| | |
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| | | import numpy as np |
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| | | from typing import Any, List, Tuple, Union |
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| | | |
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| | | def cut_interval(alphas: torch.Tensor, start: int, end: int, tail: bool): |
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| | | if not tail: |
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| | | if end == start + 1: |
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| | | cut = (end + start) / 2.0 |
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| | | else: |
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| | | alpha = alphas[start+1: end].tolist() |
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| | | reverse_steps = 1 |
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| | | for reverse_alpha in alpha[::-1]: |
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| | | if reverse_alpha > 0.35: |
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| | | reverse_steps += 1 |
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| | | else: |
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| | | break |
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| | | cut = end - reverse_steps |
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| | | else: |
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| | | if end != len(alphas) - 1: |
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| | | cut = end + 1 |
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| | | else: |
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| | | cut = start + 1 |
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| | | return float(cut) |
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| | | |
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| | | def time_stamp_lfr6(alphas: torch.Tensor, speech_lengths: torch.Tensor, raw_text: List[str], begin: int = 0, end: int = None): |
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| | | time_stamp_list = [] |
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| | | alphas = alphas[0] |
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| | | text = copy.deepcopy(raw_text) |
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| | | if end is None: |
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| | | time = speech_lengths * 60 / 1000 |
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| | | sacle_rate = (time / speech_lengths[0]).tolist() |
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| | | else: |
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| | | time = (end - begin) / 1000 |
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| | | sacle_rate = (time / speech_lengths[0]).tolist() |
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| | | |
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| | | predictor = (alphas > 0.5).int() |
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| | | fire_places = torch.nonzero(predictor == 1).squeeze(1).tolist() |
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| | | |
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| | | cuts = [] |
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| | | npeak = int(predictor.sum()) |
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| | | nchar = len(raw_text) |
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| | | if npeak - 1 == nchar: |
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| | | fire_places = torch.where((alphas > 0.5) == 1)[0].tolist() |
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| | | for i in range(len(fire_places)): |
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| | | if fire_places[i] < len(alphas) - 1: |
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| | | if 0.05 < alphas[fire_places[i]+1] < 0.5: |
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| | | fire_places[i] += 1 |
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| | | elif npeak < nchar: |
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| | | lost_num = nchar - npeak |
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| | | lost_fire = speech_lengths[0].tolist() - fire_places[-1] |
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| | | interval_distance = lost_fire // (lost_num + 1) |
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| | | for i in range(1, lost_num + 1): |
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| | | fire_places.append(fire_places[-1] + interval_distance) |
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| | | elif npeak - 1 > nchar: |
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| | | redundance_num = npeak - 1 - nchar |
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| | | for i in range(redundance_num): |
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| | | fire_places.pop() |
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| | | |
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| | | cuts.append(0) |
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| | | start_sil = True |
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| | | if start_sil: |
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| | | text.insert(0, '<sil>') |
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| | | |
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| | | for i in range(len(fire_places)-1): |
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| | | cuts.append(cut_interval(alphas, fire_places[i], fire_places[i+1], tail=(i==len(fire_places)-2))) |
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| | | |
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| | | for i in range(2, len(fire_places)-2): |
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| | | if fire_places[i-2] == fire_places[i-1] - 1 and fire_places[i-1] != fire_places[i] - 1: |
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| | | cuts[i-1] += 1 |
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| | | |
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| | | if cuts[-1] != len(alphas) - 1: |
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| | | text.append('<sil>') |
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| | | cuts.append(speech_lengths[0].tolist()) |
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| | | cuts.insert(-1, (cuts[-1] + cuts[-2]) * 0.5) |
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| | | sec_fire_places = np.array(cuts) * sacle_rate |
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| | | for i in range(1, len(sec_fire_places) - 1): |
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| | | start, end = sec_fire_places[i], sec_fire_places[i+1] |
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| | | if i == len(sec_fire_places) - 2: |
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| | | end = time |
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| | | time_stamp_list.append([int(round(start, 2) * 1000) + begin, int(round(end, 2) * 1000) + begin]) |
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| | | text = text[1:] |
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| | | if npeak - 1 == nchar or npeak > nchar: |
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| | | return time_stamp_list[:-1] |
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| | | else: |
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| | | return time_stamp_list |
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| | | |
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| | | def time_stamp_lfr6_pl(us_alphas, us_cif_peak, char_list, begin_time=0.0, end_time=None): |
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| | | START_END_THRESHOLD = 5 |
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| | | TIME_RATE = 10.0 * 6 / 1000 / 3 # 3 times upsampled |
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