From e55178abc21a3a692b7b18cc12922b4004c15f2e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E4=B9=9D=E8=80=B3?= <mengzhe.cmz@alibaba-inc.com>
Date: Thu, 30 Mar 2023 14:11:02 +0800
Subject: [PATCH] general punc model runtime

---
 .../python/onnxruntime/demo_punc_offline.py   |   9 +
 .../onnxruntime/funasr_onnx/__init__.py       |   2 +
 .../onnxruntime/funasr_onnx/punc_bin.py       | 133 +++++
 .../funasr_onnx/utils/preprocessor.py         | 470 ++++++++++++++++++
 .../onnxruntime/funasr_onnx/utils/utils.py    |  13 +
 5 files changed, 627 insertions(+)
 create mode 100644 funasr/runtime/python/onnxruntime/demo_punc_offline.py
 create mode 100644 funasr/runtime/python/onnxruntime/funasr_onnx/utils/preprocessor.py

diff --git a/funasr/runtime/python/onnxruntime/demo_punc_offline.py b/funasr/runtime/python/onnxruntime/demo_punc_offline.py
new file mode 100644
index 000000000..056f73751
--- /dev/null
+++ b/funasr/runtime/python/onnxruntime/demo_punc_offline.py
@@ -0,0 +1,9 @@
+from funasr_onnx import TargetDelayTransformer
+
+model_dir = "/disk1/mengzhe.cmz/workspace/FunASR/funasr/export/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch"
+model = TargetDelayTransformer(model_dir)
+
+text_in = "我们都是木头人不会讲话不会动"
+
+result = model(text_in)
+print(result)
diff --git a/funasr/runtime/python/onnxruntime/funasr_onnx/__init__.py b/funasr/runtime/python/onnxruntime/funasr_onnx/__init__.py
index 475047903..1620a0b25 100644
--- a/funasr/runtime/python/onnxruntime/funasr_onnx/__init__.py
+++ b/funasr/runtime/python/onnxruntime/funasr_onnx/__init__.py
@@ -1,3 +1,5 @@
 # -*- encoding: utf-8 -*-
 from .paraformer_bin import Paraformer
 from .vad_bin import Fsmn_vad
+from .punc_bin import TargetDelayTransformer
+#from .punc_bin import VadRealtimeTransformer
diff --git a/funasr/runtime/python/onnxruntime/funasr_onnx/punc_bin.py b/funasr/runtime/python/onnxruntime/funasr_onnx/punc_bin.py
index e69de29bb..64ced69be 100644
--- a/funasr/runtime/python/onnxruntime/funasr_onnx/punc_bin.py
+++ b/funasr/runtime/python/onnxruntime/funasr_onnx/punc_bin.py
@@ -0,0 +1,133 @@
+# -*- 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.preprocessor import CodeMixTokenizerCommonPreprocessor
+from .utils.utils import split_to_mini_sentence
+logging = get_logger()
+
+
+class TargetDelayTransformer():
+    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.ort_infer = OrtInferSession(model_file, device_id, intra_op_num_threads=intra_op_num_threads)
+        self.batch_size = 1
+        self.encoder_conf = config["encoder_conf"]
+        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
+        self.preprocessor = CodeMixTokenizerCommonPreprocessor(
+            train=False,
+            token_type=config.token_type,
+            token_list=config.token_list,
+            bpemodel=config.bpemodel,
+            text_cleaner=config.cleaner,
+            g2p_type=config.g2p,
+            text_name="text",
+            non_linguistic_symbols=config.non_linguistic_symbols,
+        )
+
+    def __call__(self, text: Union[list, str], split_size=20):
+        data = {"text": text}
+        result = self.preprocessor(data=data, uid="12938712838719")
+        split_text = self.preprocessor.pop_split_text_data(result)
+        mini_sentences = split_to_mini_sentence(split_text, split_size)
+        mini_sentences_id = split_to_mini_sentence(data["text"], 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.concatenate((cache_sent_id, mini_sentence_id), axis=0)
+            data = {
+                "text": mini_sentence_id,
+                "text_lengths": len(mini_sentence_id),
+            }
+            try:
+                outputs = self.infer(data['text'], data['text_lengths'])
+                y = outputs[0]
+                _, indices = y.view(-1, y.shape[-1]).topk(1, dim=1)
+                punctuations = indices
+                assert punctuations.size()[0] == 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 = punctuations.cpu().numpy()
+            new_mini_sentence_punc += [int(x) for x in punctuations_np]
+            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: List) -> Tuple[np.ndarray, np.ndarray]:
+
+        outputs = self.ort_infer(feats)
+        return outputs
+
diff --git a/funasr/runtime/python/onnxruntime/funasr_onnx/utils/preprocessor.py b/funasr/runtime/python/onnxruntime/funasr_onnx/utils/preprocessor.py
new file mode 100644
index 000000000..4c9710371
--- /dev/null
+++ b/funasr/runtime/python/onnxruntime/funasr_onnx/utils/preprocessor.py
@@ -0,0 +1,470 @@
+import re
+from abc import ABC
+from abc import abstractmethod
+from pathlib import Path
+from typing import Collection
+from typing import Dict
+from typing import Iterable
+from typing import List
+from typing import Union
+
+import numpy as np
+import scipy.signal
+import soundfile
+from typeguard import check_argument_types
+from typeguard import check_return_type
+
+from funasr.text.build_tokenizer import build_tokenizer
+from funasr.text.cleaner import TextCleaner
+from funasr.text.token_id_converter import TokenIDConverter
+
+
+class AbsPreprocessor(ABC):
+    def __init__(self, train: bool):
+        self.train = train
+
+    @abstractmethod
+    def __call__(
+            self, uid: str, data: Dict[str, Union[str, np.ndarray]]
+    ) -> Dict[str, np.ndarray]:
+        raise NotImplementedError
+
+
+def forward_segment(text, dic):
+    word_list = []
+    i = 0
+    while i < len(text):
+        longest_word = text[i]
+        for j in range(i + 1, len(text) + 1):
+            word = text[i:j]
+            if word in dic:
+                if len(word) > len(longest_word):
+                    longest_word = word
+        word_list.append(longest_word)
+        i += len(longest_word)
+    return word_list
+
+
+def seg_tokenize(txt, seg_dict):
+    out_txt = ""
+    for word in txt:
+        if word in seg_dict:
+            out_txt += seg_dict[word] + " "
+        else:
+            out_txt += "<unk>" + " "
+    return out_txt.strip().split()
+
+def seg_tokenize_wo_pattern(txt, seg_dict):
+    out_txt = ""
+    for word in txt:
+        if word in seg_dict:
+            out_txt += seg_dict[word] + " "
+        else:
+            out_txt += "<unk>" + " "
+    return out_txt.strip().split()
+
+
+def framing(
+        x,
+        frame_length: int = 512,
+        frame_shift: int = 256,
+        centered: bool = True,
+        padded: bool = True,
+):
+    if x.size == 0:
+        raise ValueError("Input array size is zero")
+    if frame_length < 1:
+        raise ValueError("frame_length must be a positive integer")
+    if frame_length > x.shape[-1]:
+        raise ValueError("frame_length is greater than input length")
+    if 0 >= frame_shift:
+        raise ValueError("frame_shift must be greater than 0")
+
+    if centered:
+        pad_shape = [(0, 0) for _ in range(x.ndim - 1)] + [
+            (frame_length // 2, frame_length // 2)
+        ]
+        x = np.pad(x, pad_shape, mode="constant", constant_values=0)
+
+    if padded:
+        # Pad to integer number of windowed segments
+        # I.e make x.shape[-1] = frame_length + (nseg-1)*nstep,
+        #  with integer nseg
+        nadd = (-(x.shape[-1] - frame_length) % frame_shift) % frame_length
+        pad_shape = [(0, 0) for _ in range(x.ndim - 1)] + [(0, nadd)]
+        x = np.pad(x, pad_shape, mode="constant", constant_values=0)
+
+    # Created strided array of data segments
+    if frame_length == 1 and frame_length == frame_shift:
+        result = x[..., None]
+    else:
+        shape = x.shape[:-1] + (
+            (x.shape[-1] - frame_length) // frame_shift + 1,
+            frame_length,
+        )
+        strides = x.strides[:-1] + (frame_shift * x.strides[-1], x.strides[-1])
+        result = np.lib.stride_tricks.as_strided(x, shape=shape, strides=strides)
+    return result
+
+
+def detect_non_silence(
+        x: np.ndarray,
+        threshold: float = 0.01,
+        frame_length: int = 1024,
+        frame_shift: int = 512,
+        window: str = "boxcar",
+) -> np.ndarray:
+    """Power based voice activity detection.
+
+    Args:
+        x: (Channel, Time)
+    >>> x = np.random.randn(1000)
+    >>> detect = detect_non_silence(x)
+    >>> assert x.shape == detect.shape
+    >>> assert detect.dtype == np.bool
+    """
+    if x.shape[-1] < frame_length:
+        return np.full(x.shape, fill_value=True, dtype=np.bool)
+
+    if x.dtype.kind == "i":
+        x = x.astype(np.float64)
+    # framed_w: (C, T, F)
+    framed_w = framing(
+        x,
+        frame_length=frame_length,
+        frame_shift=frame_shift,
+        centered=False,
+        padded=True,
+    )
+    framed_w *= scipy.signal.get_window(window, frame_length).astype(framed_w.dtype)
+    # power: (C, T)
+    power = (framed_w ** 2).mean(axis=-1)
+    # mean_power: (C, 1)
+    mean_power = np.mean(power, axis=-1, keepdims=True)
+    if np.all(mean_power == 0):
+        return np.full(x.shape, fill_value=True, dtype=np.bool)
+    # detect_frames: (C, T)
+    detect_frames = power / mean_power > threshold
+    # detects: (C, T, F)
+    detects = np.broadcast_to(
+        detect_frames[..., None], detect_frames.shape + (frame_shift,)
+    )
+    # detects: (C, TF)
+    detects = detects.reshape(*detect_frames.shape[:-1], -1)
+    # detects: (C, TF)
+    return np.pad(
+        detects,
+        [(0, 0)] * (x.ndim - 1) + [(0, x.shape[-1] - detects.shape[-1])],
+        mode="edge",
+    )
+
+
+class CommonPreprocessor(AbsPreprocessor):
+    def __init__(
+            self,
+            train: bool,
+            token_type: str = None,
+            token_list: Union[Path, str, Iterable[str]] = None,
+            bpemodel: Union[Path, str, Iterable[str]] = None,
+            text_cleaner: Collection[str] = None,
+            g2p_type: str = None,
+            unk_symbol: str = "<unk>",
+            space_symbol: str = "<space>",
+            non_linguistic_symbols: Union[Path, str, Iterable[str]] = None,
+            delimiter: str = None,
+            rir_scp: str = None,
+            rir_apply_prob: float = 1.0,
+            noise_scp: str = None,
+            noise_apply_prob: float = 1.0,
+            noise_db_range: str = "3_10",
+            speech_volume_normalize: float = None,
+            speech_name: str = "speech",
+            text_name: str = "text",
+            split_with_space: bool = False,
+            seg_dict_file: str = None,
+    ):
+        super().__init__(train)
+        self.train = train
+        self.speech_name = speech_name
+        self.text_name = text_name
+        self.speech_volume_normalize = speech_volume_normalize
+        self.rir_apply_prob = rir_apply_prob
+        self.noise_apply_prob = noise_apply_prob
+        self.split_with_space = split_with_space
+        self.seg_dict = None
+        if seg_dict_file is not None:
+            self.seg_dict = {}
+            with open(seg_dict_file) as f:
+                lines = f.readlines()
+            for line in lines:
+                s = line.strip().split()
+                key = s[0]
+                value = s[1:]
+                self.seg_dict[key] = " ".join(value)
+
+        if token_type is not None:
+            if token_list is None:
+                raise ValueError("token_list is required if token_type is not None")
+            self.text_cleaner = TextCleaner(text_cleaner)
+
+            self.tokenizer = build_tokenizer(
+                token_type=token_type,
+                bpemodel=bpemodel,
+                delimiter=delimiter,
+                space_symbol=space_symbol,
+                non_linguistic_symbols=non_linguistic_symbols,
+                g2p_type=g2p_type,
+            )
+            self.token_id_converter = TokenIDConverter(
+                token_list=token_list,
+                unk_symbol=unk_symbol,
+            )
+        else:
+            self.text_cleaner = None
+            self.tokenizer = None
+            self.token_id_converter = None
+
+        if train and rir_scp is not None:
+            self.rirs = []
+            with open(rir_scp, "r", encoding="utf-8") as f:
+                for line in f:
+                    sps = line.strip().split(None, 1)
+                    if len(sps) == 1:
+                        self.rirs.append(sps[0])
+                    else:
+                        self.rirs.append(sps[1])
+        else:
+            self.rirs = None
+
+        if train and noise_scp is not None:
+            self.noises = []
+            with open(noise_scp, "r", encoding="utf-8") as f:
+                for line in f:
+                    sps = line.strip().split(None, 1)
+                    if len(sps) == 1:
+                        self.noises.append(sps[0])
+                    else:
+                        self.noises.append(sps[1])
+            sps = noise_db_range.split("_")
+            if len(sps) == 1:
+                self.noise_db_low, self.noise_db_high = float(sps[0])
+            elif len(sps) == 2:
+                self.noise_db_low, self.noise_db_high = float(sps[0]), float(sps[1])
+            else:
+                raise ValueError(
+                    "Format error: '{noise_db_range}' e.g. -3_4 -> [-3db,4db]"
+                )
+        else:
+            self.noises = None
+
+    def _speech_process(
+            self, data: Dict[str, Union[str, np.ndarray]]
+    ) -> Dict[str, Union[str, np.ndarray]]:
+        assert check_argument_types()
+        if self.speech_name in data:
+            if self.train and (self.rirs is not None or self.noises is not None):
+                speech = data[self.speech_name]
+                nsamples = len(speech)
+
+                # speech: (Nmic, Time)
+                if speech.ndim == 1:
+                    speech = speech[None, :]
+                else:
+                    speech = speech.T
+                # Calc power on non shlence region
+                power = (speech[detect_non_silence(speech)] ** 2).mean()
+
+                # 1. Convolve RIR
+                if self.rirs is not None and self.rir_apply_prob >= np.random.random():
+                    rir_path = np.random.choice(self.rirs)
+                    if rir_path is not None:
+                        rir, _ = soundfile.read(
+                            rir_path, dtype=np.float64, always_2d=True
+                        )
+
+                        # rir: (Nmic, Time)
+                        rir = rir.T
+
+                        # speech: (Nmic, Time)
+                        # Note that this operation doesn't change the signal length
+                        speech = scipy.signal.convolve(speech, rir, mode="full")[
+                                 :, : speech.shape[1]
+                                 ]
+                        # Reverse mean power to the original power
+                        power2 = (speech[detect_non_silence(speech)] ** 2).mean()
+                        speech = np.sqrt(power / max(power2, 1e-10)) * speech
+
+                # 2. Add Noise
+                if (
+                        self.noises is not None
+                        and self.noise_apply_prob >= np.random.random()
+                ):
+                    noise_path = np.random.choice(self.noises)
+                    if noise_path is not None:
+                        noise_db = np.random.uniform(
+                            self.noise_db_low, self.noise_db_high
+                        )
+                        with soundfile.SoundFile(noise_path) as f:
+                            if f.frames == nsamples:
+                                noise = f.read(dtype=np.float64, always_2d=True)
+                            elif f.frames < nsamples:
+                                offset = np.random.randint(0, nsamples - f.frames)
+                                # noise: (Time, Nmic)
+                                noise = f.read(dtype=np.float64, always_2d=True)
+                                # Repeat noise
+                                noise = np.pad(
+                                    noise,
+                                    [(offset, nsamples - f.frames - offset), (0, 0)],
+                                    mode="wrap",
+                                )
+                            else:
+                                offset = np.random.randint(0, f.frames - nsamples)
+                                f.seek(offset)
+                                # noise: (Time, Nmic)
+                                noise = f.read(
+                                    nsamples, dtype=np.float64, always_2d=True
+                                )
+                                if len(noise) != nsamples:
+                                    raise RuntimeError(f"Something wrong: {noise_path}")
+                        # noise: (Nmic, Time)
+                        noise = noise.T
+
+                        noise_power = (noise ** 2).mean()
+                        scale = (
+                                10 ** (-noise_db / 20)
+                                * np.sqrt(power)
+                                / np.sqrt(max(noise_power, 1e-10))
+                        )
+                        speech = speech + scale * noise
+
+                speech = speech.T
+                ma = np.max(np.abs(speech))
+                if ma > 1.0:
+                    speech /= ma
+                data[self.speech_name] = speech
+
+            if self.speech_volume_normalize is not None:
+                speech = data[self.speech_name]
+                ma = np.max(np.abs(speech))
+                data[self.speech_name] = speech * self.speech_volume_normalize / ma
+        assert check_return_type(data)
+        return data
+
+    def _text_process(
+            self, data: Dict[str, Union[str, np.ndarray]]
+    ) -> Dict[str, np.ndarray]:
+        if self.text_name in data and self.tokenizer is not None:
+            text = data[self.text_name]
+            text = self.text_cleaner(text)
+            if self.split_with_space:
+                tokens = text.strip().split(" ")
+                if self.seg_dict is not None:
+                    tokens = forward_segment("".join(tokens), self.seg_dict)
+                    tokens = seg_tokenize(tokens, self.seg_dict)
+            else:
+                tokens = self.tokenizer.text2tokens(text)
+            text_ints = self.token_id_converter.tokens2ids(tokens)
+            data[self.text_name] = np.array(text_ints, dtype=np.int64)
+        assert check_return_type(data)
+        return data
+
+    def __call__(
+            self, uid: str, data: Dict[str, Union[str, np.ndarray]]
+    ) -> Dict[str, np.ndarray]:
+        assert check_argument_types()
+
+        data = self._speech_process(data)
+        data = self._text_process(data)
+        return data
+
+class CodeMixTokenizerCommonPreprocessor(CommonPreprocessor):
+    def __init__(
+            self,
+            train: bool,
+            token_type: str = None,
+            token_list: Union[Path, str, Iterable[str]] = None,
+            bpemodel: Union[Path, str, Iterable[str]] = None,
+            text_cleaner: Collection[str] = None,
+            g2p_type: str = None,
+            unk_symbol: str = "<unk>",
+            space_symbol: str = "<space>",
+            non_linguistic_symbols: Union[Path, str, Iterable[str]] = None,
+            delimiter: str = None,
+            rir_scp: str = None,
+            rir_apply_prob: float = 1.0,
+            noise_scp: str = None,
+            noise_apply_prob: float = 1.0,
+            noise_db_range: str = "3_10",
+            speech_volume_normalize: float = None,
+            speech_name: str = "speech",
+            text_name: str = "text",
+            split_text_name: str = "split_text",
+            split_with_space: bool = False,
+            seg_dict_file: str = None,
+    ):
+        super().__init__(
+            train=train,
+            # Force to use word.
+            token_type="word",
+            token_list=token_list,
+            bpemodel=bpemodel,
+            text_cleaner=text_cleaner,
+            g2p_type=g2p_type,
+            unk_symbol=unk_symbol,
+            space_symbol=space_symbol,
+            non_linguistic_symbols=non_linguistic_symbols,
+            delimiter=delimiter,
+            speech_name=speech_name,
+            text_name=text_name,
+            rir_scp=rir_scp,
+            rir_apply_prob=rir_apply_prob,
+            noise_scp=noise_scp,
+            noise_apply_prob=noise_apply_prob,
+            noise_db_range=noise_db_range,
+            speech_volume_normalize=speech_volume_normalize,
+            split_with_space=split_with_space,
+            seg_dict_file=seg_dict_file,
+        )
+        # The data field name for split text.
+        self.split_text_name = split_text_name
+
+    @classmethod
+    def split_words(cls, 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 __call__(
+            self, uid: str, data: Dict[str, Union[list, str, np.ndarray]]
+    ) -> Dict[str, Union[list, np.ndarray]]:
+        assert check_argument_types()
+        # Split words.
+        if isinstance(data[self.text_name], str):
+            split_text = self.split_words(data[self.text_name])
+        else:
+            split_text = data[self.text_name]
+        data[self.text_name] = " ".join(split_text)
+        data = self._speech_process(data)
+        data = self._text_process(data)
+        data[self.split_text_name] = split_text
+        return data
+
+    def pop_split_text_data(self, data: Dict[str, Union[str, np.ndarray]]):
+        result = data[self.split_text_name]
+        del data[self.split_text_name]
+        return result
diff --git a/funasr/runtime/python/onnxruntime/funasr_onnx/utils/utils.py b/funasr/runtime/python/onnxruntime/funasr_onnx/utils/utils.py
index fccd5a095..c7e607691 100644
--- a/funasr/runtime/python/onnxruntime/funasr_onnx/utils/utils.py
+++ b/funasr/runtime/python/onnxruntime/funasr_onnx/utils/utils.py
@@ -215,6 +215,19 @@ class OrtInferSession():
         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 read_yaml(yaml_path: Union[str, Path]) -> Dict:
     if not Path(yaml_path).exists():