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