update paraformer timestamp

维石

2024-06-06 ce6b70e4795fb5afb685d1fead898589c4970990

update paraformer timestamp

 funasr/models/paraformer/model.py

@@ -4,6 +4,7 @@
#  MIT License  (https://opensource.org/licenses/MIT)

import time
import copy
import torch
import logging
from torch.cuda.amp import autocast
@@ -21,6 +22,7 @@
from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
from funasr.models.transformer.utils.nets_utils import make_pad_mask
from funasr.utils.timestamp_tools import ts_prediction_lfr6_standard
from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank


@@ -452,6 +454,7 @@
        is_use_lm = (
            kwargs.get("lm_weight", 0.0) > 0.00001 and kwargs.get("lm_file", None) is not None
        )
        pred_timestamp = kwargs.get("pred_timestamp", False)
        if self.beam_search is None and (is_use_lm or is_use_ctc):
            logging.info("enable beam_search")
            self.init_beam_search(**kwargs)
@@ -506,6 +509,7 @@
            predictor_outs[2],
            predictor_outs[3],
        )
        
        pre_token_length = pre_token_length.round().long()
        if torch.max(pre_token_length) < 1:
            return []
@@ -564,10 +568,22 @@
                    # Change integer-ids to tokens
                    token = tokenizer.ids2tokens(token_int)
                    text_postprocessed = tokenizer.tokens2text(token)
                    if not hasattr(tokenizer, "bpemodel"):
                        text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)

                    result_i = {"key": key[i], "text": text_postprocessed}
                    
                    if pred_timestamp:
                        timestamp_str, timestamp = ts_prediction_lfr6_standard(
                            pre_peak_index[i],
                            alphas[i],
                            copy.copy(token),
                            vad_offset=kwargs.get("begin_time", 0),
                            upsample_rate=1,
                        )
                        if not hasattr(tokenizer, "bpemodel"):
                            text_postprocessed, time_stamp_postprocessed, _ = postprocess_utils.sentence_postprocess(token, timestamp)
                        result_i = {"key": key[i], "text": text_postprocessed, "timestamp": time_stamp_postprocessed,}
                    else:
                        if not hasattr(tokenizer, "bpemodel"):
                            text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
                        result_i = {"key": key[i], "text": text_postprocessed}

                    if ibest_writer is not None:
                        ibest_writer["token"][key[i]] = " ".join(token)

 funasr/utils/timestamp_tools.py

@@ -29,13 +29,13 @@


def ts_prediction_lfr6_standard(
    us_alphas, us_peaks, char_list, vad_offset=0.0, force_time_shift=-1.5, sil_in_str=True
    us_alphas, us_peaks, char_list, vad_offset=0.0, force_time_shift=-1.5, sil_in_str=True, upsample_rate=3,
):
    if not len(char_list):
        return "", []
    START_END_THRESHOLD = 5
    MAX_TOKEN_DURATION = 12
    TIME_RATE = 10.0 * 6 / 1000 / 3  #  3 times upsampled
    MAX_TOKEN_DURATION = 12  #  3 times upsampled
    TIME_RATE=10.0 * 6 / 1000 / upsample_rate
    if len(us_alphas.shape) == 2:
        alphas, peaks = us_alphas[0], us_peaks[0]  # support inference batch_size=1 only
    else: