radio show: co-host voice profile, Q&A extraction fixes, archive index

- Build Tom (co-host) voice profile (44 embeddings, 0.698 similarity to Mike)
- diarizer.py: add CO-HOST speaker label for cohost-role profiles
- voice_profiler.py: emit "Cohost: <name>" label for cohost role
- qa_extractor.py: overlap resolution at load time (midpoint boundary split),
  4s CALLER-preference threshold, turn-based caller-intro lookback (2 HOST turns),
  _preceded_by_caller_intro() helper, _PHONE_GREETING pattern,
  751-1041 + "we'll get your problem solved" promo signatures
- benchmark.py: use src.transcriber.transcribe with batch_size=16
- add index_test_episodes.py and build_cohost_profile.py scripts
- add .gitignore (exclude episodes, transcripts, *.db, .venv)
- session log: 2026-04-27-qa-extraction-cohost-indexing.md

Result: 2016-s8e43 drops from 12 false-positive Q&A pairs to 2 real caller pairs.
archive.db: 6 episodes, 762 segments, 10 Q&A pairs, FTS5 search verified.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

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

@@ -0,0 +1,115 @@
+"""
+Build voice profile for Tom (co-host) from known co-host speech windows.
+
+Uses CALLER-labeled windows from the first 60 min of co-host-era episodes,
+before any real callers would have called in.
+"""
+import os, sys
+os.environ["PYTHONIOENCODING"] = "utf-8"
+os.environ["TRANSFORMERS_OFFLINE"] = "1"
+if hasattr(sys.stdout, "reconfigure"):
+    sys.stdout.reconfigure(encoding="utf-8")
+
+from pathlib import Path
+import json
+import numpy as np
+from src.gpu import ensure_cuda_libs
+ensure_cuda_libs()
+
+import torch
+from src.voice_profiler import VoiceProfiler, SpeakerProfile
+from rich.console import Console
+
+console = Console()
+
+BASE = Path(__file__).parent
+PROFILES_DIR = BASE / "voice-profiles"
+EPISODES_DIR = BASE / "test-data" / "episodes"
+TRANS_DIR = BASE / "test-data" / "transcripts"
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+console.print(f"Device: {device}")
+
+profiler = VoiceProfiler(PROFILES_DIR, device=device)
+
+# Tom's known speech windows per episode
+# CALLER turns from diarization that are in the first 60 min (before real callers)
+# Windows at 0-40s excluded (promo/jingle, not Tom's voice)
+TOM_WINDOWS = {
+    "2014-s6e19.mp3": [
+        (195, 260),
+        (320, 425),
+        (600, 650),
+        (675, 710),
+    ],
+    "2016-s8e43.mp3": [
+        (100, 115),
+        (135, 160),
+        (270, 295),
+        (575, 605),
+        (1185, 1235),
+        (1790, 1870),
+        (2020, 2055),
+    ],
+}
+
+COHOST_NAME = "Tom"
+
+if COHOST_NAME not in profiler.profiles:
+    profiler.profiles[COHOST_NAME] = SpeakerProfile(
+        name=COHOST_NAME,
+        role="cohost",
+        embeddings=[],
+        source_episodes=[],
+    )
+
+profile = profiler.profiles[COHOST_NAME]
+console.print(f"\n[bold]Building co-host profile for: {COHOST_NAME}[/bold]")
+
+for ep_name, windows in TOM_WINDOWS.items():
+    ep_path = EPISODES_DIR / ep_name
+    if not ep_path.exists():
+        console.print(f"[yellow]  Skipping {ep_name} — not found[/yellow]")
+        continue
+
+    console.print(f"\n  Loading {ep_name}...")
+    audio = profiler._load_full_audio(ep_path)
+    profiler._get_model()
+
+    SAMPLE_RATE = 16000
+    chunk_s = 10.0
+    chunk_samples = int(chunk_s * SAMPLE_RATE)
+
+    for win_start, win_end in windows:
+        for chunk_start in range(win_start, win_end - int(chunk_s), int(chunk_s)):
+            chunk_end = chunk_start + int(chunk_s)
+            s = int(chunk_start * SAMPLE_RATE)
+            e = s + chunk_samples
+            if e > len(audio):
+                break
+            try:
+                emb = profiler._embed_audio_np(audio[s:e])
+                profile.embeddings.append(emb)
+                console.print(f"    [dim]+1 embedding @ {chunk_start}s[/dim]")
+            except Exception as ex:
+                console.print(f"    [red]Failed @ {chunk_start}s: {ex}[/red]")
+
+    profile.source_episodes.append(ep_name)
+
+if not profile.embeddings:
+    console.print("[red]No embeddings collected — check episode paths[/red]")
+    sys.exit(1)
+
+profile.compute_composite()
+console.print(f"\n[green]Tom profile built: {profile.num_samples} embeddings "
+              f"from {len(profile.source_episodes)} episodes[/green]")
+
+# Verify: check cosine similarity vs Mike to ensure separation
+mike = profiler.profiles.get("Mike Swanson")
+if mike and mike.composite_embedding is not None and profile.composite_embedding is not None:
+    sim = float(np.dot(mike.composite_embedding, profile.composite_embedding) /
+                (np.linalg.norm(mike.composite_embedding) * np.linalg.norm(profile.composite_embedding) + 1e-8))
+    console.print(f"Tom vs Mike similarity: {sim:.3f} (lower is better separation)")
+
+profiler.save_profiles()
+console.print("[bold green]Profile saved.[/bold green]")