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>

projects/radio-show/audio-processor/check_scores.py

@@ -0,0 +1,84 @@
+"""
+Quick diagnostic: print per-window WavLM similarity scores for one episode.
+Run before diarize_training.py to understand score distribution.
+"""
+import sys
+import os
+
+os.environ["PYTHONIOENCODING"] = "utf-8"
+if hasattr(sys.stdout, "reconfigure"):
+    sys.stdout.reconfigure(encoding="utf-8")
+os.environ["TRANSFORMERS_OFFLINE"] = "1"
+
+from pathlib import Path
+import numpy as np
+from src.gpu import ensure_cuda_libs
+ensure_cuda_libs()
+
+from src.voice_profiler import VoiceProfiler
+from src.config import load_config
+from rich.console import Console
+
+console = Console()
+
+BASE = Path(__file__).parent
+config = load_config()
+profiles_dir = config.resolve_path(config.diarization.voice_profiles_dir)
+
+import torch
+device = "cuda" if torch.cuda.is_available() else "cpu"
+console.print(f"Device: {device}")
+
+profiler = VoiceProfiler(profiles_dir, device=device)
+
+if not profiler.profiles:
+    console.print("[red]No voice profiles loaded[/red]")
+    sys.exit(1)
+
+# Use the first available episode
+episodes = sorted((BASE / "training-data" / "episodes").glob("*.mp3"))
+if not episodes:
+    console.print("[red]No episodes found[/red]")
+    sys.exit(1)
+
+ep = episodes[0]
+console.print(f"\nAnalyzing first 20 minutes of: {ep.name}")
+console.print("Format: [time] similarity_score  label\n")
+
+duration = profiler._get_duration(ep)
+# Scan 10-40 minutes — intro monologue usually ends before 10 min, callers appear after
+scan_start = min(600.0, duration * 0.15)   # ~10 min in or 15%
+scan_end = min(duration, 2400.0)           # up to 40 min
+
+window_s = 10.0
+hop_s = 30.0  # coarse pass — one window per 30s for speed
+
+scores = []
+for start in np.arange(scan_start, scan_end - window_s, hop_s):
+    end = start + window_s
+    try:
+        emb = profiler.extract_embedding(ep, start, end)
+        best_score = 0.0
+        best_name = ""
+        for name, profile in profiler.profiles.items():
+            s = profile.similarity(emb)
+            if s > best_score:
+                best_score = s
+                best_name = name
+
+        label = f"HOST ({best_name})" if best_score >= 0.85 else (
+            f"CALLER (below 0.85)" if best_score >= 0.70 else "UNKNOWN"
+        )
+        console.print(f"  [{start:6.0f}s-{end:.0f}s]  {best_score:.4f}  {label}")
+        scores.append(best_score)
+    except Exception as e:
+        console.print(f"  [{start:6.0f}s]  ERROR: {e}")
+
+if scores:
+    console.print(f"\nScore distribution over first 20 min:")
+    console.print(f"  min={min(scores):.4f}  max={max(scores):.4f}  mean={np.mean(scores):.4f}  median={np.median(scores):.4f}")
+    buckets = [0.0, 0.6, 0.7, 0.75, 0.80, 0.85, 0.90, 0.95, 1.01]
+    for lo, hi in zip(buckets, buckets[1:]):
+        count = sum(1 for s in scores if lo <= s < hi)
+        bar = "#" * count
+        console.print(f"  [{lo:.2f}-{hi:.2f}): {count:3d}  {bar}")