Merge pull request #218 from alibaba-damo-academy/dev_ts

update timestamp related codes and egs_modelscope

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

@@ -0,0 +1,25 @@
+# ModelScope Model
+
+## How to finetune and infer using a pretrained ModelScope Model
+
+### Inference
+
+Or you can use the finetuned model for inference directly.
+
+- Setting parameters in `infer.py`
+    - <strong>audio_in:</strong> # support wav, url, bytes, and parsed audio format.
+    - <strong>text_in:</strong> # support text, text url.
+    - <strong>output_dir:</strong> # If the input format is wav.scp, it needs to be set.
+
+- Then you can run the pipeline to infer with:
+```python
+    python infer.py
+```
+
+
+Modify inference related parameters in vad.yaml.
+
+- max_end_silence_time: The end-point silence duration  to judge the end of sentence, the parameter range is 500ms~6000ms, and the default value is 800ms
+- speech_noise_thres:  The balance of speech and silence scores, the parameter range is (-1,1)
+    - The value tends to -1, the greater probability of noise being judged as speech
+    - The value tends to 1, the greater probability of speech being judged as noise

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

@@ -0,0 +1,12 @@
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+inference_pipline = pipeline(
+    task=Tasks.speech_timestamp,
+    model='damo/speech_timestamp_prediction-v1-16k-offline',
+    output_dir='./tmp')
+
+rec_result = inference_pipline(
+    audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_timestamps.wav',
+    text_in='一 个 东 太 平 洋 国 家 为 什 么 跑 到 西 太 平 洋 来 了 呢')
+print(rec_result)

funasr/bin/asr_inference_paraformer.py

@@ -42,7 +42,7 @@ from funasr.utils import asr_utils, wav_utils, postprocess_utils
 from funasr.models.frontend.wav_frontend import WavFrontend
 from funasr.models.e2e_asr_paraformer import BiCifParaformer, ContextualParaformer
 from funasr.export.models.e2e_asr_paraformer import Paraformer as Paraformer_export
-from funasr.utils.timestamp_tools import time_stamp_lfr6_pl, time_stamp_sentence
+from funasr.utils.timestamp_tools import ts_prediction_lfr6_standard
 
 
 class Speech2Text:
@@ -245,7 +245,7 @@ class Speech2Text:
             decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
 
         if isinstance(self.asr_model, BiCifParaformer):
-            _, _, us_alphas, us_cif_peak = self.asr_model.calc_predictor_timestamp(enc, enc_len,
+            _, _, us_alphas, us_peaks = self.asr_model.calc_predictor_timestamp(enc, enc_len,
                                                                                    pre_token_length)  # test no bias cif2
 
         results = []
@@ -291,7 +291,10 @@ class Speech2Text:
                     text = None
 
                 if isinstance(self.asr_model, BiCifParaformer):
-                    timestamp = time_stamp_lfr6_pl(us_alphas[i], us_cif_peak[i], copy.copy(token), begin_time, end_time)
+                    _, timestamp = ts_prediction_lfr6_standard(us_alphas[i], 
+                                                            us_peaks[i], 
+                                                            copy.copy(token), 
+                                                            vad_offset=begin_time)
                     results.append((text, token, token_int, hyp, timestamp, enc_len_batch_total, lfr_factor))
                 else:
                     results.append((text, token, token_int, hyp, enc_len_batch_total, lfr_factor))

funasr/bin/asr_inference_paraformer_vad_punc.py

@@ -44,11 +44,10 @@ from funasr.utils import asr_utils, wav_utils, postprocess_utils
 from funasr.models.frontend.wav_frontend import WavFrontend
 from funasr.tasks.vad import VADTask
 from funasr.bin.vad_inference import Speech2VadSegment
-from funasr.utils.timestamp_tools import time_stamp_lfr6_pl
+from funasr.utils.timestamp_tools import time_stamp_sentence, ts_prediction_lfr6_standard
 from funasr.bin.punctuation_infer import Text2Punc
 from funasr.models.e2e_asr_paraformer import BiCifParaformer, ContextualParaformer
 
-from funasr.utils.timestamp_tools import time_stamp_sentence
 
 header_colors = '\033[95m'
 end_colors = '\033[0m'
@@ -257,7 +256,7 @@ class Speech2Text:
             decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
 
         if isinstance(self.asr_model, BiCifParaformer):
-            _, _, us_alphas, us_cif_peak = self.asr_model.calc_predictor_timestamp(enc, enc_len,
+            _, _, us_alphas, us_peaks = self.asr_model.calc_predictor_timestamp(enc, enc_len,
                                                                                    pre_token_length)  # test no bias cif2
 
         results = []
@@ -303,7 +302,10 @@ class Speech2Text:
                     text = None
 
                 if isinstance(self.asr_model, BiCifParaformer):
-                    timestamp = time_stamp_lfr6_pl(us_alphas[i], us_cif_peak[i], copy.copy(token), begin_time, end_time)
+                    _, timestamp = ts_prediction_lfr6_standard(us_alphas[i], 
+                                                            us_peaks[i], 
+                                                            copy.copy(token), 
+                                                            vad_offset=begin_time)
                     results.append((text, token, token_int, timestamp, enc_len_batch_total, lfr_factor))
                 else:
                     results.append((text, token, token_int, enc_len_batch_total, lfr_factor))

funasr/bin/build_trainer.py

@@ -28,7 +28,9 @@ def parse_args(mode):
     elif mode == "uniasr":
         from funasr.tasks.asr import ASRTaskUniASR as ASRTask
     elif mode == "mfcca":
-        from funasr.tasks.asr import ASRTaskMFCCA as ASRTask   
+        from funasr.tasks.asr import ASRTaskMFCCA as ASRTask
+    elif mode == "tp":
+        from funasr.tasks.asr import ASRTaskAligner as ASRTask
     else:
         raise ValueError("Unknown mode: {}".format(mode))
     parser = ASRTask.get_parser()

funasr/bin/tp_inference.py

@@ -28,6 +28,8 @@ from funasr.utils.types import str2triple_str
 from funasr.utils.types import str_or_none
 from funasr.models.frontend.wav_frontend import WavFrontend
 from funasr.text.token_id_converter import TokenIDConverter
+from funasr.utils.timestamp_tools import ts_prediction_lfr6_standard
+
 
 header_colors = '\033[95m'
 end_colors = '\033[0m'
@@ -38,61 +40,6 @@ global_sample_rate: Union[int, Dict[Any, int]] = {
     'model_fs': 16000
 }
 
-def time_stamp_lfr6_advance(us_alphas, us_cif_peak, char_list):
-    START_END_THRESHOLD = 5
-    MAX_TOKEN_DURATION = 12
-    TIME_RATE = 10.0 * 6 / 1000 / 3  #  3 times upsampled
-    if len(us_cif_peak.shape) == 2:
-        alphas, cif_peak = us_alphas[0], us_cif_peak[0]  # support inference batch_size=1 only
-    else:
-        alphas, cif_peak = us_alphas, us_cif_peak
-    num_frames = cif_peak.shape[0]
-    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 = torch.where(cif_peak>1.0-1e-4)[0].cpu().numpy() - 3.2  # total offset
-    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 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
-    # new_char_list.append(char_list[-1])
-    if num_frames - fire_place[-1] > START_END_THRESHOLD:
-        _end = (num_frames + fire_place[-1]) * 0.5
-        # _end = fire_place[-1] 
-        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
-    assert len(new_char_list) == len(timestamp_list)
-    res_str = ""
-    for char, timestamp in zip(new_char_list, timestamp_list):
-        res_str += "{} {} {};".format(char, str(timestamp[0]+0.0005)[:5], str(timestamp[1]+0.0005)[:5])
-    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
-
 
 class SpeechText2Timestamp:
     def __init__(
@@ -315,7 +262,7 @@ def inference_modelscope(
             for batch_id in range(_bs):
                 key = keys[batch_id]
                 token = speechtext2timestamp.converter.ids2tokens(batch['text'][batch_id])
-                ts_str, ts_list = time_stamp_lfr6_advance(us_alphas[batch_id], us_cif_peak[batch_id], token)
+                ts_str, ts_list = ts_prediction_lfr6_standard(us_alphas[batch_id], us_cif_peak[batch_id], token, force_time_shift=-3.0)
                 logging.warning(ts_str)
                 item = {'key': key, 'value': ts_str, 'timestamp':ts_list}
                 tp_result_list.append(item)

funasr/models/e2e_asr_paraformer.py

@@ -926,10 +926,10 @@ class BiCifParaformer(Paraformer):
     def calc_predictor_timestamp(self, encoder_out, encoder_out_lens, token_num):
         encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
             encoder_out.device)
-        ds_alphas, ds_cif_peak, us_alphas, us_cif_peak = self.predictor.get_upsample_timestamp(encoder_out,
+        ds_alphas, ds_cif_peak, us_alphas, us_peaks = self.predictor.get_upsample_timestamp(encoder_out,
                                                                                                encoder_out_mask,
                                                                                                token_num)
-        return ds_alphas, ds_cif_peak, us_alphas, us_cif_peak
+        return ds_alphas, ds_cif_peak, us_alphas, us_peaks
 
     def forward(
             self,

funasr/models/e2e_tp.py

@@ -0,0 +1,175 @@
+import logging
+from contextlib import contextmanager
+from distutils.version import LooseVersion
+from typing import Dict
+from typing import List
+from typing import Optional
+from typing import Tuple
+from typing import Union
+
+import torch
+import numpy as np
+from typeguard import check_argument_types
+
+from funasr.models.encoder.abs_encoder import AbsEncoder
+from funasr.models.frontend.abs_frontend import AbsFrontend
+from funasr.models.predictor.cif import mae_loss
+from funasr.modules.add_sos_eos import add_sos_eos
+from funasr.modules.nets_utils import make_pad_mask, pad_list
+from funasr.torch_utils.device_funcs import force_gatherable
+from funasr.train.abs_espnet_model import AbsESPnetModel
+from funasr.models.predictor.cif import CifPredictorV3
+
+
+if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
+    from torch.cuda.amp import autocast
+else:
+    # Nothing to do if torch<1.6.0
+    @contextmanager
+    def autocast(enabled=True):
+        yield
+
+
+class TimestampPredictor(AbsESPnetModel):
+    """
+    Author: Speech Lab, Alibaba Group, China
+    """
+
+    def __init__(
+            self,
+            frontend: Optional[AbsFrontend],
+            encoder: AbsEncoder,
+            predictor: CifPredictorV3,
+            predictor_bias: int = 0,
+            token_list=None,
+    ):
+        assert check_argument_types()
+
+        super().__init__()
+        # note that eos is the same as sos (equivalent ID)
+
+        self.frontend = frontend
+        self.encoder = encoder
+        self.encoder.interctc_use_conditioning = False
+
+        self.predictor = predictor
+        self.predictor_bias = predictor_bias
+        self.criterion_pre = mae_loss()
+        self.token_list = token_list
+    
+    def forward(
+            self,
+            speech: torch.Tensor,
+            speech_lengths: torch.Tensor,
+            text: torch.Tensor,
+            text_lengths: torch.Tensor,
+    ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
+        """Frontend + Encoder + Decoder + Calc loss
+
+        Args:
+                speech: (Batch, Length, ...)
+                speech_lengths: (Batch, )
+                text: (Batch, Length)
+                text_lengths: (Batch,)
+        """
+        assert text_lengths.dim() == 1, text_lengths.shape
+        # Check that batch_size is unified
+        assert (
+                speech.shape[0]
+                == speech_lengths.shape[0]
+                == text.shape[0]
+                == text_lengths.shape[0]
+        ), (speech.shape, speech_lengths.shape, text.shape, text_lengths.shape)
+        batch_size = speech.shape[0]
+        # for data-parallel
+        text = text[:, : text_lengths.max()]
+        speech = speech[:, :speech_lengths.max()]
+
+        # 1. Encoder
+        encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
+
+        encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
+            encoder_out.device)
+        if self.predictor_bias == 1:
+            _, text = add_sos_eos(text, 1, 2, -1)
+            text_lengths = text_lengths + self.predictor_bias
+        _, _, _, _, pre_token_length2 = self.predictor(encoder_out, text, encoder_out_mask, ignore_id=-1)
+
+        # loss_pre = self.criterion_pre(ys_pad_lens.type_as(pre_token_length), pre_token_length)
+        loss_pre = self.criterion_pre(text_lengths.type_as(pre_token_length2), pre_token_length2)
+
+        loss = loss_pre
+        stats = dict()
+
+        # Collect Attn branch stats
+        stats["loss_pre"] = loss_pre.detach().cpu() if loss_pre is not None else None
+        stats["loss"] = torch.clone(loss.detach())
+
+        # force_gatherable: to-device and to-tensor if scalar for DataParallel
+        loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
+        return loss, stats, weight
+
+    def encode(
+            self, speech: torch.Tensor, speech_lengths: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Frontend + Encoder. Note that this method is used by asr_inference.py
+
+        Args:
+                speech: (Batch, Length, ...)
+                speech_lengths: (Batch, )
+        """
+        with autocast(False):
+            # 1. Extract feats
+            feats, feats_lengths = self._extract_feats(speech, speech_lengths)
+
+        # 4. Forward encoder
+        # feats: (Batch, Length, Dim)
+        # -> encoder_out: (Batch, Length2, Dim2)
+        encoder_out, encoder_out_lens, _ = self.encoder(feats, feats_lengths)
+
+        return encoder_out, encoder_out_lens
+    
+    def _extract_feats(
+            self, speech: torch.Tensor, speech_lengths: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        assert speech_lengths.dim() == 1, speech_lengths.shape
+
+        # for data-parallel
+        speech = speech[:, : speech_lengths.max()]
+        if self.frontend is not None:
+            # Frontend
+            #  e.g. STFT and Feature extract
+            #       data_loader may send time-domain signal in this case
+            # speech (Batch, NSamples) -> feats: (Batch, NFrames, Dim)
+            feats, feats_lengths = self.frontend(speech, speech_lengths)
+        else:
+            # No frontend and no feature extract
+            feats, feats_lengths = speech, speech_lengths
+        return feats, feats_lengths
+
+    def calc_predictor_timestamp(self, encoder_out, encoder_out_lens, token_num):
+        encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
+            encoder_out.device)
+        ds_alphas, ds_cif_peak, us_alphas, us_peaks = self.predictor.get_upsample_timestamp(encoder_out,
+                                                                                               encoder_out_mask,
+                                                                                               token_num)
+        return ds_alphas, ds_cif_peak, us_alphas, us_peaks
+
+    def collect_feats(
+            self,
+            speech: torch.Tensor,
+            speech_lengths: torch.Tensor,
+            text: torch.Tensor,
+            text_lengths: torch.Tensor,
+    ) -> Dict[str, torch.Tensor]:
+        if self.extract_feats_in_collect_stats:
+            feats, feats_lengths = self._extract_feats(speech, speech_lengths)
+        else:
+            # Generate dummy stats if extract_feats_in_collect_stats is False
+            logging.warning(
+                "Generating dummy stats for feats and feats_lengths, "
+                "because encoder_conf.extract_feats_in_collect_stats is "
+                f"{self.extract_feats_in_collect_stats}"
+            )
+            feats, feats_lengths = speech, speech_lengths
+        return {"feats": feats, "feats_lengths": feats_lengths}

funasr/tasks/asr.py

@@ -40,6 +40,7 @@ from funasr.models.decoder.transformer_decoder import TransformerDecoder
 from funasr.models.decoder.contextual_decoder import ContextualParaformerDecoder
 from funasr.models.e2e_asr import ESPnetASRModel
 from funasr.models.e2e_asr_paraformer import Paraformer, ParaformerBert, BiCifParaformer, ContextualParaformer
+from funasr.models.e2e_tp import TimestampPredictor
 from funasr.models.e2e_asr_mfcca import MFCCA
 from funasr.models.e2e_uni_asr import UniASR
 from funasr.models.encoder.abs_encoder import AbsEncoder
@@ -124,6 +125,7 @@ model_choices = ClassChoices(
         bicif_paraformer=BiCifParaformer,
         contextual_paraformer=ContextualParaformer,
         mfcca=MFCCA,
+        timestamp_prediction=TimestampPredictor,
     ),
     type_check=AbsESPnetModel,
     default="asr",
@@ -1245,9 +1247,87 @@ class ASRTaskMFCCA(ASRTask):
 
 
 class ASRTaskAligner(ASRTaskParaformer):
+    # If you need more than one optimizers, change this value
+    num_optimizers: int = 1
+
+    # Add variable objects configurations
+    class_choices_list = [
+        # --frontend and --frontend_conf
+        frontend_choices,
+        # --model and --model_conf
+        model_choices,
+        # --encoder and --encoder_conf
+        encoder_choices,
+        # --decoder and --decoder_conf
+        decoder_choices,
+    ]
+
+    # If you need to modify train() or eval() procedures, change Trainer class here
+    trainer = Trainer
+
+    @classmethod
+    def build_model(cls, args: argparse.Namespace):
+        assert check_argument_types()
+        if isinstance(args.token_list, str):
+            with open(args.token_list, encoding="utf-8") as f:
+                token_list = [line.rstrip() for line in f]
+
+            # Overwriting token_list to keep it as "portable".
+            args.token_list = list(token_list)
+        elif isinstance(args.token_list, (tuple, list)):
+            token_list = list(args.token_list)
+        else:
+            raise RuntimeError("token_list must be str or list")
+
+        # 1. frontend
+        if args.input_size is None:
+            # Extract features in the model
+            frontend_class = frontend_choices.get_class(args.frontend)
+            if args.frontend == 'wav_frontend':
+                frontend = frontend_class(cmvn_file=args.cmvn_file, **args.frontend_conf)
+            else:
+                frontend = frontend_class(**args.frontend_conf)
+            input_size = frontend.output_size()
+        else:
+            # Give features from data-loader
+            args.frontend = None
+            args.frontend_conf = {}
+            frontend = None
+            input_size = args.input_size
+
+        # 2. Encoder
+        encoder_class = encoder_choices.get_class(args.encoder)
+        encoder = encoder_class(input_size=input_size, **args.encoder_conf)
+
+        # 3. Predictor
+        predictor_class = predictor_choices.get_class(args.predictor)
+        predictor = predictor_class(**args.predictor_conf)
+
+        # 10. Build model
+        try:
+            model_class = model_choices.get_class(args.model)
+        except AttributeError:
+            model_class = model_choices.get_class("asr")
+
+        # 8. Build model
+        model = model_class(
+            frontend=frontend,
+            encoder=encoder,
+            predictor=predictor,
+            token_list=token_list,
+            **args.model_conf,
+        )
+
+        # 11. Initialize
+        if args.init is not None:
+            initialize(model, args.init)
+
+        assert check_return_type(model)
+        return model
+
     @classmethod
     def required_data_names(
             cls, train: bool = True, inference: bool = False
     ) -> Tuple[str, ...]:
         retval = ("speech", "text")
-        return retval
+        return retval

funasr/utils/timestamp_tools.py

@@ -5,55 +5,69 @@ import numpy as np
 from typing import Any, List, Tuple, Union
 
 
-def time_stamp_lfr6_pl(us_alphas, us_cif_peak, char_list, begin_time=0.0, end_time=None):
+def ts_prediction_lfr6_standard(us_alphas, 
+                       us_peaks, 
+                       char_list, 
+                       vad_offset=0.0, 
+                       force_time_shift=-1.5
+                       ):
     if not len(char_list):
         return []
     START_END_THRESHOLD = 5
+    MAX_TOKEN_DURATION = 12
     TIME_RATE = 10.0 * 6 / 1000 / 3  #  3 times upsampled
-    if len(us_alphas.shape) == 3:
-        alphas, cif_peak = us_alphas[0], us_cif_peak[0]  # support inference batch_size=1 only
+    if len(us_alphas.shape) == 2:
+        _, peaks = us_alphas[0], us_peaks[0]  # support inference batch_size=1 only
     else:
-        alphas, cif_peak = us_alphas, us_cif_peak
-    num_frames = cif_peak.shape[0]
+        _, peaks = us_alphas, us_peaks
+    num_frames = peaks.shape[0]
     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 = torch.where(cif_peak>1.0-1e-4)[0].cpu().numpy() - 1.5
+    fire_place = torch.where(peaks>1.0-1e-4)[0].cpu().numpy() + force_time_shift  # total offset
     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>')
+        # 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):
-        # the peak is always a little ahead of the start time
-        # timestamp_list.append([(fire_place[i]-1.2)*TIME_RATE, fire_place[i+1]*TIME_RATE])
-        timestamp_list.append([(fire_place[i])*TIME_RATE, fire_place[i+1]*TIME_RATE])
-        # cut the duration to token and sil of the 0-weight frames last long
+        new_char_list.append(char_list[i])
+        if 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
+    # new_char_list.append(char_list[-1])
     if num_frames - fire_place[-1] > START_END_THRESHOLD:
-        _end = (num_frames + fire_place[-1]) / 2
+        _end = (num_frames + fire_place[-1]) * 0.5
+        # _end = fire_place[-1] 
         timestamp_list[-1][1] = _end*TIME_RATE
         timestamp_list.append([_end*TIME_RATE, num_frames*TIME_RATE])
-        char_list.append("<sil>")
+        new_char_list.append("<sil>")
     else:
         timestamp_list[-1][1] = num_frames*TIME_RATE
-    if begin_time:  # add offset time in model with vad
+    if vad_offset:  # 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
+            timestamp_list[i][0] = timestamp_list[i][0] + vad_offset / 1000.0
+            timestamp_list[i][1] = timestamp_list[i][1] + vad_offset / 1000.0
     res_txt = ""
-    for char, timestamp in zip(char_list, timestamp_list):
-        res_txt += "{} {} {};".format(char, timestamp[0], timestamp[1])
+    for char, timestamp in zip(new_char_list, timestamp_list):
+        res_txt += "{} {} {};".format(char, str(timestamp[0]+0.0005)[:5], str(timestamp[1]+0.0005)[:5])
     res = []
-    for char, timestamp in zip(char_list, timestamp_list):
+    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
+    return res_txt, res
 
 
 def time_stamp_sentence(punc_id_list, time_stamp_postprocessed, text_postprocessed):