feat: add migrations, test scripts, and utility tools

- Add database migrations (006-028) for face recognition, identity, file_uuid
- Add test scripts for ASR, face, search, processing
- Add portal frontend (Tauri)
- Add config, benchmark, and monitoring utilities
- Add model checkpoints and pretrained model references

test_faster_whisper_debug.py

@@ -0,0 +1,155 @@
+#!/usr/bin/env python3
+"""
+Debug faster_whisper hanging issue.
+"""
+
+import sys
+import time
+import threading
+import subprocess
+from pathlib import Path
+
+
+def run_with_timeout(func, args=(), timeout=60):
+    """Run function with timeout."""
+    result = {"success": False, "error": None, "output": None}
+
+    def wrapper():
+        try:
+            result["output"] = func(*args)
+            result["success"] = True
+        except Exception as e:
+            result["error"] = str(e)
+            import traceback
+
+            result["traceback"] = traceback.format_exc()
+
+    thread = threading.Thread(target=wrapper)
+    thread.start()
+    thread.join(timeout)
+    if thread.is_alive():
+        result["error"] = f"Timeout after {timeout}s"
+        # Can't interrupt thread, but we'll return
+    return result
+
+
+def test_transcribe_audio(
+    audio_path, model_size="tiny", compute_type="float32", max_duration=None
+):
+    """Test transcription with given parameters."""
+    from faster_whisper import WhisperModel
+
+    print(f"Loading model {model_size} with compute_type={compute_type}...")
+    start = time.time()
+    model = WhisperModel(model_size, device="cpu", compute_type=compute_type)
+    print(f"Model loaded in {time.time() - start:.1f}s")
+
+    # Optional: trim audio if max_duration specified
+    if max_duration:
+        import tempfile
+
+        tmp_path = Path(audio_path).parent / f"trim_{max_duration}s.wav"
+        cmd = [
+            "ffmpeg",
+            "-i",
+            str(audio_path),
+            "-t",
+            str(max_duration),
+            "-acodec",
+            "pcm_s16le",
+            "-ar",
+            "16000",
+            "-ac",
+            "1",
+            "-y",
+            str(tmp_path),
+        ]
+        subprocess.run(cmd, capture_output=True)
+        audio_path = tmp_path
+        print(f"Trimmed audio to {max_duration}s: {tmp_path}")
+
+    print(f"Starting transcription...")
+    start = time.time()
+
+    # Try to get segments iterator
+    segments, info = model.transcribe(str(audio_path), beam_size=5)
+
+    # Try to get first segment with timeout
+    print("Getting first segment...")
+    segment_count = 0
+    try:
+        for segment in segments:
+            segment_count += 1
+            print(
+                f"Segment {segment_count}: {segment.start:.1f}-{segment.end:.1f}: {segment.text[:80]}"
+            )
+            if segment_count >= 3:
+                break
+            # Break early for test
+            if max_duration and segment.end > max_duration:
+                break
+    except Exception as e:
+        print(f"Error during iteration: {e}")
+        import traceback
+
+        traceback.print_exc()
+        return False
+
+    print(f"Transcription iteration done. Total segments: {segment_count}")
+    print(f"Detected language: {info.language} (prob {info.language_probability:.2f})")
+    print(f"Time elapsed: {time.time() - start:.1f}s")
+
+    if max_duration:
+        tmp_path.unlink()
+
+    return True
+
+
+def main():
+    audio_path = "/tmp/test_audio.wav"
+    if not Path(audio_path).exists():
+        print(f"Audio file not found: {audio_path}")
+        # Extract from large video
+        video_path = "../test_video/1636719d-c31f-78ac-f1dd-8ab0b0b36c66.mov"
+        print(f"Extracting audio from {video_path}...")
+        cmd = [
+            "ffmpeg",
+            "-i",
+            video_path,
+            "-vn",
+            "-acodec",
+            "pcm_s16le",
+            "-ar",
+            "16000",
+            "-ac",
+            "1",
+            "-y",
+            audio_path,
+        ]
+        subprocess.run(cmd, capture_output=True)
+        print(f"Audio extracted to {audio_path}")
+
+    # Test 1: tiny model, float32, first 30 seconds
+    print("\n=== Test 1: tiny, float32, 30 seconds ===")
+    result = run_with_timeout(
+        test_transcribe_audio, (audio_path, "tiny", "float32", 30), timeout=120
+    )
+    print(f"Result: {result}")
+
+    # Test 2: tiny, int8, first 30 seconds
+    print("\n=== Test 2: tiny, int8, 30 seconds ===")
+    result = run_with_timeout(
+        test_transcribe_audio, (audio_path, "tiny", "int8", 30), timeout=120
+    )
+    print(f"Result: {result}")
+
+    # Test 3: small model, float32, first 30 seconds
+    print("\n=== Test 3: small, float32, 30 seconds ===")
+    result = run_with_timeout(
+        test_transcribe_audio, (audio_path, "small", "float32", 30), timeout=180
+    )
+    print(f"Result: {result}")
+
+
+if __name__ == "__main__":
+    main()