From 28ccfbfc51068a663a80764e14074df5edf2b5ba Mon Sep 17 00:00:00 2001
From: kongdeqiang <kongdeqiang960204@163.com>
Date: 星期五, 13 三月 2026 17:41:41 +0800
Subject: [PATCH] 提交
---
runtime/python/libtorch/funasr_torch/utils/timestamp_utils.py | 39 +++++++++++++++++++++------------------
1 files changed, 21 insertions(+), 18 deletions(-)
diff --git a/runtime/python/libtorch/funasr_torch/utils/timestamp_utils.py b/runtime/python/libtorch/funasr_torch/utils/timestamp_utils.py
index 7d0060c..a10d193 100644
--- a/runtime/python/libtorch/funasr_torch/utils/timestamp_utils.py
+++ b/runtime/python/libtorch/funasr_torch/utils/timestamp_utils.py
@@ -3,46 +3,50 @@
def time_stamp_lfr6_onnx(us_cif_peak, char_list, begin_time=0.0, total_offset=-1.5):
if not len(char_list):
- return '', []
+ return "", []
START_END_THRESHOLD = 5
MAX_TOKEN_DURATION = 30
TIME_RATE = 10.0 * 6 / 1000 / 3 # 3 times upsampled
- cif_peak = us_cif_peak.reshape(-1)
+ cif_peak = us_cif_peak.reshape(-1).cpu()
num_frames = cif_peak.shape[-1]
- if char_list[-1] == '</s>':
+ 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 = np.where(cif_peak>1.0-1e-4)[0] + total_offset # np format
+ fire_place = np.where(cif_peak > 1.0 - 1e-4)[0] + total_offset # np format
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>')
- timestamp_list.append([0.0, fire_place[0]*TIME_RATE])
- new_char_list.append('<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):
+ for i in range(len(fire_place) - 1):
new_char_list.append(char_list[i])
- if i == len(fire_place)-2 or 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])
+ if (
+ i == len(fire_place) - 2
+ or 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>')
+ 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
if num_frames - fire_place[-1] > START_END_THRESHOLD:
_end = (num_frames + fire_place[-1]) / 2
- timestamp_list[-1][1] = _end*TIME_RATE
- timestamp_list.append([_end*TIME_RATE, num_frames*TIME_RATE])
+ timestamp_list[-1][1] = _end * TIME_RATE
+ timestamp_list.append([_end * TIME_RATE, num_frames * TIME_RATE])
new_char_list.append("<sil>")
else:
- timestamp_list[-1][1] = num_frames*TIME_RATE
+ timestamp_list[-1][1] = num_frames * TIME_RATE
if begin_time: # 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
@@ -53,7 +57,6 @@
res_str += "{} {} {};".format(char, timestamp[0], timestamp[1])
res = []
for char, timestamp in zip(new_char_list, timestamp_list):
- if char != '<sil>':
+ if char != "<sil>":
res.append([int(timestamp[0] * 1000), int(timestamp[1] * 1000)])
return res_str, res
-
\ No newline at end of file
--
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