Each emotion can now be tuned independently. parse_segments resolves the four
controls per segment from a base (공통) dict plus an optional per-emotion
override; a missing override key inherits base. By default there are no
overrides, so every emotion delivers with the base values (모든 감정 = 기본값).
- emotion.py: Segment now carries all 4 controls + the canonical emotion name;
parse_segments(text, base, overrides). Adds EMOTION_LABELS/EMOTIONS for the UI.
- melo.py: MeloTTS.emotion_overrides store; synth resolves per-segment controls
and sends them per segment.
- melo_worker.py: _render applies each segment's own word_gap/sentence_gap/pitch
(previously reply-global).
- dashboard.py: emotion dropdown in the TTS panel; GET returns base + overrides
+ emotion list; POST {emotion,...} stores an override (or {reset:true} clears
it); base is set when emotion is omitted/"base".
Verified: an override on one emotion slows only that emotion (happy@0.7=5.66s vs
base 3.02s; sad unchanged at 3.06s); dashboard store/reset/base all work; 34
tests pass.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The dashboard voice-turn path ran _speech_text() before MeloTTS.synth, which
rewrote a leading "[힘차게] 안녕!" into "힘차게, 안녕!" — reading the first
emotion aloud and destroying the tag before the TTS emotion parser could use it.
Make _speech_text() a pass-through so every emotion tag (including the first)
reaches synth intact and shapes pitch/speed instead of being spoken. Adds a
regression test covering the leading-tag case.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Emotion tags now steer delivery rather than being read aloud. parse_segments()
splits a reply on [감정] tags: a recognised emotion word switches the pitch and
speed of the text that follows (and is dropped), while a non-emotion bracket
(e.g. [1번]) keeps its inner words as spoken content. Emotions can change
mid-reply, so a single turn is synthesised as several pitch-shifted segments and
concatenated in the melo worker (librosa pitch_shift, warmed at startup).
The emotion vocabulary is grounded in Azure Neural TTS speaking styles plus
Ekman's basic emotions, with Korean synonyms. The brain persona is updated to
emit inline tags from that set.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
