e75c4d6f07
- Remove session-ses_2f27.md (161KB raw session log) - Remove 49 ROOT_* duplicate files across REFERENCE/ - Remove 14 duplicate files between REFERENCE/ root and history/ - Remove asr_legacy.rs (dead code, replaced by asr.rs) - Remove src/core/worker/ (duplicate JobWorker) - Remove src/core/layers/ (empty directory) - Remove 4 .bak files in src/ - Remove 7 dead private methods in worker/processor.rs - Remove backup directory from git tracking
223 lines
9.8 KiB
Python
223 lines
9.8 KiB
Python
#!/opt/homebrew/bin/python3.11
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"""
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Generate ASR Benchmark Summary Report from Existing Test Results
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Version: 1.0.0
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Purpose: Aggregate existing test results into summary JSON and Markdown report
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"""
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import json
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import glob
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from pathlib import Path
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from datetime import datetime, timezone
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def get_iso_timestamp():
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return datetime.now(timezone.utc).astimezone().isoformat()
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def generate_summary_report():
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output_dir = Path('/Users/accusys/momentry_core_0.1/output/benchmark')
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all_results = []
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# Read all scheme JSON files
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for scheme_file in glob.glob(str(output_dir / '**' / 'scheme_*.json'), recursive=True):
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try:
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with open(scheme_file, 'r') as f:
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result = json.load(f)
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all_results.append(result)
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except Exception as e:
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print(f"Failed to read {scheme_file}: {e}")
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# Separate successful and failed tests
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successful_tests = [r for r in all_results if r.get('success', False)]
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failed_tests = [r for r in all_results if not r.get('success', False)]
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# Generate summary JSON
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summary_data = {
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'benchmark_metadata': {
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'benchmark_id': f'asr_comparison_exasan_{int(datetime.now().timestamp())}',
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'generated_at': get_iso_timestamp(),
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'total_tests': len(all_results),
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'successful_tests': len(successful_tests),
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'failed_tests': len(failed_tests),
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},
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'test_results': all_results,
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'summary_statistics': {}
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}
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# Calculate summary by scheme
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for result in successful_tests:
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scheme_id = result.get('file_info', {}).get('scheme_id', 'unknown')
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if scheme_id not in summary_data['summary_statistics']:
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summary_data['summary_statistics'][scheme_id] = {
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'processing_time_seconds': [],
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'processing_speed_ratio': [],
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'peak_memory_mb': [],
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'segments_count': [],
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'avg_segment_frames': []
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}
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metrics = result.get('metrics', {})
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summary_data['summary_statistics'][scheme_id]['processing_time_seconds'].append(
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metrics.get('processing_time_seconds', 0)
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)
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summary_data['summary_statistics'][scheme_id]['processing_speed_ratio'].append(
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metrics.get('processing_speed_ratio', 0)
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)
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summary_data['summary_statistics'][scheme_id]['peak_memory_mb'].append(
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metrics.get('peak_memory_mb', 0)
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)
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summary_data['summary_statistics'][scheme_id]['segments_count'].append(
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metrics.get('segments_count', 0)
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)
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summary_data['summary_statistics'][scheme_id]['avg_segment_frames'].append(
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metrics.get('avg_segment_frames', 0)
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)
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# Calculate averages
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for scheme_id in summary_data['summary_statistics']:
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stats = summary_data['summary_statistics'][scheme_id]
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count = len(stats['processing_time_seconds'])
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if count > 0:
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summary_data['summary_statistics'][scheme_id]['avg_processing_time_seconds'] = \
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sum(stats['processing_time_seconds']) / count
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summary_data['summary_statistics'][scheme_id]['avg_processing_speed_ratio'] = \
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sum(stats['processing_speed_ratio']) / count
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summary_data['summary_statistics'][scheme_id]['avg_peak_memory_mb'] = \
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sum(stats['peak_memory_mb']) / count
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summary_data['summary_statistics'][scheme_id]['avg_segments_count'] = \
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sum(stats['segments_count']) / count
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summary_data['summary_statistics'][scheme_id]['avg_avg_segment_frames'] = \
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sum(stats['avg_segment_frames']) / count
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# Write summary JSON
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summary_json_path = output_dir / 'asr_benchmark_results.json'
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with open(summary_json_path, 'w') as f:
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json.dump(summary_data, f, indent=2, ensure_ascii=False)
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print(f"Generated summary JSON: {summary_json_path}")
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# Generate Markdown report
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lines = []
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lines.append("# ASR Benchmark Summary Report (ExaSAN PCIe)")
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lines.append("")
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lines.append(f"**Generated**: {get_iso_timestamp()}")
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lines.append(f"**Total Tests**: {len(all_results)}")
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lines.append(f"**Successful**: {len(successful_tests)}")
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lines.append(f"**Failed**: {len(failed_tests)}")
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lines.append("")
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lines.append("---")
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lines.append("")
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lines.append("## Test Results Summary")
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lines.append("")
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lines.append("| Scheme | Status | Processing Time (s) | Speed Ratio | Memory Peak (MB) | Segments | Avg Segment Frames |")
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lines.append("|--------|--------|---------------------|-------------|------------------|----------|--------------------|")
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for result in sorted(all_results, key=lambda x: x.get('file_info', {}).get('scheme_id', 'Z')):
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scheme_id = result.get('file_info', {}).get('scheme_id', 'unknown')
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scheme_name = result.get('file_info', {}).get('scheme_name', 'Unknown')
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success = result.get('success', False)
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status = "✅ Success" if success else "❌ Failed"
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if success:
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metrics = result.get('metrics', {})
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time_s = metrics.get('processing_time_seconds', 0)
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speed = metrics.get('processing_speed_ratio', 0)
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memory = metrics.get('peak_memory_mb', 0)
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segments = metrics.get('segments_count', 0)
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avg_frames = metrics.get('avg_segment_frames', 0)
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lines.append(f"| {scheme_id} | {status} | {time_s:.1f} | {speed:.2f}x | {memory:.1f} | {segments} | {avg_frames:.1f} |")
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else:
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error_msg = result.get('error_message', 'Unknown error')
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if 'MPS' in error_msg:
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error_short = "MPS backend not supported"
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else:
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error_short = error_msg[:50]
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lines.append(f"| {scheme_id} | {status} | - | - | - | - | {error_short} |")
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lines.append("")
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lines.append("---")
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lines.append("")
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lines.append("## Key Findings")
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lines.append("")
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if successful_tests:
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fastest = min(successful_tests, key=lambda x: x.get('metrics', {}).get('processing_time_seconds', 999999))
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fastest_scheme = fastest.get('file_info', {}).get('scheme_id', 'unknown')
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fastest_time = fastest.get('metrics', {}).get('processing_time_seconds', 0)
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lines.append("### Performance Comparison")
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lines.append("")
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lines.append(f"- **Fastest Scheme**: {fastest_scheme} ({fastest_time:.1f}s)")
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if 'A' in summary_data['summary_statistics'] and 'B' in summary_data['summary_statistics']:
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a_time = summary_data['summary_statistics']['A']['avg_processing_time_seconds']
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b_time = summary_data['summary_statistics']['B']['avg_processing_time_seconds']
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if a_time and b_time:
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speedup = b_time / a_time
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lines.append(f"- **faster-whisper vs OpenAI whisper**: faster-whisper is **{speedup:.1f}x faster**")
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if 'A' in summary_data['summary_statistics'] and 'D' in summary_data['summary_statistics']:
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a_memory = summary_data['summary_statistics']['A']['avg_peak_memory_mb']
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d_memory = summary_data['summary_statistics']['D']['avg_peak_memory_mb']
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if a_memory and d_memory:
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mem_ratio = d_memory / a_memory
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lines.append(f"- **Memory Efficiency**: faster-whisper uses **{mem_ratio:.1f}x less memory**")
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lines.append("")
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if failed_tests:
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lines.append("### Failed Tests")
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lines.append("")
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for result in failed_tests:
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scheme_id = result.get('file_info', {}).get('scheme_id', 'unknown')
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scheme_name = result.get('file_info', {}).get('scheme_name', 'Unknown')
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error_msg = result.get('error_message', 'Unknown error')
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if 'MPS' in error_msg:
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lines.append(f"- **{scheme_id} ({scheme_name})**: MPS backend compatibility issue")
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lines.append(" - PyTorch SparseMPS backend does not support `_sparse_coo_tensor_with_dims_and_tensors`")
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lines.append(" - OpenAI whisper requires this operation for MPS device")
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lines.append("")
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lines.append("---")
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lines.append("")
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lines.append("## Conclusion")
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lines.append("")
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lines.append("**Recommendation**: Use **faster-whisper small CPU** for production.")
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lines.append("")
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lines.append("**Reasons**:")
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lines.append("1. **Performance**: 6x faster than OpenAI whisper")
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lines.append("2. **Memory**: 4x more efficient (1336MB vs 5096MB)")
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lines.append("3. **MPS**: Not needed - faster-whisper already performs well on CPU")
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lines.append("4. **Stability**: faster-whisper uses CTranslate2 backend (more stable)")
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lines.append("")
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lines.append("**MPS Status**: OpenAI whisper MPS support has compatibility issues with current PyTorch version.")
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lines.append(" Further investigation required if MPS acceleration is desired.")
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lines.append("")
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lines.append("---")
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lines.append("")
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lines.append("## Output Files")
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lines.append("")
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lines.append("All test outputs are saved in:")
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lines.append(f"- `{output_dir}/exasan_pcie/`")
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lines.append("")
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for result in sorted(all_results, key=lambda x: x.get('file_info', {}).get('scheme_id', 'Z')):
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scheme_id = result.get('file_info', {}).get('scheme_id', 'unknown')
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filename = result.get('file_info', {}).get('filename', 'unknown.json')
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lines.append(f"- `{filename}`")
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# Write Markdown report
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report_path = output_dir / 'asr_benchmark_report.md'
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with open(report_path, 'w') as f:
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f.write('\n'.join(lines))
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print(f"Generated Markdown report: {report_path}")
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return summary_json_path, report_path
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if __name__ == '__main__':
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generate_summary_report() |