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Commits
| Author | SHA1 | Message | Date | |
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 mudler-agentandEttore Di Giacinto
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79a7631cc5 |
feat(parakeet-cpp): encoder fingerprint for speaker naming, VAD trim and word filter options, pin bump (#12491)
* chore(parakeet-cpp): bump parakeet.cpp to 2de154c Brings in the speaker registry encoder fingerprint, the VAD segment trim and the opt-in word filter, a fix for a per-call thread count that stayed set on the process-wide backend after a Silero VAD pass, and bundle components loaded from memory. Assisted-by: Claude:claude-sonnet-5-5 [Claude Code] * feat(parakeet-cpp): encoder fingerprint for speaker naming, vad_trim and guard_* options Speaker naming. A registered voice now carries the encoder that made it: the embedding family (voicedetect:<arch>:<name>:<dim>) and the sha256 of the weights. The backend reports the family of the loaded speaker model in Status, voice enrollment from speaker_profiles stores it as encoder_family (old entries load without it), and the registry sent to parakeet.cpp is built with parakeet_capi_speaker_registry_add_embedding_fp. The library then refuses a registry of another encoder family and the error names both families; another quantization of the same family only warns. A voice with only a weights hash gets the loaded family when the hashes are equal. Voices without a fingerprint (registered from audio: libvoicedetect cannot report one) keep the file-name rule and are used with a warning. The library cannot mix them with fingerprinted voices in one registry, so a request that has any uses the old registry for all. speaker_strict:true drops them instead. A library without the symbols behaves as before. Transcription. vad_trim (seconds, 0 keeps the whole cuts) goes through the VAD options JSON, so it reaches /v1/vad and the segmenter. The guard_* options guard_min_local_conf, guard_local_radius and guard_drop_punct_only turn on the word filter through parakeet_capi_transcribe_path_json_with, or through the segmenter with vad:true. They are off by default, bad values fail the load, and a library without the symbol fails it with a clear message. The dropped word count is logged at debug level. Assisted-by: Claude:claude-sonnet-5-5 [Claude Code] --------- Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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mudler-agentandEttore Di Giacinto
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9eb5a9e61d |
feat(audio): remember speakers from diarization (#12414)
* feat(schema): validate portable speaker profiles Add the versioned profile schema for explicit speaker enrollment. Validate compatibility against separately supplied loaded-encoder metadata. Reject unusable speakers, invalid vectors, and inconsistent clean spans. This slice does not change HTTP routes, backend integration, or the UI. Assisted-by: OpenAI:unknown Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(parakeet): export profiles with transcripts Export opt-in speaker profiles and trusted encoder metadata. Replay registrations by ID so duplicate display names keep independent vectors. Use one profile-capable diarization for slots, names, and clean spans. Assign timestamped ASR words to those slots without a second diarization. Preserve legacy opt-out and no-ASR behavior, and propagate failures. Assisted-by: OpenAI:unknown Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(audio): enroll portable speaker profiles Gate profile exports with voice-recognition permission and validate registration against metadata from the loaded encoder. Preserve audio enrollment and independent registrations with duplicate display names. Exclude diarization and registration exchanges before API trace capture so persisted traces cannot retain profile vectors or JSON audio. Defer candidate dimensions to trusted loaded metadata. Sort candidates by registration ID so incompatible profiles cannot suppress legacy voices through registry iteration order. Keep portable identity checks closed when trusted metadata is unavailable. Test persisted traces, explicit slot zero, and selection through offline and live transport. Document privacy and the ephemeral registry lifecycle. Assisted-by: OpenAI:unknown Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ui): remember speakers from diarization Add a Studio page for diarization and opt-in speaker profiles. Preview clean intervals from the original recording before explicit registration. Join profiles by raw speaker labels, preserve duplicate names, and relabel turns only after a successful save. Discard stale results when the model or recording changes. Share registration metadata with voice management without storing vectors or recordings from this flow. Document permissions and the global, ephemeral registry. Cover enrollment, permissions, previews, and asynchronous races with mocked Playwright tests. Assisted-by: OpenAI:unknown Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: clarify HTTP speaker enrollment support Replace the stale enrollment limitation with the current HTTP workflow. Distinguish native transport from explicit registration and link its docs. Assisted-by: OpenAI:unknown Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore(parakeet): pin merged speaker profile support Use the merged commit from mudler/parakeet.cpp#80. Its tree matches the previously accepted native pin. Assisted-by: OpenAI:unknown Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: add diarization enrollment setup example Connect the existing gallery modes to the speaker enrollment workflow. Show installation, private profile export, explicit raw-slot registration, and later recognition without another export. Assisted-by: OpenAI:unknown Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(blog): explain diarization speaker profiles Put the diarization walkthrough on the LocalAI website in the feature PR. Cover the three gallery modes, explicit enrollment, and privacy limits. Link setup instructions and keep availability conditional on feature support. Assisted-by: OpenAI:unknown Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(blog): focus diarization on everyday use Explain what users can do with recordings before the setup steps. Replace the technical walkthrough with a short Studio guide and link readers to the existing reference for model names and developer use. Assisted-by: OpenAI:unknown Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(blog): lead with speaker capabilities Present speaker recognition through everyday uses and a short UI flow. Keep technical reference details in the existing documentation. Assisted-by: OpenAI:unknown Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(diarization): satisfy Go lint checks Avoid copying protobuf message state when extending backend status, check the multipart reader close result, and document the focused testing.T lint exemptions. Assisted-by: nib:gpt-5.6-sol Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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localai-org-maint-botandEttore Di Giacinto
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2ae6cae70d |
feat(parakeet-cpp): name speakers from the shared voice registry (#12382)
* feat(voice): list registered voices and record which encoder made them The voice registry could register, identify and forget but not list, and it did not remember which speaker encoder produced an embedding. Add Metadata.Model and Registry.List, answered from the index the store registry already keeps for Forget. Needed so a backend can be given the registered voices that match its own speaker encoder. Assisted-by: Claude:claude-sonnet-5-5 [Claude Code] * feat(voice): store the encoder model with a registered voice Assisted-by: Claude:claude-sonnet-5-5 [Claude Code] * feat(voice): pick the registered voices that match a speaker model Assisted-by: Claude:claude-sonnet-5-5 [Claude Code] * feat(proto): carry known voices and speaker names on diarize and live messages Assisted-by: Claude:claude-haiku-4-5 [Claude Code] * feat(diarization): name speakers from the voice registry When a diarization model has a speaker_model option, the endpoint sends the registered voices made by that encoder to the backend. The backend's name and name_score come back as extra fields next to the normalized SPEAKER_NN speaker, and the speakers summary carries the first name seen for each speaker. RTTM output and results without names are unchanged. Assisted-by: Claude:claude-sonnet-5-5 [Claude Code] * feat(live): pass registered voices to a live session and surface speaker names Live sessions now send the registered voices that match the model's speaker_model to the backend, and each speaker segment carries the name the backend matched. The realtime segment event gains an optional speaker_name field. Assisted-by: Claude:claude-sonnet-5-5 [Claude Code] * feat(parakeet-cpp): load a speaker model and build per-request voice registries Adds the speaker bindings (ABI v9 and v10, probed separately), the speaker_model, speaker_threshold and speaker_margin options, and a per-request registry builder over the known voices. Assisted-by: Claude:claude-sonnet-5-5 [Claude Code] * feat(parakeet-cpp): name the speakers in Diarize from the known voices Diarize builds a per-request speaker registry from the known voices when a speaker model is loaded, calls the named C functions, and puts each slot's registered name and score on the segments. The registry is freed on every path. A library without ABI 10 reports Unimplemented instead of dropping the names. Assisted-by: Claude:claude-sonnet-5-5 [Claude Code] * feat(parakeet-cpp): name speakers in the live scene stream The live scene stream now begins with a known-voice registry when a speaker model is loaded and the live config carries voices, and each closed speaker segment takes its slot's current name from the feed's names map. A segment that closes before its slot is identified has an empty name. The registry is freed after the stream, on every path. Assisted-by: Claude:claude-sonnet-5-5 [Claude Code] * feat(gallery): speaker naming entries and docs for parakeet-cpp Add three gallery entries that load the WeSpeaker ResNet34 speaker model next to the diarization or realtime scene models, and document speaker names in the voice recognition, diarization, audio to text and realtime pages. Assisted-by: Claude:claude-sonnet-5-5 [Claude Code] * fix(parakeet-cpp): skip an unusable registered voice instead of failing the request A registered voice with the wrong embedding size, or one the C side refused, failed the whole diarization request, so one legacy voice broke the model for every user. Skip such voices with a warning that does not carry the voice name, and take the plain path when none is left. Also map an exact 0 speaker threshold or margin to a tiny positive value, since the C side reads 0 as "use the default", and fix a stale comment about which contexts Free() walks. Assisted-by: Claude:claude-sonnet-5-5 [Claude Code] * fix(diarization): warn once per model about voices from another encoder; document the privacy limit The different-encoder warning fired on every request. Log it once per feature and speaker model, then at debug level. Document that the global voice registry lets any caller of a speaker_model model learn matching names, and that skipped wrong-sized voices are logged. Assisted-by: Claude:claude-sonnet-5-5 [Claude Code] * chore(parakeet-cpp): bump parakeet.cpp to 8c8cec0 (C-API v10) and check speaker naming against the real library The pin moves from 623a968 to 8c8cec0, which brings in everything merged in parakeet.cpp since: the voice identification change (C-API v9, #78) and raw-embedding enroll plus diarize-only speaker naming (C-API v10, #79). New real-library specs (gated on PARAKEET_BACKEND_TEST_SPEAKER_MODEL, _DIAR_MODEL, _WAV and, for the live path, _STREAM_MODEL) name the two speakers of two_speakers.wav from a committed pair of WeSpeaker embeddings, with the voices passed in reversed order. They also check that the float32 threshold reaches C through purego. The shared test loader now registers the v9/v10 and scene symbols as main.go does. The rebase onto origin/master had no conflicts. Assisted-by: Claude:claude-sonnet-5-5 [Claude Code] --------- Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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mudler's LocalAI [bot]andEttore Di Giacinto
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1cd7d63c7b |
fix(distributed): reject wrong-model requests on the remaining modalities (#10990)
#10970 gave the four PredictOptions RPCs a model-identity check so a backend reached through a stale distributed route rejects the request instead of answering from whatever model it holds (#10952). Every other modality shares that exposure: the route is cached by host:port, a worker can recycle a stopped backend's port for another model's backend, and a liveness-only probe cannot tell a stale row from a valid one. Extends the same mechanism to the 21 remaining request messages that reach a backend through the router, using the pattern #10970 established rather than a parallel one: - proto: ModelIdentity on each modality request message. - controller: populated from ModelConfig.Model at the call site that also builds ModelOptions, so load-time and request-time values are equal by construction. - backends: one generic guard in pkg/grpc/server.go (27 Go backends), the method set in backend/python/common (36 Python backends), llama-cpp (AudioTranscription/Stream, Rerank, Score) and privacy-filter (TokenClassify). - reconcile already drops the stale row on IsModelMismatch; no change. TTSRequest and SoundGenerationRequest get a SEPARATE ModelIdentity field rather than reusing their existing `model`: FileStagingClient rewrites `model` to a worker-local path, so comparing it would reject valid requests in exactly the configuration this guards. AudioEncode/AudioDecode are deliberately left unguarded: the opus codec backend is loaded from a literal rather than a ModelConfig, so no value carries the equality guarantee the comparison depends on. The four bidirectional stream RPCs are out of scope; they bypass reconcile. Empty means skip on both sides, so an old controller, an old backend, and the bare request structs in tests/e2e-backends all keep working. Assisted-by: Claude Code:claude-opus-4-8 [Read] [Edit] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]andEttore Di Giacinto
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2be07f61da |
feat(whisper): honor client cancellation via ggml abort_callback (#9710)
* refactor(transcription): propagate request ctx through ModelTranscription* Replaces context.Background() with the HTTP request ctx so client disconnects start cancelling the gRPC call. No backend-side abort wiring yet — that comes in a later commit. Pure plumbing. Assisted-by: Claude:claude-haiku-4-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(cli): pass ctx to backend.ModelTranscription Follow-up to |
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Ettore Di Giacinto
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e86ade54a6 |
feat(api): add /v1/audio/diarization endpoint with sherpa-onnx + vibevoice.cpp (#9654)
* 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 (
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