From 1596f6f414f6f41da66506debb1dff19fffeb3ec Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 24 六月 2024 11:55:17 +0800
Subject: [PATCH] fixbug hotwords

---
 funasr/utils/vad_utils.py |   65 +++++++++++++++++++++++++-------
 1 files changed, 51 insertions(+), 14 deletions(-)

diff --git a/funasr/utils/vad_utils.py b/funasr/utils/vad_utils.py
index 9135513..eba48a9 100644
--- a/funasr/utils/vad_utils.py
+++ b/funasr/utils/vad_utils.py
@@ -1,18 +1,55 @@
 import torch
 from torch.nn.utils.rnn import pad_sequence
 
+
 def slice_padding_fbank(speech, speech_lengths, vad_segments):
-	speech_list = []
-	speech_lengths_list = []
-	for i, segment in enumerate(vad_segments):
-		
-		bed_idx = int(segment[0][0]*16)
-		end_idx = min(int(segment[0][1]*16), speech_lengths[0])
-		speech_i = speech[0, bed_idx: end_idx]
-		speech_lengths_i = end_idx-bed_idx
-		speech_list.append(speech_i)
-		speech_lengths_list.append(speech_lengths_i)
-	feats_pad = pad_sequence(speech_list, batch_first=True, padding_value=0.0)
-	speech_lengths_pad = torch.Tensor(speech_lengths_list).int()
-	return feats_pad, speech_lengths_pad
-	
+    speech_list = []
+    speech_lengths_list = []
+    for i, segment in enumerate(vad_segments):
+
+        bed_idx = int(segment[0][0] * 16)
+        end_idx = min(int(segment[0][1] * 16), speech_lengths[0])
+        speech_i = speech[0, bed_idx:end_idx]
+        speech_lengths_i = end_idx - bed_idx
+        speech_list.append(speech_i)
+        speech_lengths_list.append(speech_lengths_i)
+    feats_pad = pad_sequence(speech_list, batch_first=True, padding_value=0.0)
+    speech_lengths_pad = torch.Tensor(speech_lengths_list).int()
+    return feats_pad, speech_lengths_pad
+
+
+def slice_padding_audio_samples(speech, speech_lengths, vad_segments):
+    speech_list = []
+    speech_lengths_list = []
+    for i, segment in enumerate(vad_segments):
+        bed_idx = int(segment[0][0] * 16)
+        end_idx = min(int(segment[0][1] * 16), speech_lengths)
+        speech_i = speech[bed_idx:end_idx]
+        speech_lengths_i = end_idx - bed_idx
+        speech_list.append(speech_i)
+        speech_lengths_list.append(speech_lengths_i)
+
+    return speech_list, speech_lengths_list
+
+
+def merge_vad(vad_result, max_length=15000):
+    new_result = []
+    time_step = [t[0] for t in vad_result] + [t[1] for t in vad_result]
+    time_step = sorted(list(set(time_step)))
+    if len(time_step) == 0:
+        return []
+    bg = 0
+    for i in range(len(time_step) - 1):
+        time = time_step[i]
+        if time_step[i + 1] - bg < max_length:
+            continue
+        if time - bg < max_length * 1.5:
+            new_result.append([bg, time])
+        else:
+            split_num = int(time - bg) // max_length + 1
+            spl_l = int(time - bg) // split_num
+            for j in range(split_num):
+                new_result.append([bg + j * spl_l, bg + (j + 1) * spl_l])
+        bg = time
+    new_result.append([bg, time_step[-1]])
+    return new_result

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