词汇表¶

General › Vocabulary   // vocabulary.settings; after Language
├── Row value -> "None", "1 term", "N terms" -> opens the Vocabulary sheet
├── Selected model takes no vocabulary (ModelDefinition::vocabulary_use)
│   -> muted note on the row and under the sheet's description: "SenseVoice
│   Small doesn't use these terms; rewriting and search still do"; Parakeet,
│   Underdog Bark, Whisper, and Qwen3-ASR (QwenCleo too) show none
├── Parakeet-based model with Labs › Vocabulary biasing off -> the sheet says
│   transcription uses the terms only with it on
├── Selected model reads fewer terms than the list holds -> muted line under
│   the sheet's description: "This model reads the first 38 of 52 terms"
│   (dictation::vocabulary::terms_read: Parakeet's 64-term cap or the
│   prompt's character budget, the same count transcription takes; scene
│   vocabulary-budget)
├── Sheet description -> what the list does, then "Terms with spaces match
│   only when said in full" (a term is matched whole; "Dax Rad" doesn't help
│   "Dax" said alone, so a name also said alone gets its own row)
├── Sheet rows -> Term | Sounds like (comma-separated) | ×; scroll past 8
├── Empty list -> Opens with one blank row focused; opening never animates rows
├── Add term -> New empty row, focused; the list glides to its end and the row
│   rises 8 pt as it fades in (SETTLE, bounce 0; Reduce Motion: fade only);
│   saved once the term has text
├── Edits -> Debounced drafts, like Modes' corrections -> settings.json `vocabulary`
├── × -> Removes that row at any click speed or focus; rows keep an identity,
│   so a late edit, focus change, or second × for a removed row touches nothing
├── Done, Escape, or a press outside -> Saves, drops blank rows, closes
├── Past the limits -> Keep the term (cut to 64 characters) and the spellings
│   that fit; inline danger note    // never drops the term
└── Boost -> no control in the window: Normal (default) unless changed with
    `hex settings set vocabulary_boost off|light|normal|strong`
    (settings.json `vocabulary_boost`)  // vocabulary.boost

Transcription (Parakeet: RNN-T and TDT, Underdog Bark and Nemotron 3.5 ASR too)  // vocabulary.transcription; transcription::gguf_session::with_vocabulary
├── Only with Labs › Vocabulary biasing on (`labs.vocabulary_biasing`, off by
│   default; labs.md) -> off: no run takes the list; the sheet says so, and
│   rewriting and search still use it
├── Every run and live stream reads the list and the boost at its start:
│   dictation, meetings, captures, the local API
├── First 64 terms (the engine's cap) and their spellings; empty or Off -> unchanged
├── Whole-clip run on an RNN-T model (Parakeet unified): meetings, captures,
│   the local API, dictation that falls back to the whole clip
│   └── The greedy words stand; where the audio could also spell a term, the
│       decoder follows the term alongside them, and the term replaces them
│       only when its score plus the boost per term piece beats theirs at the
│       word boundary (0.6 × the boost when the words don't sound like it)
├── Live stream or TDT model -> after the greedy decode, spans of up to 4 words
│   that look or sound like a term (or match a "sounds like" spelling) are
│   rescored on the same audio frames from the same decoder state: the term
│   replaces the span only when its score plus the boost per term piece beats
│   the heard words'
├── Left alone: case-only differences, inflections ("Cappuccinos"), and addresses
│   and paths ("opencode.ai"); rescoring also skips terms under 5 letters
│   without a spelling, which the RNN-T decoder can still write ("Kip" -> Kit)
└── SenseVoice, Cohere, GigaAM, Granite, Apple Speech -> no vocabulary in transcription

Context prompt (Whisper incl. ivrit.ai and NB-Whisper, Qwen3-ASR, QwenCleo)  // vocabulary.prompt-transcription; transcription::gguf_session::with_vocabulary
├── Always on (not Labs): replaced the per-model Recognition hints box (#129)
├── Every whole-clip run reads the list at its start: dictation, meetings,
│   captures, the local API; live streams run without it
├── "Term, Term, …": the first terms in list order, as many as fit 500
│   characters (Whisper: its prompt slot holds 223 tokens and trims an
│   overlong one from the front) or 1,000 (Qwen3-ASR's system message); the
│   64-term cap is Parakeet's bias array only (#1246)
├── The sheet says how many terms the selected model reads when that is
│   fewer than the list; the 64-term vs full-budget hit rate is unmeasured
├── Whisper: transcribe.cpp's generic context prompt in the <|startofprev|>
│   slot; Qwen3-ASR: the system message
├── Output that only repeats the prompt (two or more terms) is dropped, as
│   both models return it on silence or noise (README.md#get-ready)
└── "Sounds like" spellings are not sent; rewriting and search use them

Recognition hints, folded in                // vocabulary.hints
├── Loading settings.json -> `recognition_hints` of the selected model and the
│   recent models (newest first), split on commas, semicolons, and line
│   breaks, become terms; names already listed (any case) are skipped, and
│   the rewritten file drops the field
└── `hex settings get|set transcription.recognition_hints` -> deprecated alias:
    reads the terms comma-separated; set adds names (cli.md)

hex vocabulary                           // vocabulary.cli
├── list [--json]
├── add <term> [--sounds-like a,b] -> New term, or new spellings merged into it
│   └── Already there, nothing new -> changed: false
├── remove <term> -> Any case
└── Running Hex saves and reloads settings; an open window adopts the change

A mode's own vocabulary                  // vocabulary.modes; DictationMode::vocabulary
├── settings.json `dictation_processing.modes[].vocabulary`, same entries; no
│   window editor yet (Output lists it as the mode's first step; Edit with
│   agent's prompt explains it)
├── Its terms come first, then the shared list's, each term once (any case),
│   so the 64-term cap keeps them
├── OpenCode rewriting -> every OS, the mode chosen for the dictation
└── Transcription -> macOS dictation only: the mode of the app and page last
    seen by the context monitor (≤ 0.5 s old) when the stream or whole-clip run
    starts; meetings, captures, the local API, Linux, and Windows hear with
    the shared list

OpenCode rewriting (Modes)               // vocabulary.prompt
└── Prompt gains "Vocabulary (spell these exactly when the speaker means them):
    term (may be transcribed as a, b); …" before the foreground application

Live meeting chapters (experiment live-chapters)  // vocabulary.chapters
└── Chapter passes and the boundary pass start with "Names and terms that may
    appear: a, b, …" (first 30 terms, no spellings), read on each call

Meeting notes (Labs › Vocabulary in meeting notes)  // vocabulary.notes
├── Off (default) -> notes prompts are unchanged
└── On -> the final title and summary prompt (automatic notes and Regenerate
    summary) starts with the same line, read at each generation, ahead of the
    calendar event; it shares the transcript's excerpt budget (#1154)

Search                                   // vocabulary.search
├── hex meetings search, MCP search_transcripts, hex history --search
├── Query word = a one-word term, fuzzy on
│   ├── Its "sounds like" spellings, or the term as two words -> 0.5 (like a prefix)
│   └── A word that sounds like it (≥2 consonant sounds, ≤ half the letters changed,
│       not a stopword or common word) -> 0.3
└── Aliases never count as exact; exact hits always rank first

hex vocabulary listed in --help          // control/cli.rs: add #[command(hide = true)] to hide it
Prompt wording                           // dictation/processor.rs: vocabulary_lines
Chapter prompt wording                   // meeting/chapters.rs: vocabulary_line
Short-name rule                          // meeting/query.rs: SOUND_ALIKE_MIN_CONSONANTS
Suggested terms                          // dictation/vocabulary.rs: SUGGESTED
Boost strengths (nats per term piece)    // dictation/vocabulary.rs: VocabularyBoost::nats