48c3b13c37
- Re-ran identity_bind.py to restore identity_id on face_detections - Dedup cleanup had removed rows with identity_id, kept NULL rows - 70691 face_detections now have identity_id, 428 identities - Full package rebuild: 169MB sqlite, 1358MB tar.gz - identities.json: 428 identities + 5483 bindings + 5483 trace maps - TMDB matching complete: Audrey Hepburn 843 traces, Cary Grant 482
673 lines
21 KiB
Python
Executable File
673 lines
21 KiB
Python
Executable File
#!/opt/homebrew/bin/python3.11
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"""
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ASR Processor with chunked transcription for large files and resource monitoring.
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Maintains backward compatibility with existing API.
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"""
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import sys
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import json
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import os
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import argparse
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import signal
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import subprocess
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import tempfile
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import time
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import shutil
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from typing import List, Dict, Any, Optional, Tuple
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# Try to import psutil for resource monitoring
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PSUTIL_AVAILABLE = False
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psutil = None
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try:
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import psutil
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PSUTIL_AVAILABLE = True
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except ImportError:
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sys.stderr.write("WARNING: psutil not available, resource monitoring disabled\n")
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from redis_publisher import RedisPublisher # noqa: E402
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def save_checkpoint(
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checkpoint_path: str,
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segments: List[Dict[str, Any]],
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language: Optional[str],
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language_prob: Optional[float],
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processed_chunks: List[int],
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total_chunks: int,
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) -> None:
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"""Save transcription checkpoint to resume later."""
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checkpoint_data = {
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"segments": segments,
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"language": language or "",
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"language_probability": language_prob or 0.0,
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"processed_chunks": processed_chunks,
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"total_chunks": total_chunks,
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"timestamp": time.time(),
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}
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try:
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with open(checkpoint_path, "w") as f:
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json.dump(checkpoint_data, f, indent=2, default=str)
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except Exception as e:
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sys.stderr.write(f"ASR: Failed to save checkpoint: {e}\n")
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def load_checkpoint(checkpoint_path: str) -> Optional[Dict[str, Any]]:
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"""Load transcription checkpoint if exists."""
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try:
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with open(checkpoint_path, "r") as f:
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return json.load(f)
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except Exception:
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return None
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def check_health() -> Dict[str, Any]:
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"""Check health of ASR processor dependencies."""
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health = {
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"status": "healthy",
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"checks": {},
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"timestamp": time.time(),
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}
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# Check ffmpeg
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try:
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result = subprocess.run(["ffmpeg", "-version"], capture_output=True, text=True)
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health["checks"]["ffmpeg"] = {
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"available": result.returncode == 0,
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"version": result.stdout.split("\n")[0].split(" ")[2]
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if result.stdout
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else "unknown",
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}
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except Exception as e:
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health["checks"]["ffmpeg"] = {"available": False, "error": str(e)}
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# Check ffprobe
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try:
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result = subprocess.run(["ffprobe", "-version"], capture_output=True, text=True)
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health["checks"]["ffprobe"] = {
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"available": result.returncode == 0,
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"version": result.stdout.split("\n")[0].split(" ")[2]
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if result.stdout
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else "unknown",
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}
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except Exception as e:
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health["checks"]["ffprobe"] = {"available": False, "error": str(e)}
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# Check faster_whisper import
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try:
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import faster_whisper
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health["checks"]["faster_whisper"] = {
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"available": True,
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"version": getattr(faster_whisper, "__version__", "unknown"),
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}
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except ImportError as e:
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health["checks"]["faster_whisper"] = {"available": False, "error": str(e)}
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health["status"] = "unhealthy"
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# Check psutil import
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try:
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import psutil
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health["checks"]["psutil"] = {
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"available": True,
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"version": getattr(psutil, "__version__", "unknown"),
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}
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except ImportError:
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health["checks"]["psutil"] = {
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"available": False,
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"warning": "resource monitoring disabled",
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}
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# Determine overall status
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if not health["checks"].get("ffmpeg", {}).get("available", False) or not health[
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"checks"
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].get("ffprobe", {}).get("available", False):
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health["status"] = "unhealthy"
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return health
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def signal_handler(signum, frame):
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sys.stderr.write(f"ASR: Received signal {signum}, exiting...\n")
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sys.exit(1)
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def has_audio_stream(video_path: str) -> bool:
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"""Check if video file has audio stream using ffprobe."""
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try:
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cmd = [
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"ffprobe",
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"-v",
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"error",
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"-select_streams",
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"a",
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"-show_entries",
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"stream=codec_type",
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"-of",
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"csv=p=0",
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video_path,
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]
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result = subprocess.run(cmd, capture_output=True, text=True, check=True)
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return bool(result.stdout.strip())
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except subprocess.CalledProcessError:
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return False
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except FileNotFoundError:
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sys.stderr.write("WARNING: ffprobe not found, assuming audio exists\n")
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return True
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def get_media_duration(media_path: str) -> float:
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"""Get media duration in seconds using ffprobe."""
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cmd = [
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"ffprobe",
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"-v",
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"error",
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"-show_entries",
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"format=duration",
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"-of",
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"csv=p=0",
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media_path,
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]
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result = subprocess.run(cmd, capture_output=True, text=True)
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try:
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return float(result.stdout.strip())
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except (ValueError, AttributeError):
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return 0.0
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def extract_audio(video_path: str, audio_path: str) -> bool:
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"""Extract audio from video to WAV format."""
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cmd = [
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"ffmpeg",
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"-i",
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video_path,
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"-acodec",
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"pcm_s16le",
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"-ar",
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"16000",
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"-ac",
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"1",
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"-y",
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audio_path,
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]
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result = subprocess.run(cmd, capture_output=True)
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return result.returncode == 0 and os.path.exists(audio_path)
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def extract_chunk(
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audio_path: str, start: float, duration: float, output_path: str
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) -> bool:
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"""Extract a chunk of audio using ffmpeg."""
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cmd = [
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"ffmpeg",
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"-i",
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audio_path,
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"-ss",
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str(start),
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"-t",
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str(duration),
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"-acodec",
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"pcm_s16le",
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"-ar",
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"16000",
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"-ac",
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"1",
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"-y",
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output_path,
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]
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result = subprocess.run(cmd, capture_output=True)
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return (
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result.returncode == 0
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and os.path.exists(output_path)
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and os.path.getsize(output_path) > 0
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)
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def monitor_resources(pid: int, interval: float = 0.1) -> Dict[str, Any]:
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"""Monitor CPU and memory usage for a process."""
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if not PSUTIL_AVAILABLE or psutil is None:
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return {"cpu_percent": 0.0, "memory_mb": 0.0, "available": False}
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try:
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process = psutil.Process(pid)
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cpu_percent = process.cpu_percent(interval=interval)
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memory_info = process.memory_info()
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memory_mb = memory_info.rss / (1024 * 1024)
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return {
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"cpu_percent": cpu_percent,
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"memory_mb": memory_mb,
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"available": True,
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"pid": pid,
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}
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except (psutil.NoSuchProcess, psutil.AccessDenied, psutil.ZombieProcess):
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return {"cpu_percent": 0.0, "memory_mb": 0.0, "available": False}
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def transcribe_direct(
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model, audio_path: str, publisher: Optional[RedisPublisher] = None
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) -> Tuple[List[Dict[str, Any]], Any]:
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"""Transcribe audio directly (non-chunked)."""
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if publisher:
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publisher.info("asr", "Transcribing audio directly...")
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start_time = time.time()
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segments, info = model.transcribe(audio_path, beam_size=5)
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results = []
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total_segments = 0
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for segment in segments:
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results.append(
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{"start": segment.start, "end": segment.end, "text": segment.text.strip()}
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)
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total_segments += 1
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if total_segments % 100 == 0 and publisher:
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publisher.progress("asr", total_segments, 0, f"Segment {total_segments}")
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elapsed = time.time() - start_time
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if publisher:
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publisher.info(
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"asr", f"Direct transcription: {len(results)} segments in {elapsed:.1f}s"
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)
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return results, info
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def transcribe_chunk(
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model,
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chunk_path: str,
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chunk_start: float,
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chunk_idx: int,
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total_chunks: int,
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publisher: Optional[RedisPublisher] = None,
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) -> Tuple[List[Dict[str, Any]], Any]:
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"""Transcribe a single audio chunk."""
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if publisher:
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publisher.info("asr", f"Transcribing chunk {chunk_idx + 1}/{total_chunks}")
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start_time = time.time()
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segments, info = model.transcribe(chunk_path, beam_size=5)
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results = []
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for segment in segments:
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results.append(
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{
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"start": segment.start + chunk_start,
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"end": segment.end + chunk_start,
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"text": segment.text.strip(),
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}
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)
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elapsed = time.time() - start_time
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if publisher:
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publisher.info(
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"asr",
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f"Chunk {chunk_idx + 1}/{total_chunks}: {len(results)} segments in {elapsed:.1f}s",
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)
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return results, info
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def run_asr(
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video_path: str,
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output_path: str,
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uuid: str = "",
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chunk_duration: int = 600, # 10 minutes default
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max_direct_duration: int = 1200, # 20 minutes: use direct transcription for shorter files (safe limit)
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model_size: str = "tiny",
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compute_type: str = "int8",
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monitor_interval: int = 60,
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) -> None:
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# Set up signal handlers
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signal.signal(signal.SIGTERM, signal_handler)
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signal.signal(signal.SIGINT, signal_handler)
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publisher = RedisPublisher(uuid) if uuid else None
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if publisher:
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publisher.info("asr", "ASR_START")
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# Check for audio stream
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if not has_audio_stream(video_path):
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if publisher:
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publisher.info("asr", "No audio stream detected, skipping transcription")
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output = {
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"processor_name": "asr",
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"processor_version": "2.0.0",
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"contract_version": "1.0",
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"language": None,
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"language_probability": None,
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"segments": [],
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}
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with open(output_path, "w") as f:
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json.dump(output, f, indent=2)
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if publisher:
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publisher.complete("asr", "0 segments (no audio)")
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sys.stderr.write("ASR: No audio stream, skipping transcription\n")
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sys.stderr.flush()
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sys.exit(0)
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# Create temporary directory
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temp_dir = tempfile.mkdtemp(prefix="asr_")
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audio_path = os.path.join(temp_dir, "audio.wav")
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if publisher:
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publisher.info("asr", "Extracting audio from video...")
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# Extract audio
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if not extract_audio(video_path, audio_path):
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if publisher:
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publisher.error("asr", "Failed to extract audio")
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sys.stderr.write("ASR: Failed to extract audio\n")
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sys.stderr.flush()
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# Clean up
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shutil.rmtree(temp_dir, ignore_errors=True)
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sys.exit(1)
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# Get audio duration
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try:
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total_duration = get_media_duration(audio_path)
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except Exception as e:
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if publisher:
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publisher.error("asr", f"Failed to get audio duration: {e}")
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sys.stderr.write(f"ASR: Failed to get audio duration: {e}\n")
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sys.stderr.flush()
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shutil.rmtree(temp_dir, ignore_errors=True)
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sys.exit(1)
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if publisher:
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publisher.info(
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"asr",
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f"Audio duration: {total_duration:.1f}s ({total_duration / 3600:.1f} hrs)",
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)
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# Load Whisper model
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if publisher:
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publisher.info(
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"asr", f"Loading Whisper model ({model_size}, {compute_type})..."
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)
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try:
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from faster_whisper import WhisperModel
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model = WhisperModel(model_size, device="cpu", compute_type=compute_type)
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except Exception as e:
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if publisher:
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publisher.error("asr", f"Failed to load Whisper model: {e}")
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sys.stderr.write(f"ASR: Failed to load Whisper model: {e}\n")
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sys.stderr.flush()
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shutil.rmtree(temp_dir, ignore_errors=True)
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sys.exit(1)
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if publisher:
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publisher.info("asr", "Whisper model loaded successfully")
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# Decide whether to use chunked or direct transcription
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use_chunked = total_duration > max_direct_duration
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all_segments = []
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language = None
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language_prob = None
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chunks = [] # Initialize chunks variable
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if not use_chunked:
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# Direct transcription for shorter audio
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if publisher:
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publisher.info(
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"asr", f"Using direct transcription (duration ≤ {max_direct_duration}s)"
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)
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try:
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segments, info = transcribe_direct(model, audio_path, publisher)
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all_segments.extend(segments)
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language = info.language
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language_prob = info.language_probability
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except Exception as e:
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if publisher:
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publisher.error("asr", f"Direct transcription failed: {e}")
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sys.stderr.write(f"ASR: Direct transcription failed: {e}\n")
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sys.stderr.flush()
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# Fall back to chunked approach
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use_chunked = True
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if publisher:
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publisher.info("asr", "Falling back to chunked transcription")
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if use_chunked:
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# Chunked transcription for long audio
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if publisher:
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publisher.info(
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"asr", f"Using chunked transcription ({chunk_duration}s chunks)"
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)
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# Calculate chunks
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chunks = []
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start = 0.0
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chunk_idx = 0
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while start < total_duration:
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chunk_end = min(start + chunk_duration, total_duration)
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chunks.append(
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{
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"start": start,
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"end": chunk_end,
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"duration": chunk_end - start,
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"idx": chunk_idx,
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}
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)
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start = chunk_end
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chunk_idx += 1
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if publisher:
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publisher.info("asr", f"Split into {len(chunks)} chunks")
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chunk_temp_dir = os.path.join(temp_dir, "chunks")
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os.makedirs(chunk_temp_dir, exist_ok=True)
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last_resource_report = time.time()
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for i, chunk in enumerate(chunks):
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chunk_path = os.path.join(chunk_temp_dir, f"chunk_{i:04d}.wav")
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if publisher and os.environ.get("MOMENTRY_DISABLE_REDIS") != "1":
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publisher.progress(
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"asr", i, len(chunks), f"Processing chunk {i + 1}/{len(chunks)}"
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)
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# Extract chunk
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if not extract_chunk(
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audio_path, chunk["start"], chunk["duration"], chunk_path
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):
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if publisher:
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publisher.warning("asr", f"Failed to extract chunk {i}, skipping")
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continue
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# Resource monitoring (sample every monitor_interval seconds)
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current_time = time.time()
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if (
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PSUTIL_AVAILABLE
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and publisher
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and (current_time - last_resource_report) >= monitor_interval
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):
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resources = monitor_resources(os.getpid())
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if resources["available"]:
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publisher.info(
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"asr",
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f"Resource usage: CPU {resources['cpu_percent']:.1f}%, "
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f"Memory {resources['memory_mb']:.1f}MB",
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)
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last_resource_report = current_time
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# Transcribe chunk with retry logic
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max_retries = 3
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transcribed = False
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last_error = None
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for retry in range(max_retries):
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try:
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segments, info = transcribe_chunk(
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model, chunk_path, chunk["start"], i, len(chunks), publisher
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)
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all_segments.extend(segments)
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if language is None:
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language = info.language
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language_prob = info.language_probability
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if publisher:
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publisher.info(
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"asr",
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f"Detected language: {language} (prob {language_prob:.2f})",
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)
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transcribed = True
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break # Success, exit retry loop
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except Exception as e:
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last_error = e
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if publisher:
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publisher.warning(
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"asr",
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f"Error transcribing chunk {i} (attempt {retry + 1}/{max_retries}): {e}",
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)
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sys.stderr.write(
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f"ASR: Error transcribing chunk {i} (attempt {retry + 1}/{max_retries}): {e}\n"
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)
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sys.stderr.flush()
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if retry < max_retries - 1:
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# Wait before retry (exponential backoff)
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wait_time = 2**retry # 1, 2, 4 seconds
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if publisher:
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publisher.info("asr", f"Retrying in {wait_time}s...")
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|
time.sleep(wait_time)
|
|
else:
|
|
# Final attempt failed
|
|
if publisher:
|
|
publisher.error(
|
|
"asr",
|
|
f"Failed to transcribe chunk {i} after {max_retries} attempts: {last_error}",
|
|
)
|
|
sys.stderr.write(
|
|
f"ASR: Failed to transcribe chunk {i} after {max_retries} attempts: {last_error}\n"
|
|
)
|
|
sys.stderr.flush()
|
|
# Continue with next chunk (skip this one)
|
|
|
|
# Clean up chunk file
|
|
try:
|
|
os.unlink(chunk_path)
|
|
except Exception:
|
|
pass
|
|
|
|
# Clean up temporary directory
|
|
try:
|
|
shutil.rmtree(temp_dir, ignore_errors=True)
|
|
except Exception:
|
|
pass
|
|
|
|
# Sort segments by start time
|
|
all_segments.sort(key=lambda x: x["start"])
|
|
|
|
# Prepare output (maintain same format as original)
|
|
output = {
|
|
"processor_name": "asr",
|
|
"processor_version": "2.0.0",
|
|
"contract_version": "1.0",
|
|
"language": language if language is not None else None,
|
|
"language_probability": language_prob if language_prob is not None else None,
|
|
"segments": all_segments,
|
|
}
|
|
|
|
# Add metadata for chunked processing (optional)
|
|
if use_chunked:
|
|
output["processing_mode"] = "chunked"
|
|
output["chunk_count"] = len(chunks) if "chunks" in locals() else 0
|
|
output["chunk_duration"] = chunk_duration
|
|
else:
|
|
output["processing_mode"] = "direct"
|
|
|
|
# Write output
|
|
with open(output_path, "w") as f:
|
|
json.dump(output, f, indent=2)
|
|
|
|
if publisher:
|
|
publisher.complete(
|
|
"asr",
|
|
f"{len(all_segments)} segments ({'chunked' if use_chunked else 'direct'} mode)",
|
|
)
|
|
|
|
sys.stderr.write(
|
|
f"ASR: Transcription complete, {len(all_segments)} segments written to {output_path}\n"
|
|
)
|
|
sys.stderr.flush()
|
|
sys.exit(0)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
parser = argparse.ArgumentParser(
|
|
description="ASR Transcription with chunked processing"
|
|
)
|
|
parser.add_argument("video_path", nargs="?", help="Path to video file")
|
|
parser.add_argument("output_path", nargs="?", help="Output JSON path")
|
|
parser.add_argument("--uuid", "-u", help="UUID for Redis progress", default="")
|
|
parser.add_argument("--version", action="version", version="2.0.0")
|
|
parser.add_argument(
|
|
"--check-health", action="store_true", help="Check dependencies and exit"
|
|
)
|
|
|
|
# Hidden arguments for configuration (can be set via environment variables)
|
|
parser.add_argument(
|
|
"--chunk-duration", type=int, default=600, help=argparse.SUPPRESS
|
|
) # 10 minutes default
|
|
parser.add_argument(
|
|
"--max-direct-duration", type=int, default=1200, help=argparse.SUPPRESS
|
|
) # 20 minutes (safe limit based on testing)
|
|
parser.add_argument("--model-size", default="tiny", help=argparse.SUPPRESS)
|
|
parser.add_argument("--compute-type", default="int8", help=argparse.SUPPRESS)
|
|
parser.add_argument(
|
|
"--monitor-interval", type=int, default=60, help=argparse.SUPPRESS
|
|
)
|
|
|
|
args = parser.parse_args()
|
|
|
|
# Handle health check
|
|
if args.check_health:
|
|
health = check_health()
|
|
print(json.dumps(health, indent=2))
|
|
sys.exit(0 if health["status"] == "healthy" else 1)
|
|
|
|
# Validate required arguments when not doing health check
|
|
if args.video_path is None or args.output_path is None:
|
|
parser.error(
|
|
"video_path and output_path are required when not using --check-health"
|
|
)
|
|
|
|
# Allow environment variable overrides
|
|
chunk_duration_str = os.environ.get("MOMENTRY_ASR_CHUNK_DURATION")
|
|
if chunk_duration_str is not None:
|
|
chunk_duration = int(chunk_duration_str)
|
|
else:
|
|
chunk_duration = args.chunk_duration
|
|
|
|
max_direct_duration_str = os.environ.get("MOMENTRY_ASR_MAX_DIRECT_DURATION")
|
|
if max_direct_duration_str is not None:
|
|
max_direct_duration = int(max_direct_duration_str)
|
|
else:
|
|
max_direct_duration = args.max_direct_duration
|
|
|
|
model_size = os.environ.get("MOMENTRY_ASR_MODEL_SIZE")
|
|
if model_size is None:
|
|
model_size = args.model_size
|
|
|
|
compute_type = os.environ.get("MOMENTRY_ASR_COMPUTE_TYPE")
|
|
if compute_type is None:
|
|
compute_type = args.compute_type
|
|
|
|
run_asr(
|
|
args.video_path,
|
|
args.output_path,
|
|
args.uuid,
|
|
chunk_duration,
|
|
max_direct_duration,
|
|
model_size,
|
|
compute_type,
|
|
)
|