Add words to your personal dictionary¶

Whisper sometimes mis-spells names, technical terms, or acronyms it hasn't seen in context — Kubernetes comes out as "Cuber Netties", a colleague's name gets mangled. Your personal dictionary fixes that: the words you add are primed into the STT prompt so recognition is biased toward spelling them correctly.

The dictionary lives in a plain text file, one word or phrase per line, at:

~/.config/yazses/vocabulary.txt

(On macOS it is under ~/Library/Application Support/yazses/, on Windows under %LOCALAPPDATA%\yazses\.)

Add words¶

yazses vocab add YazSes                  # add one name
yazses vocab add Kubernetes kubectl      # add several at once
yazses vocab list                        # check what is in the dictionary

Adding is case-insensitively de-duplicated, so re-adding a word is harmless. No restart is needed: the daemon re-reads vocabulary.txt on every burst, so a word added now is in effect on your next dictation.

List and remove¶

yazses vocab list                # show every word in the dictionary
yazses vocab remove kubectl      # drop a word
yazses doctor                    # confirm what reaches the recogniser

Confirm the words are actually in use¶

yazses vocab list shows what is in the file. To see what reaches the recogniser, ask doctor:

$ yazses doctor
...
  [OK] STT prompt: app name + 24 from `yazses vocab` (YazSes, NovaFabric, KubeIntellect, +21 more)

That row names every source that is folded into the prompt, so you can tell them apart at a glance:

You do not need to restart to change the dictionary — the daemon re-reads vocabulary.txt on every burst.

How it works — and its limits¶

The dictionary words are merged into Whisper's initial_prompt. That biases recognition; it does not force it. A soft prompt nudges the decoder toward your terms but a badly mis-heard word can still slip through, especially rare proper nouns spoken quickly.

For stubborn terms, two stronger, related mechanisms exist:

• [stt] initial_prompt in config.toml is a free-form context string primed into the same prompt. Your dictionary is merged ahead of it, so both take effect together. Use initial_prompt for a sentence of context ("A talk about Kubernetes and GitOps"); use the dictionary for individual terms. The yazses tune learning loop proposes additions to initial_prompt from your corpus — but only from terms you corrected. A spelling taken from tune's own re-transcription would be one Whisper model's guess at a word Whisper cannot spell, which is the case initial_prompt exists for, so tune refuses that source. If you never correct a dictation, expect no vocabulary proposal; add the term here instead.

• The hotwords feature ([hotwords], off by default) goes further than a soft prompt: it biases recognition toward your vocabulary with a hotword trie, so rare names and jargon actually win the decode rather than just being hinted. Planned — not yet wired, so features enable refuses it for now; when it lands, turn it on where a soft prompt isn't enough:

yazses features enable hotwords
yazses restart

hotwords reads the same personal dictionary you build with yazses vocab, so there is nothing extra to configure — enable it and your existing words get the stronger biasing.

See also¶

• Configuration reference — [stt] and [hotwords]
• Feature reference
• Tune for speed and accuracy