feat(ced): sound-event classification backend (CED audio tagger) - #10425
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Wires ced.cpp (CED, 527-class AudioSet sound-event tagger; baby cry, footsteps, glass, alarms, dog bark) into LocalAI as a Go/purego backend. SKETCH (backend skeleton real; core REST wiring + CI/gallery is a checklist in DESIGN.md): - backend/backend.proto: new SoundDetection rpc + SoundClass messages (run `make protogen-go` to regenerate pkg/grpc/proto). - backend/go/ced: main.go (purego dlopen libced.so + ced_capi.h), goced.go (Ced gRPC backend: Load + SoundDetection), Makefile (clone-at-pin CED_VERSION, ggml static-PIC shared build), run.sh, package.sh, .gitignore. - DESIGN.md: REST /v1/audio/classification wiring (handler/route/capability registration checklist), gallery/index + CI registration, and a scoping note for the realtime/websocket live-recognition path (sliding-window classify over the existing ws transport + voicegate; the ced C-API per-PCM entry point is already window-friendly). Backend code does not compile until protogen-go regenerates the pb types and a libced.so is built (Makefile clones+builds it). Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
…ration
Wires the ced sound-event classification backend (AudioSet audio tagger)
end to end through the REST surface, mirroring the transcription path.
- Handler: core/http/endpoints/openai/sound_classification.go parses the
multipart audio upload, temp-files it, resolves the model config and
calls the SoundDetection RPC; returns {model, detections[]} JSON.
- Backend wrapper: core/backend/sound_classification.go (ModelSoundDetection)
loads the model and normalizes the proto response into schema types.
- Schema: core/schema/sound_classification.go (SoundClassificationResult).
- gRPC layer: SoundDetection wired through the LocalAI wrapper (interface,
Backend client, Client, embed, server, base default) so the loader-typed
client exposes the RPC; proto regenerated via make protogen-go.
- Route: POST /v1/audio/classification (+ /audio/classification alias) with
the audio/multipart default-model middleware in routes/openai.go.
- Capability surfaces: swagger @Tags/@router on the handler; FLAG_SOUND_
CLASSIFICATION usecase flag + UsecaseSoundClassification + UsecaseInfoMap +
GuessUsecases + ModalityGroups + GetAllModelConfigUsecases; meta usecase
option; /api/instructions audio area updated; auth RouteFeatureRegistry +
FeatureAudioClassification (APIFeatures, default ON) + FeatureMetas; UI
usecaseFilters, capabilities.js CAP_SOUND_CLASSIFICATION, Models.jsx filter
+ i18n; docs page features/audio-classification.md + whats-new + crosslink.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
When a realtime pipeline configures a sound-classification model, each
VAD-committed utterance (the same window the transcription path produces)
is also run through the CED sound-event classifier and the scored AudioSet
tags are emitted as a new server event. No new backend rpc is needed: the
SoundDetection gRPC method already exists on this branch.
- config: add Pipeline.SoundDetection (yaml/json sound_detection,omitempty)
beside Transcription/VAD.
- realtime: add Model.SoundDetection(ctx, audio, topK, threshold) to the
ModelInterface; implement it on wrappedModel and transcriptOnlyModel by
calling backend.ModelSoundDetection with the session's sound-classification
model config (mirrors how Transcribe dispatches). Load the optional config
in newModel / newTranscriptionOnlyModel; nil config keeps it additive.
- types: add ConversationItemSoundDetectionEvent (item_id, content_index,
detections[]{label,score,index}) with type conversation.item.sound_detection,
its ServerEventType constant and MarshalJSON, mirroring the transcription
completed event.
- realtime: add emitSoundDetection (unary path: classify the committed window,
build the event, t.SendEvent) and wire it at the utterance-commit hook right
after emitTranscription; gated on session.SoundDetectionEnabled (resolved
from Pipeline.SoundDetection at session setup, defaults top_k=5, threshold=0).
Its error is logged via xlog but never aborts the turn.
- test: Ginkgo specs for emitSoundDetection (tags emitted, empty detections,
classifier error) plus a SoundDetection method on the fakeModel double.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
The SoundDetection method added to the grpc backend interface left two test doubles (fakeBackendClient, fakeGRPCBackend) incomplete, so core/services/nodes failed to compile under `go vet`/`go test` (go build missed it: the doubles live in _test.go). Add the method to both, mirroring their existing Detect mock. Repairs CI for the nodes package. Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
…sions) Sound-event detection must activate on sounds, not speech, so it no longer runs through the voice VAD/transcription path. A sound-detection-only pipeline (sound_detection set, no transcription/LLM) now: - is accepted by prepareRealtimeConfig (sound_detection counts as a pipeline stage), - builds a lightweight model via newSoundDetectionOnlyModel (no VAD/STT/LLM/TTS loaded), and - defaults the session to turn_detection none (no VAD) with no transcription stage, so the client drives windowing via input_audio_buffer.commit (option A: client-side sliding window). The per-PCM C-API already supports arbitrary windows. commitUtterance gains a sound-only branch: it emits the conversation.item.sound_detection event (scored AudioSet tags) and stops - no transcription, no LLM response. generateResponse is now guarded on a transcription stage being present, so a sound-only turn never invokes the LLM. Existing transcription/VAD sessions are unchanged (additive). Added a commitUtterance sound-only Ginkgo spec asserting it emits the sound event and neither transcribes nor generates a response. go vet + golangci-lint (new-from-merge-base) clean; openai suite green. Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Mechanical backend-image registration for the ced sound-event classifier,
mirroring the parakeet-cpp Go/purego backend everywhere it is wired up.
- .github/backend-matrix.yml: add the ced build matrix, field-for-field copies
of the parakeet-cpp entries (cpu amd64/arm64, cublas cuda 12/13 amd64,
l4t cuda-13 arm64, l4t-jetpack cuda-12 arm64, sycl f32/f16, vulkan
amd64/arm64, rocm hipblas, and the metal darwin entry), changing only
backend and tag-suffix. dockerfile stays ./backend/Dockerfile.golang.
- backend/index.yaml: add the &ced meta anchor (capabilities map per platform)
plus ced-development and the per-arch image entries, each uri/mirror
tag-suffix matching the matrix exactly. The model gallery (GGUF) entry is
intentionally deferred pending the HuggingFace publish (TODO note inline).
- scripts/changed-backends.js: add an explicit item.backend === "ced" branch in
inferBackendPath mapping to backend/go/ced/, same mechanism and ordering as
the parakeet-cpp branch (before the generic golang fallthrough).
- .github/workflows/bump_deps.yaml: register mudler/ced.cpp -> CED_VERSION in
backend/go/ced/Makefile so the daily bot bumps the pin.
- swagger/{docs.go,swagger.json,swagger.yaml}: regenerated via make swagger so
the existing /v1/audio/classification annotations land in the generated spec.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Adds an optional server-driven sliding-window classifier so a sound-only realtime client only has to stream audio (no input_audio_buffer.commit): - Pipeline.sound_detection_window_ms / sound_detection_hop_ms config knobs. When both > 0 on a sound-only session, the server classifies the last window of streamed audio every hop and emits a conversation.item.sound_ detection event; the input buffer is trimmed to one window so a long stream stays bounded. When unset, the session stays client-driven (option A). Runs independent of VAD (sound events are not speech). - handleSoundWindow (ticker) + classifySoundWindow (one tick, extracted so it is unit-testable) + writeWindowWAV, which declares the true InputSampleRate (NewWAVHeaderWithRate) so the classifier resamples correctly. Goroutine is started after toggleVAD and torn down with the session (close + wg.Wait). - Register pipeline.sound_detection (+window_ms/hop_ms) in the config meta registry; the earlier realtime commit added pipeline.sound_detection without a registry entry, failing TestAllFieldsHaveRegistryEntries. This fixes that and covers the two new knobs. Tests: classifySoundWindow emits an event + trims the buffer to one window, no-ops on too-little audio; writeWindowWAV declares the given sample rate. go build/vet + golangci-lint (new-from-merge-base) clean; config + openai suites green. Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
The ced-base weights are now published at mudler/ced-base-gguf (Apache-2.0, converted from mispeech/ced-base). Adds gallery/ced.yaml (backend: ced + known_usecases: sound_classification) and two gallery/index.yaml entries (ced-base-f16 default, ced-base-q8 smallest) with sha256-pinned files, and removes the now-resolved TODO from backend/index.yaml. Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Publishes the rest of the CED family (same architecture, metadata-driven port
verified end-to-end on ced-tiny) to mudler/ced-{tiny,mini,small}-gguf and adds
their f16 + q8_0 gallery entries:
ced-tiny (5.5M, edge/Pi-class) f16 11MB / q8_0 6MB
ced-mini (9.6M) f16 19MB / q8_0 11MB
ced-small (22M) f16 42MB / q8_0 23MB
All sha256-pinned. ced-base remains the accuracy default.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
… repo All CED quantizations (tiny/mini/small/base, f16/q8_0) now live in a single HuggingFace repo, mudler/ced-gguf, instead of per-model repos. Repoint the 8 gallery model entries' urls + file uris accordingly. sha256 and filenames are unchanged. Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Pin the ced backend to ced.cpp 99c6ed3, which fixes a crash on any clip shorter than target_length (~10.11s): time_pos_embed was added at its full 63-frame grid instead of being sliced to the clip's actual time grid, tripping ggml_can_repeat in ggml_add. Surfaced by the live realtime e2e (sub-10s windows) and gated with a short-clip parity test upstream. Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
…ctive - README.md: add ced.cpp to the "native C/C++/GGML engines developed and maintained by the LocalAI project" table. - docs/content/features/backends.md: add a Sound Classification backend category (sound-event classification / audio tagging) listing ced.cpp. - .agents/adding-backends.md: add a "Documenting the backend" section and two verification-checklist items requiring new backends to be documented in the backends.md category list, and in-house native engines to be added to the README maintained-engines table. This directive was missing. Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
ced.cpp history was squashed into a single release commit (tagged v0.1.0), so the previous pin (99c6ed3) no longer exists upstream. Pin to c04ac14, the v0.1.0 release commit, so the backend builds against a commit that exists. Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
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- sound_classification.go: os.Create(dst) where dst = temp dir + path.Base of the upload (no traversal). #nosec G304, matching the depth-anything-cpp handler. - goced.go: reading a NUL-terminated C string from a libced-owned buffer. #nosec G103 (gosec) + //nolint:govet (golangci-lint's unsafeptr check), since the uintptr is a C-owned malloc'd buffer, not Go-GC memory. Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
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| defer CppFreeString(p) | ||
| var b []byte | ||
| for i := 0; ; i++ { | ||
| ch := *(*byte)(unsafe.Pointer(p + uintptr(i))) //nolint:govet // #nosec G103 -- C-owned NUL-terminated string from libced (not Go-GC memory) |
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Adds a sound-event classification / audio-tagging backend to LocalAI, backed by ced.cpp - a standalone C++/ggml port of CED (Xiaomi), a SOTA-tier 527-class AudioSet tagger (baby cry, footsteps, glass breaking, alarms, dog bark, ...).
CED is a plain AST/DeiT ViT over a log-mel spectrogram, ported to ggml with full PyTorch parity (f32 e2e 1.7e-7; f16/q8_0 near-lossless, identical top-5; verified on ced-tiny and ced-base). Apache-2.0 weights. CPU perf: ced.cpp f16 is 1.55x faster than the PyTorch reference (~100x realtime, 100 ms/clip), q8_0 uses 6.5x less memory.
Done
SoundDetectionrpc + backendbackend/go/ced/(purego overced_capi.h)POST /v1/audio/classification+ capability registration (swagger,/api/instructions, auth feature, FLAG usecase, Reactcapabilities.js, docs)pipeline.sound_detection:conversation.item.sound_detectionevents with scored tagsturn_detection: none(activates on sounds, not speech)pipeline.sound_detection_window_ms/_hop_ms(option B, client just streams)backend/index.yamlmeta-backend,.github/backend-matrix.yml,scripts/changed-backends.js,bump_deps.yaml,make swaggerced-{base,tiny,mini,small}-{f16,q8}) ingallery/index.yaml, sha256-pinned, all weights published to a single repomudler/ced-gguf(5.5M tiny -> 86M base; tiny/q8 is 6 MB)SoundDetectionadded to the grpc backend interface + config meta registry (repairsnodes+metatest compilation)Assisted-by: Claude Code (claude-opus-4-8)