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_minimal_asr.py

@@ -0,0 +1,194 @@
+#!/usr/bin/env python3
+"""Minimal test to isolate the hang issue."""
+
+import sys
+import os
+import tempfile
+import subprocess
+import time
+
+# Test video
+test_video = "../test_video/1636719d-c31f-78ac-f1dd-8ab0b0b36c66.mov"
+if not os.path.exists(test_video):
+    print(f"Test video not found: {test_video}")
+    sys.exit(1)
+
+print(f"Testing: {test_video}")
+print(f"Size: {os.path.getsize(test_video) / (1024**3):.2f} GB")
+
+# Create temp directory
+temp_dir = tempfile.mkdtemp(prefix="asr_minimal_")
+print(f"Temp dir: {temp_dir}")
+
+# Step 1: Extract audio
+audio_path = os.path.join(temp_dir, "audio.wav")
+print(f"\n1. Extracting audio to {audio_path}...")
+
+extract_cmd = [
+    "ffmpeg",
+    "-i",
+    test_video,
+    "-acodec",
+    "pcm_s16le",
+    "-ar",
+    "16000",
+    "-ac",
+    "1",
+    "-y",
+    audio_path,
+]
+
+print(f"Command: {' '.join(extract_cmd)}")
+start = time.time()
+result = subprocess.run(extract_cmd, capture_output=True, text=True)
+elapsed = time.time() - start
+
+if result.returncode != 0:
+    print(f"Error extracting audio: {result.stderr[:500]}")
+    sys.exit(1)
+
+print(f"Audio extraction successful: {elapsed:.1f}s")
+print(f"Audio file size: {os.path.getsize(audio_path) / (1024**2):.1f} MB")
+
+# Step 2: Get audio duration
+print("\n2. Getting audio duration...")
+duration_cmd = [
+    "ffprobe",
+    "-v",
+    "error",
+    "-show_entries",
+    "format=duration",
+    "-of",
+    "default=noprint_wrappers=1:nokey=1",
+    audio_path,
+]
+
+result = subprocess.run(duration_cmd, capture_output=True, text=True)
+if result.returncode == 0:
+    duration = float(result.stdout.strip())
+    print(f"Audio duration: {duration:.1f}s ({duration / 60:.1f} min)")
+else:
+    print(f"Error getting duration: {result.stderr[:500]}")
+    duration = 0
+
+# Step 3: Extract first 60 seconds as a test chunk
+chunk_path = os.path.join(temp_dir, "chunk_0000.wav")
+print(f"\n3. Extracting first 60 seconds to {chunk_path}...")
+
+chunk_cmd = [
+    "ffmpeg",
+    "-i",
+    audio_path,
+    "-ss",
+    "0",
+    "-t",
+    "60",
+    "-acodec",
+    "pcm_s16le",
+    "-ar",
+    "16000",
+    "-ac",
+    "1",
+    "-y",
+    chunk_path,
+]
+
+print(f"Command: {' '.join(chunk_cmd)}")
+start = time.time()
+result = subprocess.run(chunk_cmd, capture_output=True, text=True)
+elapsed = time.time() - start
+
+if result.returncode != 0:
+    print(f"Error extracting chunk: {result.stderr[:500]}")
+    sys.exit(1)
+
+print(f"Chunk extraction successful: {elapsed:.1f}s")
+print(f"Chunk file size: {os.path.getsize(chunk_path) / (1024**2):.1f} MB")
+
+# Step 4: Try to load Whisper model
+print("\n4. Testing Whisper model load...")
+import sys
+
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+try:
+    from faster_whisper import WhisperModel
+
+    print("faster_whisper import successful")
+
+    start = time.time()
+    model = WhisperModel("tiny", device="cpu", compute_type="int8")
+    elapsed = time.time() - start
+    print(f"Model loaded successfully: {elapsed:.1f}s")
+except Exception as e:
+    print(f"Error loading model: {e}")
+    sys.exit(1)
+
+# Step 5: Try to transcribe the chunk
+print("\n5. Transcribing chunk...")
+try:
+    start = time.time()
+    segments, info = model.transcribe(chunk_path, beam_size=5)
+    elapsed = time.time() - start
+
+    # Convert to list to force evaluation
+    segments = list(segments)
+    print(f"Transcription successful: {elapsed:.1f}s")
+    print(f"Detected language: {info.language} (prob {info.language_probability:.2f})")
+    print(f"Number of segments: {len(segments)}")
+
+    for i, segment in enumerate(segments[:3]):  # Show first 3 segments
+        print(
+            f"  Segment {i}: {segment.start:.1f}s - {segment.end:.1f}s: {segment.text[:50]}..."
+        )
+
+    if len(segments) > 3:
+        print(f"  ... and {len(segments) - 3} more segments")
+
+except Exception as e:
+    print(f"Error transcribing: {e}")
+    import traceback
+
+    traceback.print_exc()
+
+# Step 6: Try to transcribe the full audio (should hang for large files)
+print("\n6. Testing full audio transcription (should hang for large files)...")
+try:
+    start = time.time()
+    # Set a timeout
+    import threading
+
+    class TranscriptionResult:
+        def __init__(self):
+            self.segments = []
+            self.info = None
+            self.error = None
+
+    result = TranscriptionResult()
+
+    def transcribe_with_timeout():
+        try:
+            segs, inf = model.transcribe(audio_path, beam_size=5)
+            result.segments = list(segs)
+            result.info = inf
+        except Exception as e:
+            result.error = e
+
+    thread = threading.Thread(target=transcribe_with_timeout)
+    thread.daemon = True
+    thread.start()
+    thread.join(timeout=30)  # 30 second timeout
+
+    if thread.is_alive():
+        print("Full transcription timed out after 30 seconds (expected for large file)")
+    elif result.error:
+        print(f"Transcription error: {result.error}")
+    else:
+        elapsed = time.time() - start
+        print(f"Full transcription successful (unexpected!): {elapsed:.1f}s")
+        print(f"Segments: {len(result.segments)}")
+
+except Exception as e:
+    print(f"Error in full transcription test: {e}")
+
+print(f"\nTemp directory preserved: {temp_dir}")