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Step 3 (귀): add WhisperSTT + whisper_worker, a warm out-of-venv worker mirroring the MeloTTS shape (whisper312 venv, small/int8 on CPU). transcribe() closes the voice round trip (MeloTTS wav -> whisper text); utterances() turns an injected audio_source into Utterances (Discord voice feed pending). Wired into factory as WSAI_STT=whisper. Also address the arbiter's TTS follow-ups: - melo worker error handling: capture stderr (drained in a bounded background task so the pipe can't fill), surface the real failure cause, and defend against an empty/invalid ready line instead of dying on JSONDecodeError. - pipeline pre-warm: load slow backends (warmup()) at startup so the first utterance is answered warm; a warmup failure is logged, not fatal. Verified: real TTS->STT round trip recovers the sentence near-perfectly; warm transcribe ~1.2s (CPU). 12 tests pass. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
21 lines
843 B
Plaintext
21 lines
843 B
Plaintext
# Core skeleton has NO required third-party deps (mock mode is pure stdlib).
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# Install extras per backend you enable:
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# --- screen capture (WSAI_SOURCE=mss) ---
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# mss
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# pillow
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# --- cloud eyes + brain (WSAI_VISION=claude / WSAI_BRAIN=claude) ---
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# anthropic
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# --- voice backends (run in their OWN venvs; loaded as persistent workers) ---
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# STT: faster-whisper (WSAI_STT=whisper) — installed in a dedicated venv, e.g.
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# uv venv --python 3.12 /home/claude/jarvis-stt/whisper312
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# uv pip install --python /home/claude/jarvis-stt/whisper312/bin/python faster-whisper
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# (override interpreter/model via WSAI_WHISPER_PYTHON / WSAI_WHISPER_MODEL)
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# TTS: MeloTTS (WSAI_TTS=melo) — in /home/claude/jarvis-tts/melo311
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# (Opus decode for Discord voice audio, e.g. via the selfbot/ffmpeg path — pending)
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# --- dev ---
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# pytest
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