kami
5fe8f228c1
Add a read-only /ecosystem page that consumes the sibling services' JSON APIs (Nexus entities, Praxis attention, Hexis capabilities), fetched concurrently with honest per-panel error states. Siblings stay headless — mavweb is their human surface (arch §16). Wired via mavweb -nexus/-praxis/-hexis flags; mavweb joins the ecosystem compose network. Fix mobile horizontal overflow across all pages: .content is a flex child with default min-width:auto, so it refused to shrink below the tables' intrinsic width. min-width:0 lets wide tables pan inside .scroll instead of dragging the page sideways. Verified via CDP geometry check (scrollWidth === clientWidth at 430px). Also includes in-progress Ethos UI redesign, ecosystem deploy compose, and planning docs. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
28 lines
2.3 KiB
Markdown
28 lines
2.3 KiB
Markdown
# Plan: Speaker Recognition
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**Goal:** Maven can distinguish between different speakers on the voice channel — recognize known voices (the user, family members) and tag facts/notes/transcripts with a speaker identity.
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**Done when:**
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- Speaker embedding extractor (e.g., ECAPA-TDNN or a simple MFCC + GMM) runs on incoming voice PCM before STT
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- Embedding is compared against enrolled speaker profiles (stored as vectors in the `memory_vectors` table alongside semantic memory)
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- Unknown speakers are enrolled on first interaction (prompt: "кто это?")
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- All voice fact/note writes are tagged with `speaker:<id>` in the value/source metadata
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- Speaker identity is available as context to the router, phraser, and replier ("ok, <name>")
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**Scope:**
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- New `internal/speaker/` package — enrollment, recognition, embedding extraction
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- Reuses `internal/store.MemoryStore` for speaker vector storage (same `memory_vectors` table, different `source` prefix)
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- Reuses `internal/audio` for PCM preprocessing
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- Integration point: `cmd/mavend/voice.go:HandlePushToTalk` — speaker ID extracted before STT, passed through context
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**Steps:**
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1. Research speaker embedding approaches — simplest floor: MFCC + cosine similarity via `github.com/mjibson/go-dsp` or a pre-trained ONNX model (SpeechBrain ECAPA)
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2. Create `internal/speaker/recognizer.go` — `Recognizer` interface: `Identify(pcm []float32) (SpeakerID, confidence)`, `Enroll(id, pcm)`
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3. Create `internal/speaker/store.go` — speaker profile CRUD via `store.MemoryStore`: `Insert("speaker:<id>", embedding, meta)`, `Search(embedding, k)`
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4. Create `internal/speaker/enroll.go` — enrollment flow: capture N seconds of audio, extract embedding, prompt for name via TTS + STT round-trip
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5. Wire into `cmd/mavend/voice.go:HandlePushToTalk` — run speaker ID on the PCM before STT; pass speaker ID through `context.Context` to `applyAction`
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6. Tag all voice-written facts/notes with speaker ID — `Source` becomes `tap:voice:speaker:<id>` or metadata field
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7. Add IPC methods `MethodEnrollSpeaker`, `MethodListSpeakers`, `MethodRemoveSpeaker`
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8. Add speaker config block to `voice` in `config.Config` — `{speaker_recognition: true, model_path}`
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9. Test with 2+ recorded voice samples — verify correct identification and rejection of unknown speakers
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