* feat(api): add /v1/audio/diarization endpoint with sherpa-onnx + vibevoice.cpp
Closes#1648.
OpenAI-style multipart endpoint that returns "who spoke when". Single
endpoint instead of the issue's three-endpoint sketch (refactor /vad,
/vad/embedding, /diarization) — the typical client wants one call, and
embeddings can land later as a sibling without breaking this surface.
Response shape borrows from Pyannote/Deepgram: segments carry a
normalised SPEAKER_NN id (zero-padded, stable across the response) plus
the raw backend label, optional per-segment text when the backend bundles
ASR, and a speakers summary in verbose_json. response_format also accepts
rttm so consumers can pipe straight into pyannote.metrics / dscore.
Backends:
* vibevoice-cpp — Diarize() reuses the existing vv_capi_asr pass.
vibevoice's ASR prompt asks the model to emit
[{Start,End,Speaker,Content}] natively, so diarization is a by-product
of the same pass; include_text=true preserves the transcript per
segment, otherwise we drop it.
* sherpa-onnx — wraps the upstream SherpaOnnxOfflineSpeakerDiarization
C API (pyannote segmentation + speaker-embedding extractor + fast
clustering). libsherpa-shim grew config builders, a SetClustering
wrapper for per-call num_clusters/threshold overrides, and a
segment_at accessor (purego can't read field arrays out of
SherpaOnnxOfflineSpeakerDiarizationSegment[] directly).
Plumbing: new Diarize gRPC RPC + DiarizeRequest / DiarizeSegment /
DiarizeResponse messages, threaded through interface.go, base, server,
client, embed. Default Base impl returns unimplemented.
Capability surfaces all updated: FLAG_DIARIZATION usecase,
FeatureAudioDiarization permission (default-on), RouteFeatureRegistry
entries for /v1/audio/diarization and /audio/diarization, audio
instruction-def description widened, CAP_DIARIZATION JS symbol,
swagger regenerated, /api/instructions discovery map updated.
Tests:
* core/backend: speaker-label normalisation (first-seen → SPEAKER_NN,
per-speaker totals, nil-safety, fallback to backend NumSpeakers when
no segments).
* core/http/endpoints/openai: RTTM rendering (file-id basename, negative
duration clamping, fallback id).
* tests/e2e: mock-backend grew a deterministic Diarize that emits
raw labels "5","2","5" so the e2e suite verifies SPEAKER_NN
remapping, verbose_json speakers summary + transcript pass-through
(gated by include_text), RTTM bytes content-type, and rejection of
unknown response_format. mock-diarize model config registered with
known_usecases=[FLAG_DIARIZATION] to bypass the backend-name guard.
Docs: new features/audio-diarization.md (request/response, RTTM example,
sherpa-onnx + vibevoice setup), cross-link from audio-to-text.md, entry
in whats-new.md.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
* fix(diarization): correct sherpa-onnx symbol name + lint cleanup
CI failures on #9654:
* sherpa-onnx-grpc-{tts,transcription} and sherpa-onnx-realtime panicked
at backend startup with `undefined symbol: SherpaOnnxDestroyOfflineSpeakerDiarizationResult`.
Upstream's actual symbol is SherpaOnnxOfflineSpeakerDiarizationDestroyResult
(Destroy in the middle, not the prefix); the rest of the diarization
surface follows the same naming pattern. The mismatched name made
purego.RegisterLibFunc fail at dlopen time and crashed the gRPC server
before the BeforeAll could probe Health, taking down every sherpa-onnx
test job — not just the diarization-related ones.
* golangci-lint flagged 5 errcheck violations on new defer cleanups
(os.RemoveAll / Close / conn.Close); wrap each in a `defer func() { _ = X() }()`
closure (matches the pattern other LocalAI files use for new code, since
pre-existing bare defers are grandfathered in via new-from-merge-base).
* golangci-lint also flagged forbidigo violations: the new
diarization_test.go files used testing.T-style `t.Errorf` / `t.Fatalf`,
which are forbidden by the project's coding-style policy
(.agents/coding-style.md). Convert both files to Ginkgo/Gomega
Describe/It with Expect(...) — they get picked up by the existing
TestBackend / TestOpenAI suites, no new suite plumbing needed.
* modernize linter: tightened the diarization segment loop to
`for i := range int(numSegments)` (Go 1.22+ idiom).
Verified locally: golangci-lint with new-from-merge-base=origin/master
reports 0 issues across all touched packages, and the four mocked
diarization e2e specs in tests/e2e/mock_backend_test.go still pass.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
* fix(vibevoice-cpp): convert non-WAV input via ffmpeg + raise ASR token budget
Confirmed end-to-end against a real LocalAI instance with vibevoice-asr-q4_k
loaded and the multi-speaker MP3 sample at vibevoice.cpp/samples/2p_argument.mp3:
both /v1/audio/transcriptions and /v1/audio/diarization now succeed and
return correctly attributed speaker turns for the full clip.
Two latent issues surfaced once the diarization endpoint actually exercised
the backend with a non-trivial input:
1. vv_capi_asr only accepts WAV via load_wav_24k_mono. The previous code
passed the uploaded path straight through, so anything that wasn't
already a 24 kHz mono s16le WAV failed at the C side with rc=-8 and
the very unhelpful "vv_capi_asr failed". prepareWavInput shells out
to ffmpeg ("-ar 24000 -ac 1 -acodec pcm_s16le") in a per-call temp
dir, matching the rate the model was trained on; both AudioTranscription
and Diarize now route through it. This is the same shape sherpa-onnx
uses (utils.AudioToWav), but vibevoice needs 24 kHz rather than 16 kHz
so we don't reuse that helper.
2. The C ABI's max_new_tokens defaults to 256 when 0 is passed. That's
fine for a five-second clip but not for anything past ~10 s — vibevoice
stops mid-JSON, the parse fails, and the caller sees a hard error.
Pass a much larger budget (16 384 ≈ ~9 minutes of speech at the
model's ~30 tok/s rate); generation stops at EOS so this is a cap
rather than a target.
3. As a defensive belt-and-braces, mirror AudioTranscription's existing
"fall back to a single segment if the model emits non-JSON text"
pattern in Diarize, so partial / unusual model output never produces
a 500. This kept the endpoint usable while diagnosing (1) and (2),
and is the right behaviour to keep.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
* fix(vibevoice-cpp): pass valid WAVs through directly so ffmpeg is not required at runtime
Spotted by tests-e2e-backend (1.25.x): the previous fix forced every
incoming audio file through `ffmpeg -ar 24000 ...`, which meant the
backend container — which does not ship ffmpeg — failed even for the
existing happy path where the caller already uploads a WAV. The
container-side error was:
rpc error: code = Unknown desc = vibevoice-cpp: ffmpeg convert to
24k mono wav: exec: "ffmpeg": executable file not found in $PATH
Reading vibevoice.cpp's audio_io.cpp, `load_wav_24k_mono` uses drwav and
already accepts any PCM/IEEE-float WAV at any sample rate, downmixes
multi-channel input to mono, and resamples to 24 kHz internally. So the
only inputs that genuinely need an external converter are non-WAV
formats (MP3, OGG, FLAC, ...).
Detect WAVs by RIFF/WAVE magic at bytes 0..3 / 8..11 and pass them
straight through with a no-op cleanup; everything else still goes
through ffmpeg with the same 24 kHz mono s16le target. The result:
* Container builds without ffmpeg keep working for WAV uploads
(the e2e-backends fixture is jfk.wav at 16 kHz mono s16le).
* MP3 and other non-WAV inputs still get the new ffmpeg conversion
path so the diarization endpoint stays useful.
* If the caller uploads a non-WAV but ffmpeg isn't on PATH, the
surfaced error is still descriptive enough to act on.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
* fix(ci): make gcc-14 install in Dockerfile.golang best-effort for jammy bases
The LocalVQE PR (bb033b16) made `gcc-14 g++-14` an unconditional apt
install in backend/Dockerfile.golang and pointed update-alternatives at
them. That works on the default `BASE_IMAGE=ubuntu:24.04` (noble has
gcc-14 in main), but every Go backend that builds on
`nvcr.io/nvidia/l4t-jetpack:r36.4.0` — jammy under the hood — now fails
at the apt step:
E: Unable to locate package gcc-14
This blocked unrelated jobs:
backend-jobs(*-nvidia-l4t-arm64-{stablediffusion-ggml, sam3-cpp, whisper,
acestep-cpp, qwen3-tts-cpp, vibevoice-cpp}). LocalVQE itself is only
matrix-built on ubuntu:24.04 (CPU + Vulkan), so it doesn't actually
need gcc-14 anywhere else.
Make the gcc-14 install conditional on the package being available in
the configured apt repos. On noble: identical behaviour to today (gcc-14
installed, update-alternatives points at it). On jammy: skip the
gcc-14 stanza entirely and let build-essential's default gcc take over,
which is what the other Go backends compile with anyway.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(voice-recognition): add /v1/voice/{verify,analyze,embed} + speaker-recognition backend
Audio analog to face recognition. Adds three gRPC RPCs
(VoiceVerify / VoiceAnalyze / VoiceEmbed), their Go service and HTTP
layers, a new FLAG_SPEAKER_RECOGNITION capability flag, and a Python
backend scaffold under backend/python/speaker-recognition/ wrapping
SpeechBrain ECAPA-TDNN with a parallel OnnxDirectEngine for
WeSpeaker / 3D-Speaker ONNX exports.
The kokoros Rust backend gets matching unimplemented trait stubs —
tonic's async_trait has no defaults, so adding an RPC without Rust
stubs breaks the build (same regression fixed by eb01c772 for face).
Swagger, /api/instructions, and the auth RouteFeatureRegistry /
APIFeatures list are updated so the endpoints surface everywhere a
client or admin UI looks.
Assisted-by: Claude:claude-opus-4-7
* feat(voice-recognition): add 1:N identify + register/forget endpoints
Mirrors the face-recognition register/identify/forget surface. New
package core/services/voicerecognition/ carries a Registry interface
and a local-store-backed implementation (same in-memory vector-store
plumbing facerecognition uses, separate instance so the embedding
spaces stay isolated).
Handlers under /v1/voice/{register,identify,forget} reuse
backend.VoiceEmbed to compute the probe vector, then delegate the
nearest-neighbour search to the registry. Default cosine-distance
threshold is tuned for ECAPA-TDNN on VoxCeleb (0.25, EER ~1.9%).
As with the face registry, the current backing is in-memory only — a
pgvector implementation is a future constructor-level swap.
Assisted-by: Claude:claude-opus-4-7
* feat(voice-recognition): gallery, docs, CI and e2e coverage
- backend/index.yaml: speaker-recognition backend entry + CPU and
CUDA-12 image variants (plus matching development variants).
- gallery/index.yaml: speechbrain-ecapa-tdnn (default) and
wespeaker-resnet34 model entries. The WeSpeaker SHA-256 is a
deliberate placeholder — the HF URI must be curl'd and its hash
filled in before the entry installs.
- docs/content/features/voice-recognition.md: API reference + quickstart,
mirrors the face-recognition docs.
- React UI: CAP_SPEAKER_RECOGNITION flag export (consumers follow face's
precedent — no dedicated tab yet).
- tests/e2e-backends: voice_embed / voice_verify / voice_analyze specs.
Helper resolveFaceFixture is reused as-is — the only thing face/voice
share is "download a file into workDir", so no need for a new helper.
- Makefile: docker-build-speaker-recognition + test-extra-backend-
speaker-recognition-{ecapa,all} targets. Audio fixtures default to
VCTK p225/p226 samples from HuggingFace.
- CI: test-extra.yml grows a tests-speaker-recognition-grpc job
mirroring insightface. backend.yml matrix gains CPU + CUDA-12 image
build entries — scripts/changed-backends.js auto-picks these up.
Assisted-by: Claude:claude-opus-4-7
* feat(voice-recognition): wire a working /v1/voice/analyze head
Adds AnalysisHead: a lazy-loading age / gender / emotion inference
wrapper that plugs into both SpeechBrainEngine and OnnxDirectEngine.
Defaults to two open-licence HuggingFace checkpoints:
- audeering/wav2vec2-large-robust-24-ft-age-gender (Apache 2.0) —
age regression + 3-way gender (female / male / child).
- superb/wav2vec2-base-superb-er (Apache 2.0) — 4-way emotion.
Both are optional and degrade gracefully when transformers or the
model can't be loaded — the engine raises NotImplementedError so the
gRPC layer returns 501 instead of a generic 500.
Emotion classes pass through from the model (neutral/happy/angry/sad
on the default checkpoint); the e2e test now accepts any non-empty
dominant gender so custom age_gender_model overrides don't fail it.
Adds transformers to the backend's CPU and CUDA-12 requirements.
Assisted-by: Claude:claude-opus-4-7
* fix(voice-recognition): pin real WeSpeaker ResNet34 ONNX SHA-256
Replaces the placeholder hash in gallery/index.yaml with the actual
SHA-256 (7bb2f06e…) of the upstream
Wespeaker/wespeaker-voxceleb-resnet34-LM ONNX at ~25MB. `local-ai
models install wespeaker-resnet34` now succeeds.
Assisted-by: Claude:claude-opus-4-7
* fix(voice-recognition): soundfile loader + honest analyze default
Two issues surfaced on first end-to-end smoke with the actual backend
image:
1. torchaudio.load in torchaudio 2.8+ requires the torchcodec package
for audio decoding. Switch SpeechBrainEngine._load_waveform to the
already-present soundfile (listed in requirements.txt) plus a numpy
linear resample to 16kHz. Drops a heavy ffmpeg-linked dep and the
codepath we never exercise (torchaudio's ffmpeg backend).
2. The AnalysisHead was defaulting to audeering/wav2vec2-large-robust-
24-ft-age-gender, but AutoModelForAudioClassification silently
mangles that checkpoint — it reports the age head weights as
UNEXPECTED and re-initialises the classifier head with random
values, so the "gender" output is noise and there is no age output
at all. Make age/gender opt-in instead (empty default; users wire
a cleanly-loadable Wav2Vec2ForSequenceClassification checkpoint via
age_gender_model: option). Emotion keeps its working Superb default.
Also broaden _infer_age_gender's tensor-shape handling and catch
runtime exceptions so a dodgy age/gender head never takes down the
whole analyze call.
Docs and README updated to match the new policy.
Verified with the branch-scoped gallery on localhost:
- voice/embed → 192-d ECAPA-TDNN vector
- voice/verify → same-clip dist≈6e-08 verified=true; cross-speaker
dist 0.76–0.99 verified=false (as expected)
- voice/register/identify/forget → round-trip works, 404 on unknown id
- voice/analyze → emotion populated, age/gender omitted (opt-in)
Assisted-by: Claude:claude-opus-4-7
* fix(voice-recognition): real CI audio fixtures + fixture-agnostic verify spec
Two issues surfaced after CI actually ran the speaker-recognition e2e
target (I'd curl-tested against a running server but hadn't run the
make target locally):
1. The default BACKEND_TEST_VOICE_AUDIO_* URLs pointed at
huggingface.co/datasets/CSTR-Edinburgh/vctk paths that return 404
(the dataset is gated). Swap them for the speechbrain test samples
served from github.com/speechbrain/speechbrain/raw/develop/ —
public, no auth, correct 16kHz mono format.
2. The VoiceVerify spec required d(file1,file2) < 0.4, assuming
file1/file2 were same-speaker. The speechbrain samples are three
different speakers (example1/2/5), and there is no easy un-gated
source of true same-speaker audio pairs (VoxCeleb/VCTK/LibriSpeech
are all license- or size-gated for CI use). Replace the ceiling
check with a relative-ordering assertion: d(pair) > d(same-clip)
for both file2 and file3 — that's enough to prove the embeddings
encode speaker info, and it works with any three non-identical
clips. Actual speaker ordering d(1,2) vs d(1,3) is logged but not
asserted.
Local run: 4/4 voice specs pass (Health, LoadModel, VoiceEmbed,
VoiceVerify) on the built backend image. 12 non-voice specs skipped
as expected.
Assisted-by: Claude:claude-opus-4-7
* fix(ci): checkout with submodules in the reusable backend_build workflow
The kokoros Rust backend build fails with
failed to read .../sources/Kokoros/kokoros/Cargo.toml: No such file
because the reusable backend_build.yml workflow's actions/checkout
step was missing `submodules: true`. Dockerfile.rust does `COPY .
/LocalAI`, and without the submodule files the subsequent `cargo
build` can't find the vendored Kokoros crate.
The bug pre-dates this PR — scripts/changed-backends.js only triggers
the kokoros image job when something under backend/rust/kokoros or
the shared proto changes, so master had been coasting past it. The
voice-recognition proto addition re-broke it.
Other checkouts in backend.yml (llama-cpp-darwin) and test-extra.yml
(insightface, kokoros, speaker-recognition) already pass
`submodules: true`; this brings the shared backend image builder in
line.
Assisted-by: Claude:claude-opus-4-7
The /backend/monitor endpoint is routed as GET but its handler bound the
model name from a request body, which is invalid per REST and breaks
Swagger UI and OpenAPI codegen tools that refuse to send bodies with GET.
Switch to reading ?model=<name> as a query parameter and update the
Swagger annotation, regenerated spec files, and documentation. The
handler still falls back to body binding when the query parameter is
absent, so existing clients sending {"model": "..."} continue to work.
Fixes#9207
Signed-off-by: Adira Denis Muhando <dennisadira@gmail.com>
* feat(musicgen): add ace-step and UI interface
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Correctly handle model dir
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Drop auto-download
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Fixups
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Add to models, fixup UIs icons
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fixups
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Update docs
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* l4t13 is incompatbile
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* avoid pinning version for cuda12
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Drop l4t12
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat: Add backend gallery
This PR add support to manage backends as similar to models. There is
now available a backend gallery which can be used to install and remove
extra backends.
The backend gallery can be configured similarly as a model gallery, and
API calls allows to install and remove new backends in runtime, and as
well during the startup phase of LocalAI.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Add backends docs
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* wip: Backend Dockerfile for python backends
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat: drop extras images, build python backends separately
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fixup on all backends
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* test CI
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Tweaks
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Drop old backends leftovers
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Fixup CI
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Move dockerfile upper
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Fix proto
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Feature dropped for consistency - we prefer model galleries
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Add missing packages in the build image
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* exllama is ponly available on cublas
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* pin torch on chatterbox
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Fixups to index
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* CI
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Debug CI
* Install accellerators deps
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Add target arch
* Add cuda minor version
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Use self-hosted runners
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* ci: use quay for test images
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fixups for vllm and chatterbox
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Small fixups on CI
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chatterbox is only available for nvidia
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Simplify CI builds
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Adapt test, use qwen3
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore(model gallery): add jina-reranker-v1-tiny-en-gguf
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(gguf-parser): recover from potential panics that can happen while reading ggufs with gguf-parser
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Use reranker from llama.cpp in AIO images
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Limit concurrent jobs
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com>
