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| | | import numpy as np |
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| | | def time_stamp_lfr6_onnx(us_cif_peak, char_list, begin_time=0.0): |
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| | | def time_stamp_lfr6_onnx(us_cif_peak, char_list, begin_time=0.0, total_offset=-1.5): |
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| | | if not len(char_list): |
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| | | return [] |
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| | | START_END_THRESHOLD = 5 |
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| | | MAX_TOKEN_DURATION = 14 |
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| | | MAX_TOKEN_DURATION = 30 |
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| | | TIME_RATE = 10.0 * 6 / 1000 / 3 # 3 times upsampled |
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| | | cif_peak = us_cif_peak.reshape(-1) |
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| | | num_frames = cif_peak.shape[-1] |
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| | |
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| | | new_char_list = [] |
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| | | # for bicif model trained with large data, cif2 actually fires when a character starts |
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| | | # so treat the frames between two peaks as the duration of the former token |
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| | | fire_place = np.where(cif_peak>1.0-1e-4)[0] - 1.5 # np format |
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| | | fire_place = np.where(cif_peak>1.0-1e-4)[0] + total_offset # np format |
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| | | num_peak = len(fire_place) |
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| | | assert num_peak == len(char_list) + 1 # number of peaks is supposed to be number of tokens + 1 |
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| | | # begin silence |
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| | |
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| | | # tokens timestamp |
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| | | for i in range(len(fire_place)-1): |
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| | | new_char_list.append(char_list[i]) |
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| | | if MAX_TOKEN_DURATION < 0 or fire_place[i+1] - fire_place[i] < MAX_TOKEN_DURATION: |
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| | | if i == len(fire_place)-2 or MAX_TOKEN_DURATION < 0 or fire_place[i+1] - fire_place[i] < MAX_TOKEN_DURATION: |
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| | | timestamp_list.append([fire_place[i]*TIME_RATE, fire_place[i+1]*TIME_RATE]) |
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| | | else: |
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| | | # cut the duration to token and sil of the 0-weight frames last long |
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| | |
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| | | for char, timestamp in zip(new_char_list, timestamp_list): |
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| | | if char != '<sil>': |
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| | | res.append([int(timestamp[0] * 1000), int(timestamp[1] * 1000)]) |
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| | | return res, res_total |
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| | | return res, res_total |
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