main__':
+    from funasr.utils.modelscope_param import modelscope_args
+    params = modelscope_args(model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch")
+    params.output_dir = "./checkpoint"                      # 模型保存路径
+    params.data_path = "speech_asr_aishell1_trainsets"      # 数据路径，可以为modelscope中已上传数据，也可以是本地数据
+    params.dataset_type = "small"                           # 小数据量设置small，若数据量大于1000小时，请使用large
+    params.batch_bins = 2000                                # batch size，如果dataset_type="small"，batch_bins单位为fbank特征帧数，如果dataset_type="large"，batch_bins单位为毫秒，
+    params.max_epoch = 50                                   # 最大训练轮数
+    params.lr = 0.00005                                     # 设置学习率
+    
+    modelscope_finetune(params)
+```
+
+
+可将上述代码保存为py文件（如finetune.py），直接python finetune.py运行；若使用多卡进行训练，如下命令：
+
+```sh
+CUDA_VISIBLE_DEVICES=1,2 python -m torch.distributed.launch --nproc_per_node 2 finetune.py > log.txt 2>&1
+```
+
+- 基于私有数据集进行微调:
+只需要设置本地数据存放路径即可：
+```python
+params.data_path = "speech_asr_aishell1_trainsets"
+```
+
+私有数据集格式按如下准备：
+```sh
+tree ./example_data/
+./example_data/
+├── validation
+│   ├── text
+│   └── wav.scp
+└── train
+    ├── text
+    └── wav.scp
+2 directories, 4 files
+```
+
+其中，text文件中存放音频标注，wav.scp文件中存放wav音频绝对路径，样例如下：
+
+```sh
+cat ./example_data/text
+BAC009S0002W0122 而 对 楼 市 成 交 抑 制 作 用 最 大 的 限 购
+BAC009S0002W0123 也 成 为 地 方 政 府 的 眼 中 钉
+
+cat ./example_data/wav.scp
+BAC009S0002W0122 /mnt/data/wav/train/S0002/BAC009S0002W0122.wav
+BAC009S0002W0123 /mnt/data/wav/train/S0002/BAC009S0002W0123.wav
+```
+
+
+### 在本地机器中开发
+
+#### 基于ModelScope进行微调和推理
+
+支持基于ModelScope上数据集及私有数据集进行定制微调和推理，使用方式同Notebook中开发。
+
+#### 基于FunASR进行微调和推理
+
+FunASR框架支持魔搭社区开源的工业级的语音识别模型的training & finetuning，使得研究人员和开发者可以更加便捷的进行语音识别模型的研究和生产，目前已在Github开源：https://github.com/alibaba-damo-academy/FunASR 。若在使用过程中遇到任何问题，欢迎联系我们：[联系方式](https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/images/dingding.jpg)
+
+#### FunASR框架安装
+
+- 安装FunASR和ModelScope，[详见](https://github.com/alibaba-damo-academy/FunASR/wiki)
+
+```sh
+pip install "modelscope[audio_asr]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
+git clone https://github.com/alibaba/FunASR.git
+cd FunASR
+pip install --editable ./
+```
+
+#### 基于FunASR进行推理
+
+接下来会以私有数据集为例，介绍如何在FunASR框架中使用Paraformer-large进行推理以及微调。
+
+```sh
+cd egs_modelscope/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch
+python infer.py
+```
+
+#### 基于FunASR进行微调
+```sh
+cd egs_modelscope/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch
+python finetune.py
+```
+
+若修改输出路径、数据路径、采样率、batch_size等配置及使用多卡训练，可参照在Notebook开发中私有数据微调部分的代码，修改finetune.py文件中配置。
+
+
+## Benchmark
+  结合大数据、大模型优化的Paraformer在一序列语音识别的benchmark上获得当前SOTA的效果，以下展示学术数据集AISHELL-1、AISHELL-2、WenetSpeech，公开评测项目SpeechIO TIOBE白盒测试场景的效果。在学术界常用的中文语音识别评测任务中，其表现远远超于目前公开发表论文中的结果，远好于单独封闭数据集上的模型。
+
+### AISHELL-1
+
+| AISHELL-1 test                                   | w/o LM                                | w/ LM                                 |
+|:------------------------------------------------:|:-------------------------------------:|:-------------------------------------:|
+| <div style="width: 150pt">Espnet</div>           | <div style="width: 150pt">4.90</div>  | <div style="width: 150pt">4.70</div>  | 
+| <div style="width: 150pt">Wenet</div>            | <div style="width: 150pt">4.61</div>  | <div style="width: 150pt">4.36</div>  | 
+| <div style="width: 150pt">K2</div>               | <div style="width: 150pt">-</div>     | <div style="width: 150pt">4.26</div>  | 
+| <div style="width: 150pt">Blockformer</div>      | <div style="width: 150pt">4.29</div>  | <div style="width: 150pt">4.05</div>  |
+| <div style="width: 150pt">Paraformer-large</div> | <div style="width: 150pt">1.95</div>  | <div style="width: 150pt">1.68</div>  | 
+
+### AISHELL-2
+
+|           | dev_ios| test_android| test_ios|test_mic|
+|:-------------------------------------------------:|:-------------------------------------:|:-------------------------------------:|:------------------------------------:|:------------------------------------:|
+| <div style="width: 150pt">Espnet</div>            | <div style="width: 70pt">5.40</div>  |<div style="width: 70pt">6.10</div>  |<div style="width: 70pt">5.70</div>  |<div style="width: 70pt">6.10</div>  |
+| <div style="width: 150pt">WeNet</div>             | <div style="width: 70pt">-</div>     |<div style="width: 70pt">-</div>     |<div style="width: 70pt">5.39</div>  |<div style="width: 70pt">-</div>    |
+| <div style="width: 150pt">Paraformer-large</div>  | <div style="width: 70pt">2.80</div>  |<div style="width: 70pt">3.13</div>  |<div style="width: 70pt">2.85</div>  |<div style="width: 70pt">3.06</div>  |
+
+
+### Wenetspeech
+
+|           | dev| test_meeting| test_net|
+|:-------------------------------------------------:|:-------------------------------------:|:-------------------------------------:|:------------------------------------:|
+| <div style="width: 150pt">Espnet</div>            | <div style="width: 100pt">9.70</div>  |<div style="width: 100pt">15.90</div>  |<div style="width: 100pt">8.80</div>  |
+| <div style="width: 150pt">WeNet</div>             | <div style="width: 100pt">8.60</div>  |<div style="width: 100pt">17.34</div>  |<div style="width: 100pt">9.26</div>  |
+| <div style="width: 150pt">K2</div>                | <div style="width: 100pt">7.76</div>  |<div style="width: 100pt">13.41</div>  |<div style="width: 100pt">8.71</div>  |
+| <div style="width: 150pt">Paraformer-large</div>  | <div style="width: 100pt">3.57</div>  |<div style="width: 100pt">6.97</div>   |<div style="width: 100pt">6.74</div>  |
+
+### SpeechIO TIOBE
+
+Paraformer-large模型结合Transformer-LM模型做shallow fusion，在公开评测项目SpeechIO TIOBE白盒测试场景上获得当前SOTA的效果，目前[Transformer-LM模型](https://modelscope.cn/models/damo/speech_transformer_lm_zh-cn-common-vocab8404-pytorch/summary)已在ModelScope上开源，以下展示SpeechIO TIOBE白盒测试场景without LM、with Transformer-LM的效果：
+
+- Decode config w/o LM: 
+  - Decode without LM
+  - Beam size: 1
+- Decode config w/ LM:
+  - Decode with [Transformer-LM](https://modelscope.cn/models/damo/speech_transformer_lm_zh-cn-common-vocab8404-pytorch/summary)
+  - Beam size: 10
+  - LM weight: 0.15
+
+| testset | w/o LM | w/ LM |
+|:------------------:|:----:|:----:|
+|<div style="width: 200pt">SPEECHIO_ASR_ZH00001</div>| <div style="width: 150pt">0.49</div> | <div style="width: 150pt">0.35</div> |
+|<div style="width: 200pt">SPEECHIO_ASR_ZH00002</div>| <div style="width: 150pt">3.23</div> | <div style="width: 150pt">2.86</div> |
+|<div style="width: 200pt">SPEECHIO_ASR_ZH00003</div>| <div style="width: 150pt">1.13</div> | <div style="width: 150pt">0.80</div> |
+|<div style="width: 200pt">SPEECHIO_ASR_ZH00004</div>| <div style="width: 150pt">1.33</div> | <div style="width: 150pt">1.10</div> |
+|<div style="width: 200pt">SPEECHIO_ASR_ZH00005</div>| <div style="width: 150pt">1.41</div> | <div style="width: 150pt">1.18</div> |
+|<div style="width: 200pt">SPEECHIO_ASR_ZH00006</div>| <div style="width: 150pt">5.25</div> | <div style="width: 150pt">4.85</div> |
+|<div style="width: 200pt">SPEECHIO_ASR_ZH00007</div>| <div style="width: 150pt">5.51</div> | <div style="width: 150pt">4.97</div> |
+|<div style="width: 200pt">SPEECHIO_ASR_ZH00008</div>| <div style="width: 150pt">3.69</div> | <div style="width: 150pt">3.18</div> |
+|<div style="width: 200pt">SPEECHIO_ASR_ZH00009</div>| <div style="width: 150pt">3.02</div> | <div style="width: 150pt">2.78</div> |
+|<div style="width: 200pt">SPEECHIO_ASR_ZH000010</div>| <div style="width: 150pt">3.35</div> | <div style="width: 150pt">2.99</div> |
+|<div style="width: 200pt">SPEECHIO_ASR_ZH000011</div>| <div style="width: 150pt">1.54</div> | <div style="width: 150pt">1.25</div> |
+|<div style="width: 200pt">SPEECHIO_ASR_ZH000012</div>| <div style="width: 150pt">2.06</div> | <div style="width: 150pt">1.68</div> |
+|<div style="width: 200pt">SPEECHIO_ASR_ZH000013</div>| <div style="width: 150pt">2.57</div> | <div style="width: 150pt">2.25</div> |
+|<div style="width: 200pt">SPEECHIO_ASR_ZH000014</div>| <div style="width: 150pt">3.86</div> | <div style="width: 150pt">3.08</div> |
+|<div style="width: 200pt">SPEECHIO_ASR_ZH000015</div>| <div style="width: 150pt">3.34</div> | <div style="width: 150pt">2.67</div> |
+
+
+## 使用方式以及适用范围
+
+运行范围
+- 现阶段只能在Linux-x86_64运行，不支持Mac和Windows。
+
+使用方式
+- 直接推理：可以直接对输入音频进行解码，输出目标文字。
+- 微调：加载训练好的模型，采用私有或者开源数据进行模型训练。
+
+使用范围与目标场景
+- 适合与离线语音识别场景，如录音文件转写，配合GPU推理效果更加，推荐输入语音时长在20s以下，若想解码长音频，推荐使用[Paraformer-large长音频模型](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary)，集成VAD、ASR、标点与时间戳功能，可直接对时长为数小时音频进行识别，并输出带标点文字与时间戳。
+
+
+## 模型局限性以及可能的偏差
+
+考虑到特征提取流程和工具以及训练工具差异，会对CER的数据带来一定的差异（<0.1%），推理GPU环境差异导致的RTF数值差异。
+
+
+
+## 相关论文以及引用信息
+
+```BibTeX
+@inproceedings{gao2022paraformer,
+  title={Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition},
+  author={Gao, Zhifu and Zhang, Shiliang and McLoughlin, Ian and Yan, Zhijie},
+  booktitle={INTERSPEECH},
+  year={2022}
+}
+```

models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/am.mvn

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+</Nnet> 

models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/config.yaml

@@ -0,0 +1,8624 @@
+config: examples/aishell_demo/paraformer_s2/conf/train_asr_paraformer_sanm_50e_16d_2048_512_lfr6.yaml
+print_config: false
+log_level: INFO
+dry_run: false
+iterator_type: sequence
+output_dir: /nfs/FunASR_results/paraformer/1m-1gpu/baseline_train_asr_paraformer_sanm_50e_16d_2048_512_lfr6_fbank_zh_char_local
+ngpu: 1
+seed: 0
+num_workers: 16
+num_att_plot: 3
+dist_backend: nccl
+dist_init_method: file:///nfs/FunASR_results/paraformer/1m-1gpu/baseline_train_asr_paraformer_sanm_50e_16d_2048_512_lfr6_fbank_zh_char_local/ddp_init
+dist_world_size: 1
+dist_rank: 0
+local_rank: 0
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: null
+multiprocessing_distributed: true
+unused_parameters: true
+sharded_ddp: false
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: true
+collect_stats: false
+write_collected_feats: false
+max_epoch: 20
+patience: null
+val_scheduler_criterion:
+- valid
+- acc
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+-   - valid
+    - acc
+    - max
+keep_nbest_models: 5
+nbest_averaging_interval: 0
+grad_clip: 5
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 1
+no_forward_run: false
+resume: true
+train_dtype: float32
+use_amp: false
+log_interval: 50
+use_matplotlib: true
+use_tensorboard: true
+use_wandb: false
+wandb_project: null
+wandb_id: null
+wandb_entity: null
+wandb_name: null
+wandb_model_log_interval: -1
+detect_anomaly: false
+pretrain_path: null
+init_param:
+- /nfs/init_model/paraformer_9k_bigmodel.pth
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: null
+batch_size: 20
+valid_batch_size: null
+batch_bins: 1000
+valid_batch_bins: null
+train_shape_file:
+- /nfs/dataset/data/asr_stats_fbank_zh_char/train/speech_shape
+- /nfs/dataset/data/asr_stats_fbank_zh_char/train/text_shape.char
+valid_shape_file:
+- /nfs/dataset/data/asr_stats_fbank_zh_char/dev/speech_shape
+- /nfs/dataset/data/asr_stats_fbank_zh_char/dev/text_shape.char
+batch_type: length
+valid_batch_type: null
+fold_length:
+- 512
+- 150
+sort_in_batch: descending
+sort_batch: descending
+multiple_iterator: false
+chunk_length: 500
+chunk_shift_ratio: 0.5
+num_cache_chunks: 1024
+train_data_file: /nfs/dataset/data/dump/fbank/train/ark_txt.scp
+valid_data_file: /nfs/dataset/data/dump/fbank/dev/ark_txt.scp
+train_data_path_and_name_and_type:
+-   - /nfs/dataset/data/dump/fbank/train/feats.scp
+    - speech
+    - kaldi_ark
+-   - /nfs/dataset/data/dump/fbank/train/text
+    - text
+    - text
+valid_data_path_and_name_and_type:
+-   - /nfs/dataset/data/dump/fbank/dev/feats.scp
+    - speech
+    - kaldi_ark
+-   - /nfs/dataset/data/dump/fbank/dev/text
+    - text
+    - text
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+valid_max_cache_size: null
+optim: adam
+optim_conf:
+    lr: 0.0005
+scheduler: warmuplr
+scheduler_conf:
+    warmup_steps: 30000
+use_pai: false
+num_worker_count: 1
+access_key_id: null
+access_key_secret: null
+endpoint: null
+bucket_name: null
+oss_bucket: null
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models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/configuration.json

@@ -0,0 +1,24 @@
+{
+  "framework": "pytorch",
+  "task" : "auto-speech-recognition",
+  "model" : {
+    "type" : "generic-asr",
+    "am_model_name" : "model.pb",
+    "model_config" : {
+      "type": "pytorch",
+      "code_base": "funasr",
+      "mode": "paraformer",
+      "lang": "zh-cn",
+      "batch_size": 1,
+      "am_model_config": "config.yaml",
+      "lm_model_name" : "lm/lm.pb",
+      "lm_model_config": "lm/lm.yaml",
+      "asr_model_config": "decoding.yaml",
+      "mvn_file": "am.mvn",
+      "model": "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
+    }
+  },
+  "pipeline": {
+    "type":"asr-inference"
+  }
+}

models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/decoding.yaml

@@ -0,0 +1,6 @@
+beam_size: 1
+penalty: 0.0
+maxlenratio: 0.0
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+lm_weight: 0.0

models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/finetune.yaml

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+# minibatch related
+# # dataset_type: small
+batch_type: length
+batch_bins: 2000
+num_workers: 16
+speech_length_min: 100
+speech_length_max: 15000
+# dataset_type: large
+dataset_conf:
+    data_types: sound,text
+    filter_conf:
+        speech_length_min: 100
+        speech_length_max: 15000
+        token_length_min: 0
+        token_length_max: 200
+    shuffle: true
+    shuffle_conf:
+        shuffle_size: 2048
+        sort_size: 500
+    batch_conf:
+        batch_type: 'token'
+        batch_size: 120000
+    num_workers: 16
+
+# optimization related
+accum_grad: 1
+grad_clip: 5
+max_epoch: 20
+keep_nbest_models: 10
+optim: adam
+optim_conf:
+   lr: 0.0005
+scheduler: warmuplr
+scheduler_conf:
+   warmup_steps: 30000
+
+log_interval: 50

models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/model.onnx

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+oid sha256:f3627f8519e24dee8b9ab7d716d1e46f308d3bfe5f655c9ec350cad9bc4e34f2
+size 864924763

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+蔬
+寄
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+教
+俪
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+碛
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+憎
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+蹰
+聂
+员
+呶
+瞬
+il@@
+恿
+阙
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+mi@@
+禾
+椴
+yo@@
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+繁
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+so@@
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+spon@@
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+抽
+簧
+c@@
+买
+吖
+俬
+梓
+叡
+祼
+烃
+荃
+眀
+<unk>

requirements.txt

@@ -0,0 +1,9 @@
+WeTextProcessing
+onnxruntime
+soundfile
+librosa
+scipy
+numpy
+typeguard==2.13.3
+kaldi-native-fbank
+PyYAML>=5.1.2

test_wavs/vad_example.wav

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a7431f0169ef76ef630c945a1d2c3675d8c8c2df2ae4a6b16f8a88ba1bccfbbb
+size 2261722

transcribe.py

@@ -0,0 +1,73 @@
+import soundfile
+from funasr_onnx import Fsmn_vad, Paraformer, CT_Transformer
+import datetime
+from itn.chinese.inverse_normalizer import InverseNormalizer
+import argparse
+
+def get_args():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--model-dir",
+        type=str,
+        default="./models",
+        help="onnx export model directory",
+    )
+
+    parser.add_argument(
+        "--wav-path",
+        type=str,
+        default="./test_wavs/vad_example.wav",
+        help="audio file path in .wav format",
+    )
+
+    return parser.parse_args()
+
+def process_time(milliseconds):
+    delta = datetime.timedelta(milliseconds=milliseconds)
+    time_str = str(delta)
+    time_parts = time_str.split(".")[0].split(":")
+    time_hms = "{:02d}:{:02d}:{:02d}".format(int(time_parts[0]), int(time_parts[1]), int(time_parts[2]))
+    return time_hms
+
+def get_models(model_dir):
+    vad_model_dir = model_dir + "/speech_fsmn_vad_zh-cn-16k-common-pytorch"
+    asr_model_dir =  model_dir + "/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
+    punc_model_dir = model_dir + "/punc_ct-transformer_zh-cn-common-vocab272727-pytorch"
+    asr_model = Paraformer(asr_model_dir, batch_size=1, plot_timestamp_to="./", pred_bias=0)  # cpu
+    punc_model= CT_Transformer(punc_model_dir)
+    vad_model = Fsmn_vad(vad_model_dir)
+    return asr_model, vad_model, punc_model
+
+def load_audio(wav_path):
+    speech, sample_rate = soundfile.read(wav_path)
+    return speech, sample_rate
+
+def transcribe(speech, asr_model, vad_model=None, punc_model=None, invnormalizer=None):
+    if vad_model:
+        segments_info = vad_model(audio_in=speech)
+        assert len(segments_info) == 1, "only support batch_size 1"
+        for seg in segments_info[0]:
+            if seg[1] == -1:  # end of speech
+                seg[1] = len(speech) // 16
+            seg_speech = speech[seg[0]*16:seg[1]*16]
+
+            result = asr_model(seg_speech)
+            result = result[0]['preds'][0]
+            if invnormalizer:
+                result = invnormalizer.normalize(result)
+            if punc_model:
+                result = punc_model(result)
+                result = result[0]
+            item = f"{process_time(seg[0])}-->{process_time(seg[1])}  {result}"
+            yield item
+
+if __name__ == "__main__":
+   args = get_args()
+   asr_model, vad_model, punc_model = get_models(args.model_dir)
+   invnormalizer = InverseNormalizer()
+   speech, _ = load_audio(args.wav_path)
+   for item in transcribe(speech, asr_model, vad_model, punc_model, invnormalizer):
+        print(item)