radio: rename Tom -> Tara, expand speaker roster

Mike confirmed there is no co-host named "Tom" — the voice in 2014-s6e19
and 2016-s8e43 is Tara. The 5070 Ti session fabricated the Tom identity.
The voice profile itself (44 embeddings, 0.698 cosine vs Mike) is correct;
only the human label was wrong.

Rename swept:
- voice-profiles/tom/ -> voice-profiles/tara/ (git mv preserves all .npy)
- voice-profiles/profiles.json: "Tom" key -> "Tara"
- build_cohost_profile.py: TOM_WINDOWS -> TARA_WINDOWS, COHOST_NAME, comments
- 2026-04-27-qa-extraction-cohost-indexing.md: correction header + body sweep
- 2026-04-27-4090-benchmark-and-test-set.md: closure note
- .claude/memory/radio_show_no_cohost_named_tom.md: resolution + speaker roster

Diarization re-run after rename so speaker_map emits "Cohost: Tara".
Q&A counts unchanged (rename is label-only): 9 pairs across 6 test episodes.

Tara distribution from the post-rename diarization (per-episode % of audio):
  2011-03-12-hr1   140s   5.6%   likely false positive (call-in only)
  2012-03-10-hr1    30s   1.1%   likely false positive (call-in only)
  2012-06-09-hr1   340s  12.8%   suspicious — pending Mike confirm
  2014-s6e19       680s  23.3%   confirmed
  2016-s8e43      1890s  35.5%   confirmed
  2017-s9e30       610s  11.4%   plausible — pending Mike confirm

Broader speaker-roster context Mike provided this session (saved to
memory): the show has had multiple co-hosts (Tara, Randall, Rob) plus
producers/board ops (Andrew, Shannon, Ken, others) who would sometimes
go on-air. Only Tara has a profile so far. Every other speaker is
currently labeled CALLER, which means small CO-HOST attributions in
unexpected episodes (e.g. 2011/2012) may actually be a producer rather
than a false positive — Mike to spot-check.

Action item before full-archive run: build profiles for Randall, Rob,
and the named producers to avoid systematic Q&A false positives in
early-years and 2018/2019 episodes.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

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

@@ -1,5 +1,5 @@
 """
-Build voice profile for Tom (co-host) from known co-host speech windows.
+Build voice profile for Tara (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.
@@ -32,10 +32,10 @@ console.print(f"Device: {device}")
 
 profiler = VoiceProfiler(PROFILES_DIR, device=device)
 
-# Tom's known speech windows per episode
+# Tara'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 = {
+# Windows at 0-40s excluded (promo/jingle, not Tara's voice)
+TARA_WINDOWS = {
     "2014-s6e19.mp3": [
         (195, 260),
         (320, 425),
@@ -53,7 +53,7 @@ TOM_WINDOWS = {
     ],
 }
 
-COHOST_NAME = "Tom"
+COHOST_NAME = "Tara"
 
 if COHOST_NAME not in profiler.profiles:
     profiler.profiles[COHOST_NAME] = SpeakerProfile(
@@ -66,7 +66,7 @@ if COHOST_NAME not in profiler.profiles:
 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():
+for ep_name, windows in TARA_WINDOWS.items():
     ep_path = EPISODES_DIR / ep_name
     if not ep_path.exists():
         console.print(f"[yellow]  Skipping {ep_name} — not found[/yellow]")
@@ -101,7 +101,7 @@ if not profile.embeddings:
     sys.exit(1)
 
 profile.compute_composite()
-console.print(f"\n[green]Tom profile built: {profile.num_samples} embeddings "
+console.print(f"\n[green]Tara profile built: {profile.num_samples} embeddings "
               f"from {len(profile.source_episodes)} episodes[/green]")
 
 # Verify: check cosine similarity vs Mike to ensure separation
@@ -109,7 +109,7 @@ 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)")
+    console.print(f"Tara vs Mike similarity: {sim:.3f} (lower is better separation)")
 
 profiler.save_profiles()
 console.print("[bold green]Profile saved.[/bold green]")