import torch
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import copy
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import logging
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import numpy as np
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from typing import Any, List, Tuple, Union
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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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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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predictor = (alphas > 0.5).int()
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fire_places = torch.nonzero(predictor == 1).squeeze(1).tolist()
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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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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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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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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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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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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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if len(us_alphas.shape) == 3:
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alphas, cif_peak = us_alphas[0], us_cif_peak[0] # support inference batch_size=1 only
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else:
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alphas, cif_peak = us_alphas, us_cif_peak
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num_frames = cif_peak.shape[0]
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if char_list[-1] == '</s>':
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char_list = char_list[:-1]
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# char_list = [i for i in text]
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timestamp_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 = torch.where(cif_peak>1.0-1e-4)[0].cpu().numpy() - 1.5
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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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if fire_place[0] > START_END_THRESHOLD:
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char_list.insert(0, '<sil>')
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timestamp_list.append([0.0, fire_place[0]*TIME_RATE])
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# tokens timestamp
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for i in range(len(fire_place)-1):
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# the peak is always a little ahead of the start time
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# timestamp_list.append([(fire_place[i]-1.2)*TIME_RATE, fire_place[i+1]*TIME_RATE])
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timestamp_list.append([(fire_place[i])*TIME_RATE, fire_place[i+1]*TIME_RATE])
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# cut the duration to token and sil of the 0-weight frames last long
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# tail token and end silence
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if num_frames - fire_place[-1] > START_END_THRESHOLD:
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_end = (num_frames + fire_place[-1]) / 2
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timestamp_list[-1][1] = _end*TIME_RATE
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timestamp_list.append([_end*TIME_RATE, num_frames*TIME_RATE])
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char_list.append("<sil>")
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else:
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timestamp_list[-1][1] = num_frames*TIME_RATE
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if begin_time: # add offset time in model with vad
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for i in range(len(timestamp_list)):
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timestamp_list[i][0] = timestamp_list[i][0] + begin_time / 1000.0
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timestamp_list[i][1] = timestamp_list[i][1] + begin_time / 1000.0
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res_txt = ""
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for char, timestamp in zip(char_list, timestamp_list):
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res_txt += "{} {} {};".format(char, timestamp[0], timestamp[1])
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logging.warning(res_txt) # for test
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res = []
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for char, timestamp in zip(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
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