- Fix voice_profiler threshold bug (HOST label overwrote Unknown unconditionally) - Audio preload optimization: single ffmpeg per episode, 149.5x realtime on 5070 Ti - WavLM threshold raised to 0.85 (Mike 0.90-0.99, callers 0.46-0.83) - Promo/bumper filter: weighted signature scoring, 42->27 clean Q&A pairs - Text-only Q&A fallback for episodes with no CALLER diarization labels - TRANSFORMERS_OFFLINE=1 to skip HuggingFace freshness checks - Add diarize_2018.py for targeted re-run + FTS5 rebuild - Add benchmark.py + BENCH_SETUP.md for GURU-BEAST-ROG (RTX 4090) comparison - Commit 9-episode training diarization.json outputs - Session log: 2026-04-27-diarization-pipeline.md Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
162 lines
5.3 KiB
Python
162 lines
5.3 KiB
Python
"""
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Re-diarize the two 2018 episodes that had stale diarization, then
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patch them into the existing archive DB. Also times the run to
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validate the audio-preload optimization.
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"""
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import sys
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import os
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import time
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os.environ["PYTHONIOENCODING"] = "utf-8"
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if hasattr(sys.stdout, "reconfigure"):
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sys.stdout.reconfigure(encoding="utf-8")
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if hasattr(sys.stderr, "reconfigure"):
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sys.stderr.reconfigure(encoding="utf-8")
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os.environ["TRANSFORMERS_OFFLINE"] = "1"
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from pathlib import Path
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from src.gpu import ensure_cuda_libs
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ensure_cuda_libs()
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from src.config import load_config
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from src.diarizer import diarize, VoiceProfileStore
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from src.indexer import ArchiveIndex
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from src.qa_extractor import load_diarized_transcript, extract_qa_pairs, tag_qa_pairs_with_ollama
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from rich.console import Console
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import re, json
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console = Console()
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BASE = Path(__file__).parent
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EPISODES_DIR = BASE / "training-data" / "episodes"
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TRANSCRIPTS_DIR = BASE / "training-data" / "transcripts"
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DB_PATH = BASE / "archive" / "archive.db"
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config = load_config()
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voice_profiles = VoiceProfileStore(
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config.resolve_path(config.diarization.voice_profiles_dir)
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)
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targets = ["2018-s10e17", "2018-s10e21"]
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episodes = [EPISODES_DIR / f"{stem}.mp3" for stem in targets]
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console.print("[bold]Re-diarizing 2018 episodes (optimized audio preload)[/bold]\n")
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total_audio_s = 0
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total_wall_s = 0
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for ep_path in episodes:
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if not ep_path.exists():
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console.print(f"[red]Missing: {ep_path.name}[/red]")
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continue
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stem = ep_path.stem
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transcript_dir = TRANSCRIPTS_DIR / stem
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t0 = time.monotonic()
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result = diarize(ep_path, voice_profiles=voice_profiles,
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host_match_threshold=0.85)
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wall = time.monotonic() - t0
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result.save(transcript_dir)
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audio_dur = result.turns[-1].end if result.turns else 0
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rtf = audio_dur / wall if wall > 0 else 0
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total_audio_s += audio_dur
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total_wall_s += wall
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speakers = result.speakers_ranked()
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console.print(
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f" {stem}: {len(result.turns)} turns | "
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+ ", ".join(f"{s} ({t:.0f}s)" for s, t in speakers[:3])
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+ f" [{wall:.1f}s wall / {rtf:.1f}x realtime]"
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)
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if total_wall_s > 0:
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console.print(
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f"\n[bold]Speed:[/bold] {total_audio_s:.0f}s audio in {total_wall_s:.1f}s "
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f"= {total_audio_s/total_wall_s:.1f}x realtime"
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)
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# Patch just these two episodes into the existing DB
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console.print("\n[bold]Patching DB...[/bold]")
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def episode_id(stem):
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return re.sub(r"-hr\d$", "", stem, flags=re.IGNORECASE)
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with ArchiveIndex(DB_PATH) as idx:
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for ep_path in episodes:
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if not ep_path.exists():
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continue
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stem = ep_path.stem
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transcript_dir = TRANSCRIPTS_DIR / stem
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transcript_path = transcript_dir / "transcript.json"
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diarization_path = transcript_dir / "diarization.json"
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if not transcript_path.exists():
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console.print(f"[yellow]No transcript: {stem}[/yellow]")
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continue
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ep_id = episode_id(stem)
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with open(transcript_path) as f:
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td = json.load(f)
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duration = td.get("duration")
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date_m = re.search(r"(\d{4}-\d{2}-\d{2})", stem)
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date = date_m.group(1) if date_m else None
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segments = load_diarized_transcript(transcript_path, diarization_path)
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# Remove old rows then re-add. FTS5 content tables are rebuilt at the end.
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idx._conn.execute("DELETE FROM segments WHERE episode_id = ?", (ep_id,))
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idx._conn.execute("DELETE FROM qa_pairs WHERE episode_id = ?", (ep_id,))
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idx._conn.commit()
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idx.add_episode(ep_id, ep_path, date=date, duration=duration)
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# Bypass add_segments guard (it skips if rows already exist)
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idx._conn.executemany(
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"INSERT INTO segments (episode_id, seg_index, start, end, speaker, text) "
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"VALUES (?, ?, ?, ?, ?, ?)",
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[
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(ep_id, i, s["start"], s["end"], s.get("speaker", "UNKNOWN"), s["text"])
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for i, s in enumerate(segments)
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]
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)
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idx._conn.commit()
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host_segs = sum(1 for s in segments if s["speaker"] == "HOST")
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other_segs = len(segments) - host_segs
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console.print(f" {ep_id}: {len(segments)} segs (HOST={host_segs}, other={other_segs})")
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pairs = extract_qa_pairs(segments)
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console.print(f" {len(pairs)} Q&A pairs", end="")
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if pairs:
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console.print(f" — tagging with Ollama...", end="")
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pairs = tag_qa_pairs_with_ollama(
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pairs, ollama_host=config.llm.ollama_host, model=config.llm.model
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)
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for pair in pairs:
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idx.add_qa_pair(
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ep_id,
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pair.question_start, pair.question_end,
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pair.answer_start, pair.answer_end,
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pair.question_text, pair.answer_text,
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topic=pair.topic, tags=pair.topic_tags,
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)
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console.print()
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# Rebuild FTS indexes — required after manual DELETE/re-INSERT on content tables
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idx._conn.execute("INSERT INTO segments_fts(segments_fts) VALUES('rebuild')")
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idx._conn.execute("INSERT INTO qa_fts(qa_fts) VALUES('rebuild')")
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idx._conn.commit()
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stats = idx.stats()
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console.print(f"\n[bold green]Done.[/bold green] DB now: "
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f"{stats['episodes']} episodes | "
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f"{stats['segments']} segments | "
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f"{stats['qa_pairs']} Q&A pairs")
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