python/FunASR-XL.git

			@@ -4,134 +4,121 @@
			import numpy as np
			from typing import Any, List, Tuple, Union

			def cut_interval(alphas: torch.Tensor, start: int, end: int, tail: bool):
			if not tail:
			if end == start + 1:
			cut = (end + start) / 2.0
			else:
			alpha = alphas[start+1: end].tolist()
			reverse_steps = 1
			for reverse_alpha in alpha[::-1]:
			if reverse_alpha > 0.35:
			reverse_steps += 1
			else:
			break
			cut = end - reverse_steps
			else:
			if end != len(alphas) - 1:
			cut = end + 1
			else:
			cut = start + 1
			return float(cut)

			def time_stamp_lfr6(alphas: torch.Tensor, speech_lengths: torch.Tensor, raw_text: List[str], begin: int = 0, end: int = None):
			time_stamp_list = []
			alphas = alphas[0]
			text = copy.deepcopy(raw_text)
			if end is None:
			time = speech_lengths * 60 / 1000
			sacle_rate = (time / speech_lengths[0]).tolist()
			else:
			time = (end - begin) / 1000
			sacle_rate = (time / speech_lengths[0]).tolist()

			predictor = (alphas > 0.5).int()
			fire_places = torch.nonzero(predictor == 1).squeeze(1).tolist()

			cuts = []
			npeak = int(predictor.sum())
			nchar = len(raw_text)
			if npeak - 1 == nchar:
			fire_places = torch.where((alphas > 0.5) == 1)[0].tolist()
			for i in range(len(fire_places)):
			if fire_places[i] < len(alphas) - 1:
			if 0.05 < alphas[fire_places[i]+1] < 0.5:
			fire_places[i] += 1
			elif npeak < nchar:
			lost_num = nchar - npeak
			lost_fire = speech_lengths[0].tolist() - fire_places[-1]
			interval_distance = lost_fire // (lost_num + 1)
			for i in range(1, lost_num + 1):
			fire_places.append(fire_places[-1] + interval_distance)
			elif npeak - 1 > nchar:
			redundance_num = npeak - 1 - nchar
			for i in range(redundance_num):
			fire_places.pop()

			cuts.append(0)
			start_sil = True
			if start_sil:
			text.insert(0, '<sil>')

			for i in range(len(fire_places)-1):
			cuts.append(cut_interval(alphas, fire_places[i], fire_places[i+1], tail=(i==len(fire_places)-2)))

			for i in range(2, len(fire_places)-2):
			if fire_places[i-2] == fire_places[i-1] - 1 and fire_places[i-1] != fire_places[i] - 1:
			cuts[i-1] += 1

			if cuts[-1] != len(alphas) - 1:
			text.append('<sil>')
			cuts.append(speech_lengths[0].tolist())
			cuts.insert(-1, (cuts[-1] + cuts[-2]) * 0.5)
			sec_fire_places = np.array(cuts) * sacle_rate
			for i in range(1, len(sec_fire_places) - 1):
			start, end = sec_fire_places[i], sec_fire_places[i+1]
			if i == len(sec_fire_places) - 2:
			end = time
			time_stamp_list.append([int(round(start, 2) * 1000) + begin, int(round(end, 2) * 1000) + begin])
			text = text[1:]
			if npeak - 1 == nchar or npeak > nchar:
			return time_stamp_list[:-1]
			else:
			return time_stamp_list

			def time_stamp_lfr6_pl(us_alphas, us_cif_peak, char_list, begin_time=0.0, end_time=None):
			def ts_prediction_lfr6_standard(us_alphas,
			us_peaks,
			char_list,
			vad_offset=0.0,
			force_time_shift=-1.5
			):
			if not len(char_list):
			return []
			START_END_THRESHOLD = 5
			MAX_TOKEN_DURATION = 12
			TIME_RATE = 10.0 * 6 / 1000 / 3 # 3 times upsampled
			if len(us_alphas.shape) == 3:
			alphas, cif_peak = us_alphas[0], us_cif_peak[0] # support inference batch_size=1 only
			if len(us_alphas.shape) == 2:
			_, peaks = us_alphas[0], us_peaks[0] # support inference batch_size=1 only
			else:
			alphas, cif_peak = us_alphas, us_cif_peak
			num_frames = cif_peak.shape[0]
			_, peaks = us_alphas, us_peaks
			num_frames = peaks.shape[0]
			if char_list[-1] == '</s>':
			char_list = char_list[:-1]
			# char_list = [i for i in text]
			timestamp_list = []
			new_char_list = []
			# for bicif model trained with large data, cif2 actually fires when a character starts
			# so treat the frames between two peaks as the duration of the former token
			fire_place = torch.where(cif_peak>1.0-1e-4)[0].cpu().numpy() - 1.5
			fire_place = torch.where(peaks>1.0-1e-4)[0].cpu().numpy() + force_time_shift # total offset
			num_peak = len(fire_place)
			assert num_peak == len(char_list) + 1 # number of peaks is supposed to be number of tokens + 1
			# begin silence
			if fire_place[0] > START_END_THRESHOLD:
			char_list.insert(0, '<sil>')
			# char_list.insert(0, '<sil>')
			timestamp_list.append([0.0, fire_place[0]*TIME_RATE])
			new_char_list.append('<sil>')
			# tokens timestamp
			for i in range(len(fire_place)-1):
			# the peak is always a little ahead of the start time
			# timestamp_list.append([(fire_place[i]-1.2)TIME_RATE, fire_place[i+1]TIME_RATE])
			timestamp_list.append([(fire_place[i])TIME_RATE, fire_place[i+1]TIME_RATE])
			# cut the duration to token and sil of the 0-weight frames last long
			new_char_list.append(char_list[i])
			if MAX_TOKEN_DURATION < 0 or fire_place[i+1] - fire_place[i] <= MAX_TOKEN_DURATION:
			timestamp_list.append([fire_place[i]TIME_RATE, fire_place[i+1]TIME_RATE])
			else:
			# cut the duration to token and sil of the 0-weight frames last long
			_split = fire_place[i] + MAX_TOKEN_DURATION
			timestamp_list.append([fire_place[i]TIME_RATE, _splitTIME_RATE])
			timestamp_list.append([_splitTIME_RATE, fire_place[i+1]TIME_RATE])
			new_char_list.append('<sil>')
			# tail token and end silence
			# new_char_list.append(char_list[-1])
			if num_frames - fire_place[-1] > START_END_THRESHOLD:
			_end = (num_frames + fire_place[-1]) / 2
			_end = (num_frames + fire_place[-1]) * 0.5
			# _end = fire_place[-1]
			timestamp_list[-1][1] = _end*TIME_RATE
			timestamp_list.append([_endTIME_RATE, num_framesTIME_RATE])
			char_list.append("<sil>")
			new_char_list.append("<sil>")
			else:
			timestamp_list[-1][1] = num_frames*TIME_RATE
			if begin_time: # add offset time in model with vad
			if vad_offset: # add offset time in model with vad
			for i in range(len(timestamp_list)):
			timestamp_list[i][0] = timestamp_list[i][0] + begin_time / 1000.0
			timestamp_list[i][1] = timestamp_list[i][1] + begin_time / 1000.0
			timestamp_list[i][0] = timestamp_list[i][0] + vad_offset / 1000.0
			timestamp_list[i][1] = timestamp_list[i][1] + vad_offset / 1000.0
			res_txt = ""
			for char, timestamp in zip(char_list, timestamp_list):
			res_txt += "{} {} {};".format(char, timestamp[0], timestamp[1])
			logging.warning(res_txt) # for test
			for char, timestamp in zip(new_char_list, timestamp_list):
			res_txt += "{} {} {};".format(char, str(timestamp[0]+0.0005)[:5], str(timestamp[1]+0.0005)[:5])
			res = []
			for char, timestamp in zip(char_list, timestamp_list):
			for char, timestamp in zip(new_char_list, timestamp_list):
			if char != '<sil>':
			res.append([int(timestamp[0] * 1000), int(timestamp[1] * 1000)])
			return res_txt, res


			def time_stamp_sentence(punc_id_list, time_stamp_postprocessed, text_postprocessed):
			res = []
			if text_postprocessed is None:
			return res
			if time_stamp_postprocessed is None:
			return res
			if len(time_stamp_postprocessed) == 0:
			return res
			if len(text_postprocessed) == 0:
			return res
			if punc_id_list is None or len(punc_id_list) == 0:
			res.append({
			'text': text_postprocessed.split(),
			"start": time_stamp_postprocessed[0][0],
			"end": time_stamp_postprocessed[-1][1]
			})
			return res
			if len(punc_id_list) != len(time_stamp_postprocessed):
			res.append({
			'text': text_postprocessed.split(),
			"start": time_stamp_postprocessed[0][0],
			"end": time_stamp_postprocessed[-1][1]
			})
			return res

			sentence_text = ''
			sentence_start = time_stamp_postprocessed[0][0]
			texts = text_postprocessed.split()
			for i in range(len(punc_id_list)):
			sentence_text += texts[i]
			if punc_id_list[i] == 2:
			sentence_text += ','
			res.append({
			'text': sentence_text,
			"start": sentence_start,
			"end": time_stamp_postprocessed[i][1]
			})
			sentence_text = ''
			sentence_start = time_stamp_postprocessed[i][1]
			elif punc_id_list[i] == 3:
			sentence_text += '.'
			res.append({
			'text': sentence_text,
			"start": sentence_start,
			"end": time_stamp_postprocessed[i][1]
			})
			sentence_text = ''
			sentence_start = time_stamp_postprocessed[i][1]
			return res