188 lines
7.1 KiB
Python
188 lines
7.1 KiB
Python
import torch
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import codecs
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import logging
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import argparse
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import numpy as np
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# import edit_distance
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from itertools import zip_longest
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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(
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fire_place,
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integrate - torch.ones([batch_size], device=alphas.device) * threshold,
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integrate,
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)
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fires = torch.stack(list_fires, 1)
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return fires
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def ts_prediction_lfr6_standard(
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us_alphas, us_peaks, char_list, vad_offset=0.0, force_time_shift=-1.5, sil_in_str=True
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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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alphas, peaks = us_alphas[0], us_peaks[0] # support inference batch_size=1 only
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else:
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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 = (
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torch.where(peaks >= 1.0 - 1e-4)[0].cpu().numpy() + force_time_shift
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) # 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 = (
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torch.where(peaks >= 1.0 - 1e-4)[0].cpu().numpy() + force_time_shift
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) # 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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# 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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# if char != '<sil>':
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if not sil_in_str and char == "<sil>":
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continue
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res_txt += "{} {} {};".format(
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char, str(timestamp[0] + 0.0005)[:5], str(timestamp[1] + 0.0005)[:5]
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)
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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 timestamp_sentence(
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punc_id_list, timestamp_postprocessed, text_postprocessed, return_raw_text=False
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):
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punc_list = ["，", "。", "？", "、"]
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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 timestamp_postprocessed is None:
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return res
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if len(timestamp_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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{
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"text": text_postprocessed.split(),
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"start": timestamp_postprocessed[0][0],
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"end": timestamp_postprocessed[-1][1],
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"timestamp": timestamp_postprocessed,
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}
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)
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return res
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if len(punc_id_list) != len(timestamp_postprocessed):
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logging.warning("length mismatch between punc and timestamp")
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sentence_text = ""
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sentence_text_seg = ""
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ts_list = []
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sentence_start = timestamp_postprocessed[0][0]
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sentence_end = timestamp_postprocessed[0][1]
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texts = text_postprocessed.split()
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punc_stamp_text_list = list(
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zip_longest(punc_id_list, timestamp_postprocessed, texts, fillvalue=None)
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)
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for punc_stamp_text in punc_stamp_text_list:
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punc_id, timestamp, text = punc_stamp_text
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# sentence_text += text if text is not None else ''
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if text is not None:
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if "a" <= text[0] <= "z" or "A" <= text[0] <= "Z":
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sentence_text += " " + text
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elif len(sentence_text) and (
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"a" <= sentence_text[-1] <= "z" or "A" <= sentence_text[-1] <= "Z"
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):
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sentence_text += " " + text
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else:
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sentence_text += text
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sentence_text_seg += text + " "
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ts_list.append(timestamp)
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punc_id = int(punc_id) if punc_id is not None else 1
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sentence_end = timestamp[1] if timestamp is not None else sentence_end
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sentence_text_seg = (
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sentence_text_seg[:-1] if sentence_text_seg[-1] == " " else sentence_text_seg
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)
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if punc_id > 1:
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sentence_text += punc_list[punc_id - 2]
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if return_raw_text:
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res.append(
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{
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"text": sentence_text,
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"start": sentence_start,
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"end": sentence_end,
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"timestamp": ts_list,
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"raw_text": sentence_text_seg,
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}
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)
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else:
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res.append(
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{
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"text": sentence_text,
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"start": sentence_start,
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"end": sentence_end,
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"timestamp": ts_list,
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}
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)
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sentence_text = ""
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sentence_text_seg = ""
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ts_list = []
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sentence_start = sentence_end
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return res
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