From e30a17cf4e715b3d139fa1e0ba01cda1bcf0f884 Mon Sep 17 00:00:00 2001
From: shixian.shi <shixian.shi@alibaba-inc.com>
Date: 星期三, 10 一月 2024 11:23:41 +0800
Subject: [PATCH] update funasr-onnx
---
funasr/utils/timestamp_tools.py | 359 ++++++++++++++++++++++++++++++++++++-----------------------
1 files changed, 218 insertions(+), 141 deletions(-)
diff --git a/funasr/utils/timestamp_tools.py b/funasr/utils/timestamp_tools.py
index 7ba3e0b..8186dff 100644
--- a/funasr/utils/timestamp_tools.py
+++ b/funasr/utils/timestamp_tools.py
@@ -1,39 +1,61 @@
-from scipy.fftpack import shift
import torch
-import copy
import codecs
import logging
-import edit_distance
import argparse
import numpy as np
-from typing import Any, List, Tuple, Union
+# import edit_distance
+from itertools import zip_longest
+
+
+def cif_wo_hidden(alphas, threshold):
+ batch_size, len_time = alphas.size()
+ # loop varss
+ integrate = torch.zeros([batch_size], device=alphas.device)
+ # intermediate vars along time
+ list_fires = []
+ for t in range(len_time):
+ alpha = alphas[:, t]
+ integrate += alpha
+ list_fires.append(integrate)
+ fire_place = integrate >= threshold
+ integrate = torch.where(fire_place,
+ integrate - torch.ones([batch_size], device=alphas.device)*threshold,
+ integrate)
+ fires = torch.stack(list_fires, 1)
+ return fires
def ts_prediction_lfr6_standard(us_alphas,
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 []
+ return "", []
START_END_THRESHOLD = 5
MAX_TOKEN_DURATION = 12
TIME_RATE = 10.0 * 6 / 1000 / 3 # 3 times upsampled
if len(us_alphas.shape) == 2:
- _, peaks = us_alphas[0], us_peaks[0] # support inference batch_size=1 only
+ alphas, peaks = us_alphas[0], us_peaks[0] # support inference batch_size=1 only
else:
- _, peaks = us_alphas, us_peaks
- num_frames = peaks.shape[0]
+ alphas, peaks = us_alphas, us_peaks
if char_list[-1] == '</s>':
char_list = char_list[:-1]
+ fire_place = torch.where(peaks>1.0-1e-4)[0].cpu().numpy() + force_time_shift # total offset
+ if len(fire_place) != len(char_list) + 1:
+ alphas /= (alphas.sum() / (len(char_list) + 1))
+ alphas = alphas.unsqueeze(0)
+ peaks = cif_wo_hidden(alphas, threshold=1.0-1e-4)[0]
+ fire_place = torch.where(peaks>1.0-1e-4)[0].cpu().numpy() + force_time_shift # total offset
+ num_frames = peaks.shape[0]
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(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
+ # 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>')
@@ -66,6 +88,8 @@
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):
@@ -75,6 +99,7 @@
def time_stamp_sentence(punc_id_list, time_stamp_postprocessed, text_postprocessed):
+ punc_list = ['锛�', '銆�', '锛�', '銆�']
res = []
if text_postprocessed is None:
return res
@@ -84,161 +109,213 @@
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]
+ "end": time_stamp_postprocessed[-1][1],
+ 'text_seg': text_postprocessed.split(),
+ "ts_list": time_stamp_postprocessed,
})
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 = ''
+ print(" warning length mistach!!!!!!")
+ sentence_text = ""
+ sentence_text_seg = ""
+ ts_list = []
sentence_start = time_stamp_postprocessed[0][0]
+ sentence_end = time_stamp_postprocessed[0][1]
texts = text_postprocessed.split()
- for i in range(len(punc_id_list)):
- sentence_text += texts[i]
- if punc_id_list[i] == 2:
- sentence_text += ','
+ punc_stamp_text_list = list(zip_longest(punc_id_list, time_stamp_postprocessed, texts, fillvalue=None))
+ for punc_stamp_text in punc_stamp_text_list:
+ punc_id, time_stamp, 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':
+ sentence_text += ' ' + text
+ elif len(sentence_text) and ('a' <= sentence_text[-1] <= 'z' or 'A' <= sentence_text[-1] <= 'Z'):
+ sentence_text += ' ' + text
+ else:
+ sentence_text += text
+ sentence_text_seg += text + ' '
+ ts_list.append(time_stamp)
+
+ 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
+
+ if punc_id > 1:
+ sentence_text += punc_list[punc_id - 2]
res.append({
'text': sentence_text,
"start": sentence_start,
- "end": time_stamp_postprocessed[i][1]
+ "end": sentence_end,
+ "text_seg": sentence_text_seg,
+ "ts_list": ts_list
})
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]
+ sentence_text_seg = ''
+ ts_list = []
+ sentence_start = sentence_end
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))
+# 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 _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):
- 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
+def convert_external_alphas(alphas_file, text_file, output_file):
+ from funasr.models.paraformer.cif_predictor 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:
- logging.warning("length mismatch")
- return self._accumlated_shift / self._accumlated_tokens
+ 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))
-SUPPORTED_MODES = ['cal_aas']
+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)
+ # if args.mode == 'cal_aas':
+ # asc = AverageShiftCalculator()
+ # asc(args.input, args.input2)
+ if 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))
--
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