# Fixes for words the recognizer gets wrong the same way every time.
#
#   heard => replacement
#
# Matching is case-insensitive and word-bounded, so "coral voice" is rewritten
# mid-sentence but "chorale" is left alone. Everything after a # is ignored.
#
# This is the blunt instrument, and that is the point: it is exact, testable,
# and costs nothing at runtime. Vocabulary biasing (vocabulary.txt) is the
# softer tool that stops the mistake happening at all — reach for that first,
# and add a rule here only once you have seen the SAME wrong word more than
# once. A rule is blind to context, so make each one specific enough that it
# cannot fire on ordinary speech: prefer "coral voice" over bare "coral".
#
# These were observed in testing; delete any that don't match how you speak.
# The two engines mishear differently, so both sets are here — the rules are
# specific enough not to collide.

# SpeechTranscriber (the default). Its errors are phonetically close, which is
# what makes short rules like these enough.
Kakoro => Kokoro
Pipika => Pipecat
Metemma => Metamate
echo tale => echo tail
graph QL => GraphQL

# The older dictation model, used by --stt-engine apple and --analyzer-module
# dictation. It fails further from the target, so it needs vocabulary biasing
# as well as these.
coral voice => Kokoro voice
pit transport => Pipecat transport
pipe cat => Pipecat
LN point => endpoint
fab ricator => Phabricator
meta mate => Metamate