Streaming replies and STT, amplitude lip-sync, one place for speaking

Latency: replies are spoken sentence-by-sentence off the desk API's NDJSON
endpoint, so the wait is time-to-first-sentence rather than the whole model
call, and Deepgram's live websocket transcribes while you're still talking
instead of uploading the WAV afterwards. Both fall back invisibly — a stream
that fails before anything was said drops to converse(), and a socket that
never opens just means the old one-shot path.

Speaking lived in four near-copies in the controller (a reply, a holding line,
a streamed sentence, a dialogue scene) that had already drifted: one didn't arm
barge-in, another skipped the follow-up rule. It's now speech.Speaker plus an
Utterance describing the policy differences, with collaborators injected so the
whole of it tests without Qt or audio.

The mouth follows the audio rather than a timer: tts.level_of reduces each PCM
frame to a 0..1 loudness on a sqrt curve (speech sits well below peak, and a
linear map leaves the mouth barely open during normal talking) and that indexes
the talking frames, which the sprite script now draws as an openness ramp.
Offline pyttsx3 has no waveform, so stale levels hand control back to the timed
loop instead of freezing the mouth mid-syllable.

Also: the pet starts where you left it (ignoring positions on monitors that are
no longer connected, since restoring those faithfully is how it ends up
somewhere unreachable), and `python -m bolt_pet --doctor` is a preflight that
says what to do about each problem rather than only what's wrong.

tests/test_pipeline_smoke.py breaks the pure-logic rule on purpose. Every unit
test passed all week while notifications sat unspoken for minutes, the pet said
things twice and [laughing] got read aloud — each an interaction between two
individually-correct units. It drives whole turns against a real HTTP server on
a loopback port, faking only the mic and the speakers. It found a NameError in
the paint path that would have fired on every repaint while talking.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

tests/test_speech_text.py

@@ -4,6 +4,8 @@
 import sys
 from pathlib import Path
 
+import pytest
+
 sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
 
 from bolt_pet.speech_text import for_display, for_speech, is_question
@@ -77,3 +79,33 @@ def test_is_question_ignores_question_marks_that_are_not_spoken():
     assert not is_question("Docs are at https://example.com/x?y=1")
     assert not is_question("")
     assert not is_question(None)
+
+
+# ── ElevenLabs v3 delivery tags (observed live 2026-07-31) ──────────────────
+# "[laughing] That one came through clean" was spoken as "laughing That one
+# came through clean": the tags mean something to the dialogue model inside a
+# dialoguectl scene, and nothing at all to the ordinary reply voice.
+
+def test_delivery_tags_are_never_spoken_aloud():
+    spoken = for_speech("[laughing] That one came through clean.")
+    assert "laughing" not in spoken
+    assert spoken.startswith("That one came through clean")
+
+
+def test_the_bubble_does_not_caption_a_laugh_nobody_heard():
+    assert "[laughing]" not in for_display("[laughing] All good.")
+
+
+@pytest.mark.parametrize("tag", [
+    "[whispering]", "[cheerfully]", "[sighs]", "[laughs]", "[clears throat]",
+    "[nervously]", "[excited]", "[pause]", "[shouting]",
+])
+def test_the_common_tags_are_all_covered(tag):
+    assert "" == for_speech(tag).strip()
+
+
+def test_ordinary_bracketed_text_survives():
+    """The tags are matched narrowly on purpose — real bracketed content is
+    part of what the user asked to hear."""
+    assert "1" in for_speech("See reference [1] for details.")
+    assert "docs" in for_speech("It's in [the docs] somewhere.")