From 03d4ce829814b4a7f57235fda049351c524ba32b Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期五, 17 三月 2023 14:06:56 +0800
Subject: [PATCH] Merge branch 'main' into dev_xw
---
funasr/utils/timestamp_tools.py | 240 ++++++++++++++++++++++++++++++++++++++++++++++++++++++-----
1 files changed, 219 insertions(+), 21 deletions(-)
diff --git a/funasr/utils/timestamp_tools.py b/funasr/utils/timestamp_tools.py
index 4a367f8..423110c 100644
--- a/funasr/utils/timestamp_tools.py
+++ b/funasr/utils/timestamp_tools.py
@@ -1,59 +1,80 @@
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
-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,
+ sil_in_str=True
+ ):
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, _split*TIME_RATE])
+ timestamp_list.append([_split*TIME_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([_end*TIME_RATE, num_frames*TIME_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])
+ 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(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
+ return res_txt, res
def time_stamp_sentence(punc_id_list, time_stamp_postprocessed, text_postprocessed):
@@ -107,4 +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)
--
Gitblit v1.9.1