lai-diarize

name: lai-diarize description: Identify speakers ("who said what") in aligned captions via pyannote.audio. Real speaker names come from the agent's own reasoning over transcript + context (default), with a CLI-LLM fallback for headless runs. Trigger on multi-speaker content (podcasts, interviews, meetings) or phrases like "diarize", "speaker detection", "说话人识别", "区分说话人", "label the speakers". Requires aligned captions — run /lai-align first.

Speaker Diarization

Preferred model: Claude Sonnet (cost-efficient for agent-driven naming). This skill runs on whatever model is active in the parent session — any Claude model works; no hard switch. Sonnet has no 1M-context variant, so if the parent session is Opus[1M], continuing on Opus is normal (avoids a no-op model swap).

Adds speaker labels to aligned captions. Speaker detection (who speaks when) is always CLI-based via pyannote.audio; speaker naming (who each one is) is agent-driven by default.

Basic Command

<base> = source media stem (e.g. podcast from podcast.mp3) or YouTube ID. Files all land in the current directory:

lai diarize run podcast.mp3 podcast.aligned.json podcast.diarized.json
# shortcut:
lai-diarize podcast.mp3 podcast.aligned.json podcast.diarized.json

Output labels detected speakers as SPEAKER_00, SPEAKER_01, …

Speaker count is auto-detected. Override only when auto-detection is clearly wrong:

• diarization.num_speakers=3 — exact count (when known)
• diarization.min_speakers=N / diarization.max_speakers=N — bound the search

Giving Speakers Real Names

Agent-driven (default)

After the basic command finishes, the agent reads the diarized output (the file you wrote with output_caption=…) together with any available context, and writes the named result. You may write the named version back into the same path (in-place edit) or to a separate path — depends on your project's convention.

Two-file convention (preferred when state matters, e.g. CI pipelines): emit the acoustic-only output as diarized.raw.json and let the agent write the named result to diarized.json. This keeps "acoustic切分 done" distinct from "named, ready for publish," and lets downstream stages hard-fail when the agent hasn't run yet. The ai-podcast-pipeline repo follows this convention (see its CLAUDE.md).

Signals the agent uses:

1. Explicit context the user provides in the conversation
2. meta.md beside the source (YAML frontmatter, format below)
3. Transcript evidence — self-introductions ("I'm Alice…"), mutual addressing ("thanks, Bob"), host/guest dynamics, topical expertise
4. Existing inline labels in the source text ([Alice], >> Bob:, SPEAKER_01:) — preserved by the CLI and matched by the agent
5. Speaker-change markers in supervision.custom — see Forward Search below

Process:

1. Read diarized.json — collect unique SPEAKER_XX ids and sample 3–5 segments per speaker
2. Gather context (inline hints, meta.md, transcript clues)
3. Map each SPEAKER_XX → real name with a confidence note. If unsure, keep SPEAKER_XX rather than guessing
4. Resolve ghost tiers via forward search (next subsection) before finalizing
5. Rewrite the speaker field across all segments; do not touch text, start, end, or segment order
6. Show the user the mapping before finalizing if any mapping is uncertain

Forward Search via >> / speaker-change markers

VTT and SRT broadcast captions encode speaker turns with markers like >> (usually escaped as >> in raw VTT), <v Speaker>, [Speaker], or all-caps lead-ins. LattifAI preserves whatever marker it found in supervision.custom:

"custom": {
    "original_speaker": ">>",
    "speaker_change": true
}

Key insight: >> alone (no trailing name) is still a strong signal — the captioner asserts a new speaker starts here. When the resolved speaker for such a segment is still SPEAKER_XX / Unknown / empty (typically a 1–3 segment "ghost tier" that pyannote couldn't merge into a main cluster), don't leave it unnamed. Run forward search:

1. Walk forward from this segment through same-SPEAKER_XX neighbors and onwards through later segments after >> boundaries.
2. Stop at the first identity anchor, in priority order:
   • Self-introduction — "I'm X", "My name is X", "I'm a Y at Z" (match Z against meta.md affiliation fields)
   • Cross-address — an adjacent speaker says "Thanks, X", "X, what do you think?", "Let me hand it to X"
   • Topic ownership — domain reference that pins exactly one speaker in meta.md (e.g. "in my RNA work…" → host with affiliation: "Atomic AI")
3. Backfill the anchor's real name into the originating >> segment.
4. Fallback — if no anchor is found before the speaker turn ends (next >> or end of file), keep the segment as SPEAKER_XX rather than guessing.

Dominant-neighbor merge (when >> is absent): tiers with ≤3 segments and no speaker-change marker are usually pyannote boundary artifacts. If such a segment is sandwiched between two segments of the same real speaker, attribute it to that speaker — short interjections ("Yes.", "Yeah.", "Right.") don't carry identity, and the acoustic edge is more likely segmentation noise than a third party.

meta.md (optional but strong signal — drives both num_speakers and forward-search topic anchors). All fields below are parsed by both the agent-driven path and the CLI-LLM fallback (lai diarize naming / diarize run):

---
title: "Deep Dive into LLMs"
speakers:
  - name: Alice Chen
    role: host
    affiliation: "Anthropic (research engineer)"   # self-introduction & topic-ownership anchor
    aliases: ["Alice"]                              # short forms LLM should fold back to full name
    bio: "Host of the show. Background in distributed systems."
  - name: Bob Smith
    role: guest
    affiliation: "Stanford AI Lab"
    aliases: ["Bob", "Bobby"]
    bio: "PhD candidate working on RLHF and scaling laws."
topics: ["RLHF", "scaling laws", "alignment"]       # episode-level keyword hints
prior_episodes:
  - "Episode 42: pretraining — same guest, covers scaling laws"
---

Keep name clean (no ", OpenAI" suffix) — put organizations in affiliation so the agent can match self-introductions ("I'm a researcher at Stanford" → Bob) to exactly one speaker, and downstream slug resolvers don't break on commas. aliases let the LLM map cross-references like "thanks, Swyx" back to the full legal name instead of inventing a third speaker; bio and topics give the LLM episode-specific expertise to anchor topical references against.

CLI-LLM fallback (headless / automated runs)

When the agent is not in the loop (batch pipelines, CI, unattended scripts), let the CLI do name inference with its own LLM backend:

lai config set diarization.llm.model_name gemini-3-flash-preview    # one-time
# Gemini key: see /lai-transcribe
lai diarize run --direct -Y \
    podcast.mp3 podcast.aligned.json podcast.diarized.json \
    diarization.infer_speakers=true \
    diarization.llm.reasoning=true

• diarization.infer_speakers=true — enable CLI-side name inference (requires LLM config above)
• diarization.llm.reasoning=true — ask the LLM to show its reasoning before committing to a name; trades latency for accuracy on ambiguous speakers

You can also pass hints at invocation time without any LLM:

lai diarize run podcast.mp3 podcast.aligned.json podcast.diarized.json \
    context="Host: Alice Chen (tech journalist), Guest: Bob Smith (AI researcher)"
# or point at a meta.md (first positional `context` arg also accepts a file path):
lai diarize run podcast.mp3 podcast.aligned.json podcast.diarized.json context=podcast.meta.md

Output

Each supervision gains a speaker field:

{ "text": "Welcome to the show.", "start": 0.0, "end": 2.5, "speaker": "Alice Chen" }

Common Issues

Related Skills

• /lai-align — produce the aligned input (required)
• /lai-transcribe — transcript from scratch
• /lai-translate, /lai-summarize — run on diarized output for speaker-aware results