QAPair gets caller_name and caller_role fields populated by a new
attach_caller_names(pairs, transcript_segments) helper. For each pair,
finds the active opening intro at the question_start time (8s forward
tolerance, no backward limit — a caller's call can run for 10+ minutes
and the intro happens once at the start) and attaches the speaker name.
Validation on 9-episode test set:
19/19 Q&A pairs (100%) now have caller names attached.
Examples of corrections from oracle attribution:
2018-s10e18 @ 73:36 Christopher (was misattributed to "Tara")
2015-s7e19 @ 35:45 William (was misattributed to "Tara")
2010-05-08-hr1 Jackie x3, Bruce
2012-03-10-hr1 Adam x2
2016-s8e43 John, Doug
2017-s9e30 Tom, Denise x3, Charlie
speaker_oracle.py: adds speaker_at(time, intros) helper used both by the
existing resolve_speakers() and the new caller-name attachment. Also
adds the "let's fit/bring/put X in/on" intro pattern variant (caught
Charlie at 70:21 in 2017-s9e30 that "talk to X" missed).
download_full_archive.py: SSH keepalive every 30s + per-file retry-on-
failure (up to 3 attempts with reconnect). Earlier run hung on a dead
connection at file 109 of 589 with no recovery; restarted run is now
running at ~10 MB/s vs ~2-3 MB/s before.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
New module src/speaker_oracle.py extracts speaker introductions from
transcripts ("let's talk to William", "we have Clay from the Nerd Junkies",
"in Tara's place, we have Clay", "thanks for the call <name>") and binds
them to non-HOST diarization turns. Pure post-pass on diarization JSONs,
no audio processing — corrects audio-only cosine errors using Mike's
deterministic on-air announcements.
Algorithm:
- Extract intros: regex patterns for caller pickups, guest intros,
fill-in announcements, caller closes. Case-strict (rejects mid-sentence
lowercase matches), with a blacklist of common false-positive words.
Deduplicates same-name intros within 5s.
- Resolve speakers: for each non-HOST turn, find the LATEST opening intro
at or before turn.start (with 8s forward tolerance for boundary slop).
Later intros implicitly close earlier callers, so the most recent
intro wins. No artificial lookback limit (callers can talk for 10+ min).
- Falls back to caller_close patterns within 30s after a turn ends.
Validation on 9-episode test set:
2018-s10e18: Christopher 190s correctly named (was mislabeled "Tara")
2012-06-09 : Kay 160s correctly named (was mislabeled "Tara")
2015-s7e19 : Clay 45s as fillin for Tara, William 40s as caller
2016-s8e43 : Charles 630s, Bruce 210s, John 205s — most callers named
2017-s9e30 : Denise 295s, Tom 115s, Elaine 85s, Jeff 10s
Many other callers across all episodes correctly named.
Remaining unnamed CO-HOST/CALLER (~5-10% of non-HOST time) are real
co-host banter or callers without explicit Mike-introductions.
benchmark.py: adds Phase 2.5 "Name Resolution" between diarization and
Q&A extraction. Prints named-speaker breakdown per episode. Doesn't
modify diarization JSONs (resolution is computed on demand).
Next step: feed named turns into qa_extractor so Q&A pairs get caller
name attached for searchability. Also: bootstrap recurring-speaker
profiles (Tara, Tony, Rob, Randall, producers) by accumulating
intro-tagged windows across the full archive once download completes.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adds a transcript-driven bumper filter to the diarization pipeline. When
a transcript segment matches qa_extractor's promo/bumper signatures, the
overlapping audio windows are labeled BUMPER and the WavLM cosine match
is skipped. Prevents music/promo from being matched against speaker
profiles (the failure mode Mike caught in 2018-s10e18 @ 09:20-10:05).
Code changes:
- src/voice_profiler.py: identify_speakers() takes optional skip_ranges
parameter; windows whose midpoint falls in a skip range get labeled
"[bumper]" and skip cosine match
- src/diarizer.py: diarize() takes optional transcript_path; pre-computes
bumper time ranges via qa_extractor._is_promo_or_bumper, passes to
identify_speakers; adds BUMPER speaker label
- benchmark.py: passes transcript_path to diarize()
Aggregate impact across 9-episode test set:
Tara attribution: 4880s -> 3680s (-1200s / -25%)
Q&A pairs: 17 -> 19 (+2)
(bumper-flagged segments had been disrupting conversation detection
in 2017-s9e30 and 2018-s10e18)
CALLER total: 1320s -> 1190s (bumpers previously labeled CALLER moved)
Per-episode bumpers caught: 1-8, total ~165 bumper segments across set
Remaining Tara false positives are real callers acoustically similar to
Tara (Christopher in 2018, Kay in 2012, William and Charles in 2015) and
guest Clay in 2015-s7e19 — those need profile rebuild + Clay profile,
not bumper filtering.
Adds download_full_archive.py — resumable mirror-style downloader that
walks IX server's /home/gurushow/public_html/archive/{year}/ and copies
all MP3s to archive-data/episodes/. Run is in progress (~589 files,
~10-15GB). Used to source clean profile windows for the remaining
co-hosts (Tara rebuild, Clay, Tony, Rob, Randall, producers).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- Voice profiler using microsoft/wavlm-base-sv (512-dim x-vector embeddings)
- Bootstrap from archive: 180 embeddings from 9 episodes across 2010-2018
- Host identification accuracy: 0.87-0.98 similarity for live speech,
0.60-0.64 for non-host audio (produced intros, co-host)
- Dropped speechbrain dependency (requires torchaudio, CUDA version conflicts)
- Patched torchaudio CUDA 12.8/13.1 version check (warning instead of error)
- Profile stored in voice-profiles/mike-swanson/ with per-chunk embeddings
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Audio processor CLI tool with 6-stage pipeline: transcribe (faster-whisper GPU),
diarize (pyannote), detect segments (multi-signal classifier), remove commercials,
split segments, analyze content (Ollama)
- Post-show workflow doc for episode posts, forum threads, deep-dive blog posts
- Training plan for using 579-episode archive for voice profiles and commercial detection
- Successful test: 45min episode transcribed in 2:37 on RTX 5070 Ti
- Sample transcript output from S7E30 (March 2015)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
