feat: update Python processors and add utility scripts

- Update ASR, face, OCR, pose processors - Add release pre-flight check script - Add synonym generation, chunk processing scripts - Add face recognition, stamp search utilities
2026-04-30 15:07:49 +08:00
parent f4697396e4
commit 8f05a7c188
256 changed files with 60505 additions and 299 deletions
@@ -0,0 +1,161 @@
+#!/opt/homebrew/bin/python3.11
+"""
+VAD (Voice Activity Detection) - 語音活動檢測
+使用 Silero VAD 模型提取語音片段
+
+技術來源:
+- Silero VAD: https://github.com/snakers4/silero-vad
+- 模型基於深度學習，準確度 95%+
+"""
+
+import torch
+import numpy as np
+
+
+def load_vad_model():
+    """
+    載入 Silero VAD 模型
+
+    Returns:
+        model: VAD 模型
+        utils: 工具函數
+    """
+    model, utils = torch.hub.load(
+        repo_or_dir="snakers4/silero-vad",
+        model="silero_vad",
+        force_reload=False,
+        trust_repo=True,
+    )
+    return model, utils
+
+
+def extract_speech_segments(
+    audio_path, model, utils, min_speech_duration_ms=500, min_silence_duration_ms=300
+):
+    """
+    使用 VAD 提取語音片段
+
+    Args:
+        audio_path: 音頻文件路徑
+        model: VAD 模型
+        utils: 工具函數
+        min_speech_duration_ms: 最小語音持續時間（毫秒）
+        min_silence_duration_ms: 最小靜音持續時間（毫秒）
+
+    Returns:
+        speech_segments: 語音片段列表 [(start_sec, end_sec), ...]
+        audio_waveform: 音頻波形 (numpy array)
+        sample_rate: 採樣率
+    """
+    get_speech_timestamps, save_audio, read_audio, _, _ = utils
+
+    # 讀取音頻
+    wav = read_audio(audio_path, sampling_rate=16000)
+    sample_rate = 16000
+
+    # 獲取語音時間戳
+    speech_timestamps = get_speech_timestamps(
+        wav,
+        model,
+        sampling_rate=sample_rate,
+        min_speech_duration_ms=min_speech_duration_ms,
+        min_silence_duration_ms=min_silence_duration_ms,
+        return_seconds=True,
+    )
+
+    # 轉換為片段列表
+    speech_segments = [(ts["start"], ts["end"]) for ts in speech_timestamps]
+
+    return speech_segments, wav.numpy(), sample_rate
+
+
+def extract_speech_audio(audio_path, model, utils, output_dir=None):
+    """
+    提取語音片段並保存為單獨音頻文件
+
+    Args:
+        audio_path: 原始音頻路徑
+        model: VAD 模型
+        utils: 工具函數
+        output_dir: 輸出目錄（可選）
+
+    Returns:
+        speech_audios: 語音音頻列表 [numpy array, ...]
+        speech_segments: 語音片段列表
+    """
+    get_speech_timestamps, save_audio, read_audio, _, _ = utils
+
+    # 讀取音頻
+    wav = read_audio(audio_path, sampling_rate=16000)
+    sample_rate = 16000
+
+    # 獲取語音時間戳
+    speech_timestamps = get_speech_timestamps(
+        wav,
+        model,
+        sampling_rate=sample_rate,
+        min_speech_duration_ms=500,
+        min_silence_duration_ms=300,
+        return_seconds=False,  # 使用樣本索引
+    )
+
+    # 提取語音片段
+    speech_audios = []
+    speech_segments = []
+
+    for i, ts in enumerate(speech_timestamps):
+        start_sample = ts["start"]
+        end_sample = ts["end"]
+
+        # 提取音頻片段
+        speech_audio = wav[start_sample:end_sample]
+        speech_audios.append(speech_audio.numpy())
+        speech_segments.append(
+            (
+                start_sample / sample_rate,  # 轉換為秒
+                end_sample / sample_rate,
+            )
+        )
+
+        # 保存為文件（可選）
+        if output_dir:
+            import os
+
+            output_path = os.path.join(output_dir, f"speech_{i:03d}.wav")
+            save_audio(output_path, speech_audio, sample_rate)
+
+    return speech_audios, speech_segments
+
+
+if __name__ == "__main__":
+    # 測試 VAD
+    import sys
+
+    if len(sys.argv) < 2:
+        print("Usage: python3 vad.py <audio_path>")
+        sys.exit(1)
+
+    audio_path = sys.argv[1]
+
+    print("[VAD] Loading model...")
+    model, utils = load_vad_model()
+
+    print(f"[VAD] Processing: {audio_path}")
+    segments, wav, sr = extract_speech_segments(audio_path, model, utils)
+
+    print(f"\n[VAD] Results:")
+    print(f"  Sample rate: {sr} Hz")
+    print(f"  Speech segments: {len(segments)}")
+    print(f"  Total duration: {len(wav) / sr:.2f}s")
+
+    total_speech = sum(end - start for start, end in segments)
+    print(
+        f"  Total speech: {total_speech:.2f}s ({total_speech / (len(wav) / sr) * 100:.1f}%)"
+    )
+
+    print(f"\n[VAD] Segments:")
+    for i, (start, end) in enumerate(segments[:10]):
+        print(f"  {i + 1:3d}. {start:6.2f}s - {end:6.2f}s ({end - start:5.2f}s)")
+
+    if len(segments) > 10:
+        print(f"  ... and {len(segments) - 10} more segments")