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| | | from itertools import zip_longest |
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| | | |
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| | | import torch |
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| | | import copy |
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| | | import codecs |
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| | | import logging |
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| | | import edit_distance |
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| | | import argparse |
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| | | import pdb |
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| | | import numpy as np |
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| | | from typing import Any, List, Tuple, Union |
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| | | import edit_distance |
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| | | from itertools import zip_longest |
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| | | |
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| | | def cif_wo_hidden(alphas, threshold): |
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| | | batch_size, len_time = alphas.size() |
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| | | # loop varss |
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| | | integrate = torch.zeros([batch_size], device=alphas.device) |
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| | | # intermediate vars along time |
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| | | list_fires = [] |
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| | | for t in range(len_time): |
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| | | alpha = alphas[:, t] |
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| | | integrate += alpha |
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| | | list_fires.append(integrate) |
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| | | fire_place = integrate >= threshold |
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| | | integrate = torch.where(fire_place, |
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| | | integrate - torch.ones([batch_size], device=alphas.device)*threshold, |
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| | | integrate) |
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| | | fires = torch.stack(list_fires, 1) |
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| | | return fires |
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| | | |
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| | | def ts_prediction_lfr6_standard(us_alphas, |
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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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| | | alphas, 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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| | | alphas, peaks = us_alphas, us_peaks |
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| | | if char_list[-1] == '</s>': |
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| | | char_list = char_list[:-1] |
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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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| | | if len(fire_place) != len(char_list) + 1: |
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| | | alphas /= (alphas.sum() / (len(char_list) + 1)) |
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| | | alphas = alphas.unsqueeze(0) |
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| | | peaks = cif_wo_hidden(alphas, threshold=1.0-1e-4)[0] |
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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_frames = peaks.shape[0] |
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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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| | | # 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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