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 ts_prediction_lfr6_standard(us_alphas,
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us_peaks,
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char_list,
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vad_offset=0.0,
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force_time_shift=-1.5
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):
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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 = 12
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TIME_RATE = 10.0 * 6 / 1000 / 3 # 3 times upsampled
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if len(us_alphas.shape) == 2:
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_, peaks = us_alphas[0], us_peaks[0] # support inference batch_size=1 only
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else:
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_, peaks = us_alphas, us_peaks
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num_frames = peaks.shape[0]
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if char_list[-1] == '</s>':
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char_list = char_list[:-1]
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timestamp_list = []
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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 = torch.where(peaks>1.0-1e-4)[0].cpu().numpy() + force_time_shift # total offset
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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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new_char_list.append('<sil>')
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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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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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_split = fire_place[i] + MAX_TOKEN_DURATION
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timestamp_list.append([fire_place[i]*TIME_RATE, _split*TIME_RATE])
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timestamp_list.append([_split*TIME_RATE, fire_place[i+1]*TIME_RATE])
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new_char_list.append('<sil>')
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# tail token and end silence
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# new_char_list.append(char_list[-1])
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if num_frames - fire_place[-1] > START_END_THRESHOLD:
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_end = (num_frames + fire_place[-1]) * 0.5
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# _end = fire_place[-1]
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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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new_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 vad_offset: # 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] + vad_offset / 1000.0
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timestamp_list[i][1] = timestamp_list[i][1] + vad_offset / 1000.0
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res_txt = ""
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for char, timestamp in zip(new_char_list, timestamp_list):
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res_txt += "{} {} {};".format(char, str(timestamp[0]+0.0005)[:5], str(timestamp[1]+0.0005)[:5])
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res = []
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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_txt, res
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def time_stamp_sentence(punc_id_list, time_stamp_postprocessed, text_postprocessed):
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res = []
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if text_postprocessed is None:
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return res
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if time_stamp_postprocessed is None:
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return res
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if len(time_stamp_postprocessed) == 0:
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return res
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if len(text_postprocessed) == 0:
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return res
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if punc_id_list is None or len(punc_id_list) == 0:
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res.append({
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'text': text_postprocessed.split(),
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"start": time_stamp_postprocessed[0][0],
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"end": time_stamp_postprocessed[-1][1]
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})
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return res
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if len(punc_id_list) != len(time_stamp_postprocessed):
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res.append({
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'text': text_postprocessed.split(),
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"start": time_stamp_postprocessed[0][0],
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"end": time_stamp_postprocessed[-1][1]
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})
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return res
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sentence_text = ''
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sentence_start = time_stamp_postprocessed[0][0]
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texts = text_postprocessed.split()
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for i in range(len(punc_id_list)):
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sentence_text += texts[i]
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if punc_id_list[i] == 2:
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sentence_text += ','
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res.append({
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'text': sentence_text,
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"start": sentence_start,
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"end": time_stamp_postprocessed[i][1]
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})
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sentence_text = ''
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sentence_start = time_stamp_postprocessed[i][1]
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elif punc_id_list[i] == 3:
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sentence_text += '.'
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res.append({
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'text': sentence_text,
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"start": sentence_start,
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"end": time_stamp_postprocessed[i][1]
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})
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sentence_text = ''
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sentence_start = time_stamp_postprocessed[i][1]
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return res
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