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 | 165 ++++++++++++++++++++++++++++++++++++------------------
1 files changed, 109 insertions(+), 56 deletions(-)
diff --git a/funasr/utils/timestamp_tools.py b/funasr/utils/timestamp_tools.py
index f6a6e98..63f179a 100644
--- a/funasr/utils/timestamp_tools.py
+++ b/funasr/utils/timestamp_tools.py
@@ -1,107 +1,160 @@
import torch
-import copy
+import codecs
import logging
+import argparse
import numpy as np
-from typing import Any, List, Tuple, Union
+# import edit_distance
+from itertools import zip_longest
-def time_stamp_lfr6_pl(us_alphas, us_cif_peak, char_list, begin_time=0.0, end_time=None):
+
+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,
+ 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) == 3:
- alphas, cif_peak = us_alphas[0], us_cif_peak[0] # support inference batch_size=1 only
+ if len(us_alphas.shape) == 2:
+ alphas, 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]
+ alphas, peaks = us_alphas, us_peaks
if char_list[-1] == '</s>':
char_list = char_list[:-1]
- # char_list = [i for i in text]
+ 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(cif_peak>1.0-1e-4)[0].cpu().numpy() - 1.5
- num_peak = len(fire_place)
- assert num_peak == len(char_list) + 1 # number of peaks is supposed to be number of tokens + 1
+ fire_place = torch.where(peaks>1.0-1e-4)[0].cpu().numpy() + force_time_shift # total offset
+ # 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):
+
+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
+
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]
+ "start": timestamp_postprocessed[0][0],
+ "end": timestamp_postprocessed[-1][1],
+ "timestamp": timestamp_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 = ''
- sentence_start = time_stamp_postprocessed[0][0]
+ 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 = timestamp_postprocessed[0][0]
+ sentence_end = timestamp_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, timestamp_postprocessed, texts, fillvalue=None))
+ for punc_stamp_text in punc_stamp_text_list:
+ 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':
+ 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(timestamp)
+
+ punc_id = int(punc_id) if punc_id is not None else 1
+ sentence_end = timestamp[1] if timestamp 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,
+ "timestamp": 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
--
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