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
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Warren
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test_full_audio.py
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test_full_audio.py
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#!/usr/bin/env python3
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"""
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Test transcription of full audio file with progress monitoring.
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"""
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import sys
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import time
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import threading
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import warnings
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import psutil
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from pathlib import Path
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# Capture warnings (disabled due to urllib3 warning)
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# warnings.filterwarnings("error") # Convert warnings to exceptions
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def monitor_memory(pid, interval=1, stop_event=None):
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"""Monitor memory usage of process."""
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samples = []
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while not stop_event or not stop_event.is_set():
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try:
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proc = psutil.Process(pid)
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mem = proc.memory_info().rss / 1024 / 1024
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samples.append((time.time(), mem))
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except:
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pass
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time.sleep(interval)
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return samples
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def transcribe_full(
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audio_path, model_size="tiny", compute_type="int8", timeout_per_segment=30
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):
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"""Transcribe full audio with timeout per segment."""
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from faster_whisper import WhisperModel
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print(f"Loading model {model_size} ({compute_type})...")
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start = time.time()
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model = WhisperModel(model_size, device="cpu", compute_type=compute_type)
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print(f"Model loaded in {time.time() - start:.1f}s")
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print(f"Starting transcription of {audio_path}...")
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print(f"File size: {Path(audio_path).stat().st_size / 1024 / 1024:.1f} MB")
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segments, info = model.transcribe(audio_path, beam_size=5)
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# Start memory monitoring in background
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import threading
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stop_event = threading.Event()
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mem_samples = []
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def monitor():
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nonlocal mem_samples
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while not stop_event.is_set():
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try:
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proc = psutil.Process()
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mem = proc.memory_info().rss / 1024 / 1024
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mem_samples.append((time.time(), mem))
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except:
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pass
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time.sleep(2)
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monitor_thread = threading.Thread(target=monitor, daemon=True)
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monitor_thread.start()
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results = []
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segment_times = []
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start_time = time.time()
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last_segment_time = start_time
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try:
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for i, segment in enumerate(segments):
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segment_time = time.time()
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elapsed = segment_time - last_segment_time
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last_segment_time = segment_time
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segment_times.append(elapsed)
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results.append(
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{
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"start": segment.start,
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"end": segment.end,
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"text": segment.text.strip(),
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}
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)
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# Print progress
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if len(mem_samples) > 0:
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current_mem = mem_samples[-1][1]
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else:
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current_mem = 0
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print(
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f"[{i + 1}] {segment.start:.1f}-{segment.end:.1f} ({elapsed:.1f}s, mem: {current_mem:.1f} MB): {segment.text[:80]}..."
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)
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# Reset timeout for next segment
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# If segment takes too long, maybe something is wrong
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if elapsed > timeout_per_segment:
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print(
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f"WARNING: Segment {i + 1} took {elapsed:.1f}s > {timeout_per_segment}s timeout"
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)
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# Continue anyway
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total_time = time.time() - start_time
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print(f"Transcription completed in {total_time:.1f}s")
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print(f"Total segments: {len(results)}")
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print(
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f"Average time per segment: {total_time / len(results) if results else 0:.2f}s"
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)
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except Exception as e:
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print(f"Error during transcription: {e}")
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import traceback
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traceback.print_exc()
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finally:
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stop_event.set()
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monitor_thread.join(timeout=5)
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if mem_samples:
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peak_mem = max(m[1] for m in mem_samples)
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avg_mem = sum(m[1] for m in mem_samples) / len(mem_samples)
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print(f"Memory usage: peak {peak_mem:.1f} MB, average {avg_mem:.1f} MB")
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return results, info
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def main():
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audio_path = "/tmp/test_audio.wav"
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if not Path(audio_path).exists():
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print(f"Audio file not found: {audio_path}")
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sys.exit(1)
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print(f"Testing full audio transcription")
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print(f"Audio duration: 1:54:39 (approx)")
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# Set a total timeout of 10 minutes
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start = time.time()
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results = None
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info = None
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def run_transcribe():
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nonlocal results, info
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results, info = transcribe_full(audio_path, timeout_per_segment=60)
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thread = threading.Thread(target=run_transcribe)
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thread.start()
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thread.join(timeout=600) # 10 minutes
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if thread.is_alive():
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print("\nTIMEOUT: Transcription took longer than 10 minutes")
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# Can't interrupt, but we can exit
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sys.exit(1)
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if results is not None:
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print(f"\nSuccessfully transcribed {len(results)} segments")
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print(f"Language: {info.language} (prob {info.language_probability:.2f})")
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# Save results
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output_path = Path("test_output/full_audio_transcription.json")
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output_path.parent.mkdir(exist_ok=True)
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import json
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with open(output_path, "w") as f:
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json.dump(
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{
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"language": info.language,
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"language_probability": info.language_probability,
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"segments": results,
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},
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f,
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indent=2,
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)
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print(f"Results saved to {output_path}")
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print(f"Total execution time: {time.time() - start:.1f}s")
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if __name__ == "__main__":
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main()
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