chore: import upstream snapshot with attribution

2026-07-13 13:25:10 +08:00
commit c397331b1e
3684 changed files with 990993 additions and 0 deletions
@@ -0,0 +1,66 @@
+# -*- encoding: utf-8 -*-
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+#  MIT License  (https://opensource.org/licenses/MIT)
+
+import numpy as np
+
+
+def time_stamp_lfr6_onnx(us_cif_peak, char_list, begin_time=0.0, total_offset=-1.5):
+    if not len(char_list):
+        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)
+    num_frames = cif_peak.shape[-1]
+    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
+    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>')
+        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):
+        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])
+        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
+    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])
+        new_char_list.append("<sil>")
+    else:
+        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
+            timestamp_list[i][1] = timestamp_list[i][1] + begin_time / 1000.0
+    assert len(new_char_list) == len(timestamp_list)
+    res_str = ""
+    for char, timestamp in zip(new_char_list, timestamp_list):
+        res_str += "{} {} {};".format(char, timestamp[0], timestamp[1])
+    res = []
+    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_str, res