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 +1,137 @@
+#!/opt/homebrew/bin/python3.11
+"""Test transcription of a chunk from large video."""
+
+import sys
+import os
+import tempfile
+import subprocess
+import time
+
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+
+def extract_chunk(audio_path, start, duration, chunk_path):
+    """Extract a single chunk."""
+    cmd = [
+        "ffmpeg",
+        "-i",
+        audio_path,
+        "-ss",
+        str(start),
+        "-t",
+        str(duration),
+        "-acodec",
+        "pcm_s16le",
+        "-ar",
+        "16000",
+        "-ac",
+        "1",
+        "-y",
+        chunk_path,
+    ]
+    result = subprocess.run(cmd, capture_output=True, timeout=30)
+    return (
+        result.returncode == 0
+        and os.path.exists(chunk_path)
+        and os.path.getsize(chunk_path) > 0
+    )
+
+
+def main():
+    video_path = "/Users/accusys/test_video/1636719d-c31f-78ac-f1dd-8ab0b0b36c66.mov"
+    if not os.path.exists(video_path):
+        print(f"Video not found: {video_path}")
+        return
+
+    # First extract audio (or reuse existing audio.wav from previous run)
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
+        audio_path = f.name
+
+    # Extract audio
+    print("Extracting audio from video...")
+    cmd = [
+        "ffmpeg",
+        "-i",
+        video_path,
+        "-vn",
+        "-acodec",
+        "pcm_s16le",
+        "-ar",
+        "16000",
+        "-ac",
+        "1",
+        "-y",
+        audio_path,
+    ]
+    result = subprocess.run(cmd, capture_output=True, timeout=60)
+    if result.returncode != 0:
+        print(f"Audio extraction failed: {result.stderr.decode()[:200]}")
+        return
+
+    print(f"Audio extracted: {os.path.getsize(audio_path)} bytes")
+
+    # Extract first chunk (60 seconds)
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
+        chunk_path = f.name
+
+    try:
+        if not extract_chunk(audio_path, 0, 60, chunk_path):
+            print("Failed to extract chunk")
+            return
+
+        print(f"Chunk extracted: {os.path.getsize(chunk_path)} bytes")
+
+        # Load Whisper model
+        print("Loading Whisper model...")
+        try:
+            from faster_whisper import WhisperModel
+
+            model = WhisperModel("tiny", device="cpu", compute_type="int8")
+            print("Model loaded")
+        except ImportError as e:
+            print(f"Failed to import faster_whisper: {e}")
+            return
+        except Exception as e:
+            print(f"Failed to load model: {e}")
+            return
+
+        # Try transcription
+        print("Transcribing chunk...")
+        start_time = time.time()
+        try:
+            # Use beam_size=5 like in ASR processor
+            segments, info = model.transcribe(chunk_path, beam_size=5)
+            elapsed = time.time() - start_time
+            print(f"Transcription initiated in {elapsed:.2f}s")
+
+            # Convert generator to list (actual transcription happens here)
+            print("Converting segments to list...")
+            segments_list = list(segments)
+            total_elapsed = time.time() - start_time
+            print(f"Transcription completed in {total_elapsed:.2f}s")
+            print(f"Segments: {len(segments_list)}")
+            print(
+                f"Language: {info.language}, Probability: {info.language_probability}"
+            )
+
+            for i, segment in enumerate(segments_list[:5]):
+                print(
+                    f"Segment {i}: {segment.start:.2f}s - {segment.end:.2f}s: {segment.text}"
+                )
+
+        except Exception as e:
+            print(f"Transcription failed: {e}")
+            import traceback
+
+            traceback.print_exc()
+
+    finally:
+        if os.path.exists(chunk_path):
+            os.unlink(chunk_path)
+        if os.path.exists(audio_path):
+            os.unlink(audio_path)
+        print("Cleaned up temporary files")
+
+
+if __name__ == "__main__":
+    main()