python/FunASR-XL.git

			@@ -1,6 +1,10 @@
			import torch
			import copy
			import codecs
			import logging
			import edit_distance
			import argparse
			import pdb
			import numpy as np
			from typing import Any, List, Tuple, Union

			@@ -9,7 +13,8 @@
			us_peaks,
			char_list,
			vad_offset=0.0,
			force_time_shift=-1.5
			force_time_shift=-1.5,
			sil_in_str=True
			):
			if not len(char_list):
			return []
			@@ -62,6 +67,8 @@
			timestamp_list[i][1] = timestamp_list[i][1] + vad_offset / 1000.0
			res_txt = ""
			for char, timestamp in zip(new_char_list, timestamp_list):
			#if char != '<sil>':
			if not sil_in_str and char == '<sil>': continue
			res_txt += "{} {} {};".format(char, str(timestamp[0]+0.0005)[:5], str(timestamp[1]+0.0005)[:5])
			res = []
			for char, timestamp in zip(new_char_list, timestamp_list):
			@@ -121,4 +128,181 @@
			return res


			class AverageShiftCalculator():
			def __init__(self):
			logging.warning("Calculating average shift.")
			def __call__(self, file1, file2):
			uttid_list1, ts_dict1 = self.read_timestamps(file1)
			uttid_list2, ts_dict2 = self.read_timestamps(file2)
			uttid_intersection = self._intersection(uttid_list1, uttid_list2)
			res = self.as_cal(uttid_intersection, ts_dict1, ts_dict2)
			logging.warning("Average shift of {} and {}: {}.".format(file1, file2, str(res)[:8]))
			logging.warning("Following timestamp pair differs most: {}, detail:{}".format(self.max_shift, self.max_shift_uttid))

			def _intersection(self, list1, list2):
			set1 = set(list1)
			set2 = set(list2)
			if set1 == set2:
			logging.warning("Uttid same checked.")
			return set1
			itsc = list(set1 & set2)
			logging.warning("Uttid differs: file1 {}, file2 {}, lines same {}.".format(len(list1), len(list2), len(itsc)))
			return itsc

			def read_timestamps(self, file):
			# read timestamps file in standard format
			uttid_list = []
			ts_dict = {}
			with codecs.open(file, 'r') as fin:
			for line in fin.readlines():
			text = ''
			ts_list = []
			line = line.rstrip()
			uttid = line.split()[0]
			uttid_list.append(uttid)
			body = " ".join(line.split()[1:])
			for pd in body.split(';'):
			if not len(pd): continue
			# pdb.set_trace()
			char, start, end = pd.lstrip(" ").split(' ')
			text += char + ','
			ts_list.append((float(start), float(end)))
			# ts_lists.append(ts_list)
			ts_dict[uttid] = (text[:-1], ts_list)
			logging.warning("File {} read done.".format(file))
			return uttid_list, ts_dict

			def _shift(self, filtered_timestamp_list1, filtered_timestamp_list2):
			shift_time = 0
			for fts1, fts2 in zip(filtered_timestamp_list1, filtered_timestamp_list2):
			shift_time += abs(fts1[0] - fts2[0]) + abs(fts1[1] - fts2[1])
			num_tokens = len(filtered_timestamp_list1)
			return shift_time, num_tokens

			def as_cal(self, uttid_list, ts_dict1, ts_dict2):
			# calculate average shift between timestamp1 and timestamp2
			# when characters differ, use edit distance alignment
			# and calculate the error between the same characters
			self._accumlated_shift = 0
			self._accumlated_tokens = 0
			self.max_shift = 0
			self.max_shift_uttid = None
			for uttid in uttid_list:
			(t1, ts1) = ts_dict1[uttid]
			(t2, ts2) = ts_dict2[uttid]
			_align, _align2, _align3 = [], [], []
			fts1, fts2 = [], []
			_t1, _t2 = [], []
			sm = edit_distance.SequenceMatcher(t1.split(','), t2.split(','))
			s = sm.get_opcodes()
			for j in range(len(s)):
			if s[j][0] == "replace" or s[j][0] == "insert":
			_align.append(0)
			if s[j][0] == "replace" or s[j][0] == "delete":
			_align3.append(0)
			elif s[j][0] == "equal":
			_align.append(1)
			_align3.append(1)
			else:
			continue
			# use s to index t2
			for a, ts , t in zip(_align, ts2, t2.split(',')):
			if a:
			fts2.append(ts)
			_t2.append(t)
			sm2 = edit_distance.SequenceMatcher(t2.split(','), t1.split(','))
			s = sm2.get_opcodes()
			for j in range(len(s)):
			if s[j][0] == "replace" or s[j][0] == "insert":
			_align2.append(0)
			elif s[j][0] == "equal":
			_align2.append(1)
			else:
			continue
			# use s2 tp index t1
			for a, ts, t in zip(_align3, ts1, t1.split(',')):
			if a:
			fts1.append(ts)
			_t1.append(t)
			if len(fts1) == len(fts2):
			shift_time, num_tokens = self._shift(fts1, fts2)
			self._accumlated_shift += shift_time
			self._accumlated_tokens += num_tokens
			if shift_time/num_tokens > self.max_shift:
			self.max_shift = shift_time/num_tokens
			self.max_shift_uttid = uttid
			else:
			logging.warning("length mismatch")
			return self._accumlated_shift / self._accumlated_tokens


			def convert_external_alphas(alphas_file, text_file, output_file):
			from funasr.models.predictor.cif import cif_wo_hidden
			with open(alphas_file, 'r') as f1, open(text_file, 'r') as f2, open(output_file, 'w') as f3:
			for line1, line2 in zip(f1.readlines(), f2.readlines()):
			line1 = line1.rstrip()
			line2 = line2.rstrip()
			assert line1.split()[0] == line2.split()[0]
			uttid = line1.split()[0]
			alphas = [float(i) for i in line1.split()[1:]]
			new_alphas = np.array(remove_chunk_padding(alphas))
			new_alphas[-1] += 1e-4
			text = line2.split()[1:]
			if len(text) + 1 != int(new_alphas.sum()):
			# force resize
			new_alphas *= (len(text) + 1) / int(new_alphas.sum())
			peaks = cif_wo_hidden(torch.Tensor(new_alphas).unsqueeze(0), 1.0-1e-4)
			if " " in text:
			text = text.split()
			else:
			text = [i for i in text]
			res_str, _ = ts_prediction_lfr6_standard(new_alphas, peaks[0], text,
			force_time_shift=-7.0,
			sil_in_str=False)
			f3.write("{} {}\n".format(uttid, res_str))


			def remove_chunk_padding(alphas):
			# remove the padding part in alphas if using chunk paraformer for GPU
			START_ZERO = 45
			MID_ZERO = 75
			REAL_FRAMES = 360 # for chunk based encoder 10-120-10 and fsmn padding 5
			alphas = alphas[START_ZERO:] # remove the padding at beginning
			new_alphas = []
			while True:
			new_alphas = new_alphas + alphas[:REAL_FRAMES]
			alphas = alphas[REAL_FRAMES+MID_ZERO:]
			if len(alphas) < REAL_FRAMES: break
			return new_alphas

			SUPPORTED_MODES = ['cal_aas', 'read_ext_alphas']


			def main(args):
			if args.mode == 'cal_aas':
			asc = AverageShiftCalculator()
			asc(args.input, args.input2)
			elif args.mode == 'read_ext_alphas':
			convert_external_alphas(args.input, args.input2, args.output)
			else:
			logging.error("Mode {} not in SUPPORTED_MODES: {}.".format(args.mode, SUPPORTED_MODES))


			if __name__ == '__main__':
			parser = argparse.ArgumentParser(description='timestamp tools')
			parser.add_argument('--mode',
			default=None,
			type=str,
			choices=SUPPORTED_MODES,
			help='timestamp related toolbox')
			parser.add_argument('--input', default=None, type=str, help='input file path')
			parser.add_argument('--output', default=None, type=str, help='output file name')
			parser.add_argument('--input2', default=None, type=str, help='input2 file path')
			parser.add_argument('--kaldi-ts-type',
			default='v2',
			type=str,
			choices=['v0', 'v1', 'v2'],
			help='kaldi timestamp to write')
			args = parser.parse_args()
			main(args)