Files
claudetools/projects/radio-show/audio-processor/diarize_2018.py
Mike Swanson 79abef9dc9 radio: diarization pipeline fixes, benchmark setup, test episode set
- 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>
2026-04-27 13:20:40 -07:00

162 lines
5.3 KiB
Python

"""
Re-diarize the two 2018 episodes that had stale diarization, then
patch them into the existing archive DB. Also times the run to
validate the audio-preload optimization.
"""
import sys
import os
import time
os.environ["PYTHONIOENCODING"] = "utf-8"
if hasattr(sys.stdout, "reconfigure"):
sys.stdout.reconfigure(encoding="utf-8")
if hasattr(sys.stderr, "reconfigure"):
sys.stderr.reconfigure(encoding="utf-8")
os.environ["TRANSFORMERS_OFFLINE"] = "1"
from pathlib import Path
from src.gpu import ensure_cuda_libs
ensure_cuda_libs()
from src.config import load_config
from src.diarizer import diarize, VoiceProfileStore
from src.indexer import ArchiveIndex
from src.qa_extractor import load_diarized_transcript, extract_qa_pairs, tag_qa_pairs_with_ollama
from rich.console import Console
import re, json
console = Console()
BASE = Path(__file__).parent
EPISODES_DIR = BASE / "training-data" / "episodes"
TRANSCRIPTS_DIR = BASE / "training-data" / "transcripts"
DB_PATH = BASE / "archive" / "archive.db"
config = load_config()
voice_profiles = VoiceProfileStore(
config.resolve_path(config.diarization.voice_profiles_dir)
)
targets = ["2018-s10e17", "2018-s10e21"]
episodes = [EPISODES_DIR / f"{stem}.mp3" for stem in targets]
console.print("[bold]Re-diarizing 2018 episodes (optimized audio preload)[/bold]\n")
total_audio_s = 0
total_wall_s = 0
for ep_path in episodes:
if not ep_path.exists():
console.print(f"[red]Missing: {ep_path.name}[/red]")
continue
stem = ep_path.stem
transcript_dir = TRANSCRIPTS_DIR / stem
t0 = time.monotonic()
result = diarize(ep_path, voice_profiles=voice_profiles,
host_match_threshold=0.85)
wall = time.monotonic() - t0
result.save(transcript_dir)
audio_dur = result.turns[-1].end if result.turns else 0
rtf = audio_dur / wall if wall > 0 else 0
total_audio_s += audio_dur
total_wall_s += wall
speakers = result.speakers_ranked()
console.print(
f" {stem}: {len(result.turns)} turns | "
+ ", ".join(f"{s} ({t:.0f}s)" for s, t in speakers[:3])
+ f" [{wall:.1f}s wall / {rtf:.1f}x realtime]"
)
if total_wall_s > 0:
console.print(
f"\n[bold]Speed:[/bold] {total_audio_s:.0f}s audio in {total_wall_s:.1f}s "
f"= {total_audio_s/total_wall_s:.1f}x realtime"
)
# Patch just these two episodes into the existing DB
console.print("\n[bold]Patching DB...[/bold]")
def episode_id(stem):
return re.sub(r"-hr\d$", "", stem, flags=re.IGNORECASE)
with ArchiveIndex(DB_PATH) as idx:
for ep_path in episodes:
if not ep_path.exists():
continue
stem = ep_path.stem
transcript_dir = TRANSCRIPTS_DIR / stem
transcript_path = transcript_dir / "transcript.json"
diarization_path = transcript_dir / "diarization.json"
if not transcript_path.exists():
console.print(f"[yellow]No transcript: {stem}[/yellow]")
continue
ep_id = episode_id(stem)
with open(transcript_path) as f:
td = json.load(f)
duration = td.get("duration")
date_m = re.search(r"(\d{4}-\d{2}-\d{2})", stem)
date = date_m.group(1) if date_m else None
segments = load_diarized_transcript(transcript_path, diarization_path)
# Remove old rows then re-add. FTS5 content tables are rebuilt at the end.
idx._conn.execute("DELETE FROM segments WHERE episode_id = ?", (ep_id,))
idx._conn.execute("DELETE FROM qa_pairs WHERE episode_id = ?", (ep_id,))
idx._conn.commit()
idx.add_episode(ep_id, ep_path, date=date, duration=duration)
# Bypass add_segments guard (it skips if rows already exist)
idx._conn.executemany(
"INSERT INTO segments (episode_id, seg_index, start, end, speaker, text) "
"VALUES (?, ?, ?, ?, ?, ?)",
[
(ep_id, i, s["start"], s["end"], s.get("speaker", "UNKNOWN"), s["text"])
for i, s in enumerate(segments)
]
)
idx._conn.commit()
host_segs = sum(1 for s in segments if s["speaker"] == "HOST")
other_segs = len(segments) - host_segs
console.print(f" {ep_id}: {len(segments)} segs (HOST={host_segs}, other={other_segs})")
pairs = extract_qa_pairs(segments)
console.print(f" {len(pairs)} Q&A pairs", end="")
if pairs:
console.print(f" — tagging with Ollama...", end="")
pairs = tag_qa_pairs_with_ollama(
pairs, ollama_host=config.llm.ollama_host, model=config.llm.model
)
for pair in pairs:
idx.add_qa_pair(
ep_id,
pair.question_start, pair.question_end,
pair.answer_start, pair.answer_end,
pair.question_text, pair.answer_text,
topic=pair.topic, tags=pair.topic_tags,
)
console.print()
# Rebuild FTS indexes — required after manual DELETE/re-INSERT on content tables
idx._conn.execute("INSERT INTO segments_fts(segments_fts) VALUES('rebuild')")
idx._conn.execute("INSERT INTO qa_fts(qa_fts) VALUES('rebuild')")
idx._conn.commit()
stats = idx.stats()
console.print(f"\n[bold green]Done.[/bold green] DB now: "
f"{stats['episodes']} episodes | "
f"{stats['segments']} segments | "
f"{stats['qa_pairs']} Q&A pairs")