Initial commit of current state

corrections.example.txt

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+# 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