From 94de39dde2e616a01683c518023d0fab72b4e103 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 19 二月 2024 22:21:50 +0800
Subject: [PATCH] aishell example
---
funasr/utils/timestamp_tools.py | 213 ++++-------------------------------------------------
1 files changed, 17 insertions(+), 196 deletions(-)
diff --git a/funasr/utils/timestamp_tools.py b/funasr/utils/timestamp_tools.py
index c194179..63f179a 100644
--- a/funasr/utils/timestamp_tools.py
+++ b/funasr/utils/timestamp_tools.py
@@ -3,7 +3,7 @@
import logging
import argparse
import numpy as np
-import edit_distance
+# import edit_distance
from itertools import zip_longest
@@ -19,7 +19,7 @@
list_fires.append(integrate)
fire_place = integrate >= threshold
integrate = torch.where(fire_place,
- integrate - torch.ones([batch_size], device=alphas.device),
+ integrate - torch.ones([batch_size], device=alphas.device)*threshold,
integrate)
fires = torch.stack(list_fires, 1)
return fires
@@ -98,14 +98,14 @@
return res_txt, res
-def time_stamp_sentence(punc_id_list, time_stamp_postprocessed, text_postprocessed):
+def timestamp_sentence(punc_id_list, timestamp_postprocessed, text_postprocessed):
punc_list = ['锛�', '銆�', '锛�', '銆�']
res = []
if text_postprocessed is None:
return res
- if time_stamp_postprocessed is None:
+ if timestamp_postprocessed is None:
return res
- if len(time_stamp_postprocessed) == 0:
+ if len(timestamp_postprocessed) == 0:
return res
if len(text_postprocessed) == 0:
return res
@@ -113,23 +113,22 @@
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],
- 'text_seg': text_postprocessed.split(),
- "ts_list": time_stamp_postprocessed,
+ "start": timestamp_postprocessed[0][0],
+ "end": timestamp_postprocessed[-1][1],
+ "timestamp": timestamp_postprocessed,
})
return res
- if len(punc_id_list) != len(time_stamp_postprocessed):
- print(" warning length mistach!!!!!!")
+ if len(punc_id_list) != len(timestamp_postprocessed):
+ logging.warning("length mismatch between punc and timestamp")
sentence_text = ""
sentence_text_seg = ""
ts_list = []
- sentence_start = time_stamp_postprocessed[0][0]
- sentence_end = time_stamp_postprocessed[0][1]
+ sentence_start = timestamp_postprocessed[0][0]
+ sentence_end = timestamp_postprocessed[0][1]
texts = text_postprocessed.split()
- punc_stamp_text_list = list(zip_longest(punc_id_list, time_stamp_postprocessed, texts, fillvalue=None))
+ punc_stamp_text_list = list(zip_longest(punc_id_list, timestamp_postprocessed, texts, fillvalue=None))
for punc_stamp_text in punc_stamp_text_list:
- punc_id, time_stamp, text = punc_stamp_text
+ punc_id, timestamp, text = punc_stamp_text
# sentence_text += text if text is not None else ''
if text is not None:
if 'a' <= text[0] <= 'z' or 'A' <= text[0] <= 'Z':
@@ -139,10 +138,10 @@
else:
sentence_text += text
sentence_text_seg += text + ' '
- ts_list.append(time_stamp)
+ ts_list.append(timestamp)
punc_id = int(punc_id) if punc_id is not None else 1
- sentence_end = time_stamp[1] if time_stamp is not None else sentence_end
+ sentence_end = timestamp[1] if timestamp is not None else sentence_end
if punc_id > 1:
sentence_text += punc_list[punc_id - 2]
@@ -150,8 +149,7 @@
'text': sentence_text,
"start": sentence_start,
"end": sentence_end,
- "text_seg": sentence_text_seg,
- "ts_list": ts_list
+ "timestamp": ts_list
})
sentence_text = ''
sentence_text_seg = ''
@@ -160,181 +158,4 @@
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)
--
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