b54c2def30
- 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
117 lines
3.4 KiB
Python
117 lines
3.4 KiB
Python
#!/usr/bin/env python3
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"""Test ASR processor on a 10-minute clip from the large problematic video."""
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import sys
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import os
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import subprocess
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import json
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import tempfile
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import time
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import shutil
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# Paths
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LARGE_VIDEO = "../test_video/1636719d-c31f-78ac-f1dd-8ab0b0b36c66.mov"
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if not os.path.exists(LARGE_VIDEO):
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print(f"Large video not found: {LARGE_VIDEO}")
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sys.exit(1)
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print(f"Large video size: {os.path.getsize(LARGE_VIDEO) / (1024**3):.2f} GB")
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# Create temporary directory
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temp_dir = tempfile.mkdtemp(prefix="asr_test_")
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clip_path = os.path.join(temp_dir, "clip.mp4")
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output_path = os.path.join(temp_dir, "output.json")
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try:
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# Extract 10-minute clip (600 seconds) starting at 0:00
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print("Extracting 10-minute clip...")
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ffmpeg_cmd = [
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"ffmpeg",
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"-i",
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LARGE_VIDEO,
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"-ss",
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"0",
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"-t",
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"600", # 10 minutes
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"-c",
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"copy",
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"-y",
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clip_path,
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]
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result = subprocess.run(ffmpeg_cmd, capture_output=True)
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if result.returncode != 0:
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print(f"FFmpeg failed: {result.stderr.decode()}")
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sys.exit(1)
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if not os.path.exists(clip_path):
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print("Clip not created")
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sys.exit(1)
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print(
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f"Clip created: {clip_path} ({os.path.getsize(clip_path) / (1024**2):.1f} MB)"
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)
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# Run ASR processor on clip with chunked mode forced (set max_direct_duration=300)
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env = os.environ.copy()
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env["MOMENTRY_ASR_MAX_DIRECT_DURATION"] = "300" # 5 minutes, force chunked
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env["MOMENTRY_ASR_CHUNK_DURATION"] = "120" # 2-minute chunks for testing
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env["MOMENTRY_ASR_MODEL_SIZE"] = "tiny"
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env["MOMENTRY_ASR_COMPUTE_TYPE"] = "int8"
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cmd = [
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"/opt/homebrew/bin/python3.11",
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"scripts/asr_processor.py",
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clip_path,
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output_path,
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"--uuid",
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"test_large",
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]
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print(f"Running ASR processor with forced chunked mode...")
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print(f"Command: {' '.join(cmd)}")
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start = time.time()
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proc = subprocess.run(
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cmd, capture_output=True, text=True, env=env, timeout=900
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) # 15 min timeout
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elapsed = time.time() - start
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print(f"ASR completed in {elapsed:.1f}s")
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print(f"Return code: {proc.returncode}")
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if proc.stdout:
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print(f"STDOUT:\n{proc.stdout}")
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if proc.stderr:
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print(f"STDERR:\n{proc.stderr}")
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# Check output
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if os.path.exists(output_path):
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with open(output_path, "r") as f:
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data = json.load(f)
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segments = data.get("segments", [])
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print(f"Output contains {len(segments)} segments")
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print(
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f"Language: {data.get('language')} (prob {data.get('language_probability')})"
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)
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print(f"Processing mode: {data.get('processing_mode', 'unknown')}")
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if segments:
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print(f"First segment: {segments[0]}")
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# Verify timestamps are correct (should be within 0-600s)
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for seg in segments[:5]:
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if seg["start"] < 0 or seg["end"] > 600:
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print(f"WARNING: segment outside clip range: {seg}")
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else:
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print("ERROR: Output file not created")
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sys.exit(1)
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except subprocess.TimeoutExpired:
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print("ERROR: ASR processing timed out after 900 seconds")
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sys.exit(1)
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except Exception as e:
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print(f"ERROR: {e}")
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import traceback
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traceback.print_exc()
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sys.exit(1)
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finally:
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# Clean up
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print(f"Cleaning up {temp_dir}")
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shutil.rmtree(temp_dir, ignore_errors=True)
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print("Done.")
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