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
2026-04-30 15:11:53 +08:00
parent 4d75b2e251
commit b54c2def30
192 changed files with 46721 additions and 0 deletions
@@ -0,0 +1,150 @@
+#!/usr/bin/env python3
+"""
+Debug ASR processing stages for large video.
+"""
+
+import os
+import sys
+import time
+import subprocess
+import tempfile
+import json
+from pathlib import Path
+
+
+def run_ffmpeg_extract(video_path, audio_path):
+    """Extract audio using ffmpeg."""
+    cmd = [
+        "ffmpeg",
+        "-i",
+        str(video_path),
+        "-vn",
+        "-acodec",
+        "pcm_s16le",
+        "-ar",
+        "16000",
+        "-ac",
+        "1",
+        "-y",
+        str(audio_path),
+    ]
+    print(f"Running ffmpeg: {' '.join(cmd)}")
+    start = time.time()
+    proc = subprocess.run(cmd, capture_output=True, text=True)
+    elapsed = time.time() - start
+    print(f"ffmpeg completed in {elapsed:.1f}s, return code: {proc.returncode}")
+    if proc.returncode != 0:
+        print(f"stderr: {proc.stderr[:500]}")
+    return proc.returncode == 0, elapsed
+
+
+def test_asr_stages(video_path):
+    """Test ASR stages step by step."""
+    video_path = Path(video_path)
+    print(f"Testing video: {video_path}")
+    print(f"Size: {video_path.stat().st_size / 1024 / 1024:.1f} MB")
+
+    # Stage 1: Check audio streams
+    print("\n=== Stage 1: Check audio streams ===")
+    cmd = [
+        "ffprobe",
+        "-v",
+        "error",
+        "-select_streams",
+        "a",
+        "-show_entries",
+        "stream=codec_name,channels,sample_rate,duration",
+        "-of",
+        "csv=p=0",
+        str(video_path),
+    ]
+    proc = subprocess.run(cmd, capture_output=True, text=True)
+    print(f"Audio streams: {proc.stdout.strip()}")
+
+    # Stage 2: Extract audio
+    print("\n=== Stage 2: Extract audio ===")
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
+        audio_path = f.name
+    try:
+        success, extract_time = run_ffmpeg_extract(video_path, audio_path)
+        if success:
+            print(f"Audio extracted to {audio_path}")
+            print(f"Audio size: {Path(audio_path).stat().st_size / 1024 / 1024:.1f} MB")
+        else:
+            print("Audio extraction failed")
+            os.unlink(audio_path)
+            return
+    except Exception as e:
+        print(f"Error extracting audio: {e}")
+        return
+
+    # Stage 3: Load faster_whisper model (just import)
+    print("\n=== Stage 3: Test faster_whisper import ===")
+    try:
+        start = time.time()
+        from faster_whisper import WhisperModel
+
+        elapsed = time.time() - start
+        print(f"Import faster_whisper: {elapsed:.1f}s")
+    except Exception as e:
+        print(f"Import failed: {e}")
+        os.unlink(audio_path)
+        return
+
+    # Stage 4: Transcribe a small segment (first 30 seconds)
+    print("\n=== Stage 4: Transcribe first 30 seconds ===")
+    try:
+        # Trim audio to first 30 seconds
+        trim_path = audio_path + ".trim.wav"
+        cmd = [
+            "ffmpeg",
+            "-i",
+            audio_path,
+            "-t",
+            "30",
+            "-acodec",
+            "pcm_s16le",
+            "-ar",
+            "16000",
+            "-ac",
+            "1",
+            "-y",
+            trim_path,
+        ]
+        subprocess.run(cmd, capture_output=True)
+
+        # Load model with small model
+        start = time.time()
+        model = WhisperModel("tiny", device="cpu", compute_type="int8")
+        load_time = time.time() - start
+        print(f"Model loaded in {load_time:.1f}s")
+
+        # Transcribe
+        start = time.time()
+        segments, info = model.transcribe(trim_path, beam_size=5)
+        segments = list(segments)  # Force processing
+        transcribe_time = time.time() - start
+        print(f"Transcription of 30s audio: {transcribe_time:.1f}s")
+        print(
+            f"Detected language: {info.language} with probability {info.language_probability}"
+        )
+        print(f"Segments found: {len(segments)}")
+
+        # Cleanup
+        os.unlink(trim_path)
+    except Exception as e:
+        print(f"Transcription test failed: {e}")
+        import traceback
+
+        traceback.print_exc()
+    finally:
+        os.unlink(audio_path)
+
+    print("\n=== Debug complete ===")
+
+
+if __name__ == "__main__":
+    if len(sys.argv) != 2:
+        print(f"Usage: {sys.argv[0]} <video_file>")
+        sys.exit(1)
+    test_asr_stages(sys.argv[1])