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20 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
Richard Palethorpe
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63bcbf6c12 |
fix(pii): post-merge review fixes + live NER e2e for the privacy-filter tier (#10401)
* fix(pii): post-merge review fixes + live NER e2e for the privacy-filter tier Follow-up to the NER tier engine (#10360), already on master. This carries only the incremental review fixes and tests that postdate that merge — the feature itself is not re-introduced. Review fixes: - openai_completion.go: remove the dead `elem >= 0` conjunct in applyAnyText (the `elem < 0` guard above already returns). - application.go: collapse ResolvePIIPolicy's inline re-implementation of PIIIsEnabled to a single cfg.PIIIsEnabled() call (sole source of the "explicit pii.enabled wins, else cloud-proxy default" rule) and return true past the !enabled guard where it is provable. - pattern.go: hoist the triple `appConfig != nil && EnableTracing` check in patternDetector.Detect into one local. - grammar.go: MaxQuantifier was 4096, but Go's regexp/syntax rejects repeat bounds above 1000 at Parse time, so walk()'s {n,m} guard could never fire — dead code shadowed by the parser. Lower it to 512 so a bound in (512,1000] is rejected here with an actionable error; >1000 still fails closed via Parse. Specs pin the relationship so the guard can't silently revert. - PatternListEditor.jsx: clamp a directly-typed negative min_len to >=0 and force the DOM value back when clamping (min={0} only constrained the spinner, so a negative reached saved config and silently disabled the length filter). Tests: - piipattern_test.go: MaxQuantifier guard specs (must stay live, not dead). - model-config.spec.js: assert the min_len clamp, and that entity_actions collapses a duplicate group to a single row (map semantics; regression guard against emitting an array that drops a row on save). - tests/e2e-backends: token_classify capability driving the TokenClassify gRPC RPC against the backend image, asserting byte-correct, UTF-8 rune-aligned spans (entity.Text == text[start:end]) at threshold 0. Verified on CPU via `make test-extra-backend-privacy-filter` (3/3 specs). - Makefile: test-extra-backend-privacy-filter wrapper. - tests/e2e: e2e_pii_ner_test.go drives /api/pii/analyze + /api/pii/redact (mask + block) through the full HTTP -> detector -> redactor path; gated on PII_NER_MODEL_GGUF so the default suite is unaffected. - .github/workflows/tests-pii-ner-e2e.yml: path-filtered / nightly CI job running the container harness on CPU. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Richard Palethorpe <io@richiejp.com> * feat(gallery): add privacy-filter-nemotron (f16 + q8) GGUF conversions of OpenMed/privacy-filter-nemotron — a fine-grained English PII token-classifier (55 categories / 221 BIOES classes), fine-tuned from openai/privacy-filter on NVIDIA's Nemotron-PII dataset. Sibling to the existing privacy-filter-multilingual entry, trading language breadth for category depth. - privacy-filter-nemotron: F16 reference artifact (~2.8 GB). - privacy-filter-nemotron-q8: Q8_0 quant (~1.64 GB) for RAM-constrained / edge use; description notes the size/speed tradeoff and to validate on your own data (a single dropped span is a PII leak). Both run on the privacy-filter backend with known_usecases [token_classify] and a default mask policy (min_score 0.5); operators add per-category entity_actions as needed. sha256s taken from the HF repo's LFS object ids. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Richard Palethorpe <io@richiejp.com> --------- Signed-off-by: Richard Palethorpe <io@richiejp.com> |
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LocalAI [bot]
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32c47706ae |
feat(realtime): speaker-aware conversations - surface identity to client and LLM (#10424)
* feat(realtime): add voice_recognition enforce + identity config Add Enforce *bool and Identity *VoiceIdentityConfig to PipelineVoiceRecognition, plus EnforceGate/IdentityEnabled/ AnnounceEnabled/PersonalizeEnabled helpers. Enforce nil defaults to gating (backward compatible); identity surfacing is independent of the gate. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(realtime): add Speaker type and conversation.item.speaker event Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(realtime): split voiceGate into Resolve + authorize Split the speaker authorization into a Resolve step (embed once, produce a types.Speaker identity) and a pure authorize policy step, with a 0..100 confidence score mirroring /v1/voice/identify. The legacy Authorize wrapper is kept so existing specs stay green. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(realtime): resolve speaker per turn and emit conversation.item.speaker Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(realtime): personalize LLM turns with recognized speaker Set the per-message name field on each recognized user turn and append a current-speaker note to the system message, both gated by the voice recognition identity config. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(realtime): document speaker identity surfacing and personalization Document the new voice_recognition keys (enforce, identity.*) and the LocalAI-extension conversation.item.speaker server event in the realtime feature docs. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(realtime): cover when:first+identity re-resolution and multi-speaker history Add two integration specs to harden the speaker-aware realtime path: - when:first with an Identity block re-resolves the speaker every turn even though re-authorization is skipped after the first match: a later resolve error now fails closed, while a clean later resolve still surfaces and names the speaker. - multi-speaker history attribution: each user turn carries its own per-message name and the injected system note reflects the latest speaker. Test-only change; no production behavior was modified. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(realtime): surface speaker labels in conversation.item.speaker Carry the registered speaker's labels (identify mode) on types.Speaker so they flow into the conversation.item.speaker event and the stored item. Verify mode has no labels, so the field is omitted there. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(e2e): cover conversation.item.speaker over a real websocket Add a realtime-pipeline-identity config (verify mode, enforce:false, identity announce+announce_unknown+personalize) and two e2e specs driving the real server over a real WebSocket with the mock VoiceEmbed backend: an authorized speaker yields a conversation.item.speaker event naming e2e-speaker (matched true) and reaches response.done; an unauthorized speaker yields an unknown (matched false, no name) event and still responds, proving enforce:false never drops a turn. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(config): register voice_recognition enforce + identity fields The meta registry coverage test (TestAllFieldsHaveRegistryEntries) requires every config field to have an entry in core/config/meta/registry.go. The new voice_recognition.enforce and voice_recognition.identity.* fields were missing, failing tests-linux and tests-apple. Add registry entries (toggles) so the fields are surfaced in the model-config editor and the coverage test passes. Assisted-by: Claude:claude-opus-4-8 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> Co-authored-by: Ettore Di Giacinto <mudler@users.noreply.github.com> |
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Richard Palethorpe
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3fa7b2955c |
feat(pii): NER tier engine — privacy-filter.cpp backend + NER-centric PII filter (#10360)
Squashed feat/pii-ner-tier-engine rebased onto master (was 45 commits; see backup/pii-ner-tier-engine-prerebase). Net change: - privacy-filter.cpp: standalone GGML engine for the openai-privacy-filter PII/NER token classifier, wired as a LocalAI gRPC backend (CPU/CUDA/Vulkan). TokenClassify moves off the patched llama.cpp path onto this backend. - PII filter reworked to be NER-centric (encoder/NER detection tier scanning whole conversations as one document), with a recreated bounded restricted- regex secret-matching pattern detector tier alongside it (per-model pii_detection.builtins / .patterns + core/services/routing/piipattern). - Detection labelled by source (ner vs pattern); backend trace / confidence / debug observability; analyze/redact exposed as a synchronous API. - Instance-wide default detector policy + per-usecase default-on; request filtering extended to completions, embeddings, edits & Ollama. - React UI: NER-centric PII editor, detector-models table, pattern/builtins editor, middleware default-policy UI. - Gallery: privacy-filter-multilingual token-classify model + NER install filter; token_classify known_usecase; batch sized to context for NER models. privacy-filter backend registered in the backend gallery (cpu/vulkan/cuda-13 meta + image entries with a capabilities map) matching its CI matrix jobs, and an /import-model auto-detect importer (PrivacyFilterImporter, narrow privacy-filter GGUF detection) replacing the prior pref-only registration. Reconciled against master's independent evolution: - Dropped master's PIIPatternOverrides feature (global-pattern runtime overrides + /api/pii/patterns API + runtime_settings.json persistence). The per-model NER + pattern-detector design supersedes it; it was built on the global redactor pattern set this branch replaced. - Reverted the llama.cpp Score carry-patch (0006-server-task-type-score): removed the patch and restored master's grpc-server.cpp Score RPC (direct llama_decode, slot-loop bypass) and LLAMA_VERSION pin, plus master's model_config validation forbidding score + chat/completion/embeddings on llama-cpp. token_classify is unaffected (it runs on the privacy-filter backend, not llama-cpp). Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Richard Palethorpe <io@richiejp.com> |
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LocalAI [bot]
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4d3d54d61b |
test(e2e): live-server voice-recognition gate test (#10324)
Add mock-backend VoiceEmbed/VoiceVerify (deterministic DC-offset speaker discrimination) and a verify-mode gated realtime pipeline, then drive the real HTTP/WS stack: an authorized speaker reaches response.done while an unauthorized one is dropped before the LLM with a speaker_not_authorized event. Assisted-by: Claude:opus-4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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Richard Palethorpe
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085fc53bbc |
fix(router): production-ready request router + auto-size batch for embedding/rerank (#10104)
* fix(router): score classifier production-readiness Conversation trimming runs through the classifier model's chat template and trims by exact token count, sized to the model's n_batch which is now scaled to context so long probes can't crash the backend. Missing chat_message templates are a hard error at router build time. Router- facing factories (Embedder/Scorer/Reranker/TokenCounter) re-resolve ModelConfig per call so a model installed post-startup doesn't bind a stub Backend="" config and silently fall into the loader's auto- iterate path. New 'vector_store' backend trace recorded inside localVectorStore on every Search/Insert — including the backend-load-failure path that previously vanished into an xlog.Warn — with outcome tagging (hit/miss/empty_store/backend_load_error/find_error/insert_error/ok). Companion cleanup drops misleading similarity:0 and input_tokens_count:0 from non-hit and text-mode traces. Gallery local-store-development aliases to 'local-store' so the master image satisfies pkg/model.LocalStoreBackend lookups from the embedding cache. Misc: llama-cpp TokenizeString reads the correct 'prompt' JSON key (the original bug); ModelTokenize nil-guard; non-fatal mitm proxy startup; PII 'route_local' renamed to 'allow' with docs/UI in sync; model-editor footer no longer eats the edit area on small screens; several config-editor template/dropdown/section fixes. Tests: e2e router specs (casual/code-hint + long-conversation trim), vector_store trace specs, lazy-factory specs, gallery dev-alias resolution, Playwright trace badge + scroll regression. Assisted-by: Claude:claude-opus-4-7 [Claude Code] Signed-off-by: Richard Palethorpe <io@richiejp.com> * feat(backend): auto-size batch to context for embedding and rerank models Embedding and rerank models pool over the whole input in a single physical batch (n_ubatch). With batch left at the 512 default, the backend rejects longer inputs with "input is too large to process", silently capping a large-context embedder (e.g. 8k/32k) at 512 tokens. Size n_batch to the context for these single-pass usecases, mirroring the existing FLAG_SCORE behaviour; an explicit batch: still wins. Extracts EffectiveContextSize/EffectiveBatchSize from grpcModelOpts so the effective decode window has one home for other callers to reuse. Adds an e2e-aio regression test that embeds a >512-token input. The AIO embedding model is switched to nomic-embed-text-v1.5 (2048 context) because the previous granite model was capped at 512 tokens and could not exercise the larger batch. Assisted-by: claude-code:claude-opus-4-8 [Claude Code] Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(gallery): raise arch-router scoring output cap via parallel:64 Scoring decodes the whole prompt+candidate in a single llama_decode and reads one logit row per candidate token. The vendored llama.cpp server caps causal output rows at n_parallel, so the default of 1 aborts with GGML_ASSERT(n_outputs_max <= cparams.n_outputs_max) on multi-token route labels. Set options: [parallel:64] on both arch-router quant entries to lift the cap; kv_unified (the grpc-server default) keeps the full context per sequence, so this does not split the KV cache. Assisted-by: claude-code:claude-opus-4-8 [Claude Code] Signed-off-by: Richard Palethorpe <io@richiejp.com> --------- Signed-off-by: Richard Palethorpe <io@richiejp.com> |
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Richard Palethorpe
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fbcd886a47 |
fix(application): stop backend processes synchronously on shutdown (#10058)
application.New wires a fire-and-forget goroutine that runs StopAllGRPC + distributed.Shutdown when the app context is cancelled. Callers (tests, CLI signal handler) cancel the context and then exit immediately, so the test binary / process can terminate before that goroutine kills the spawned backend children. go-processmanager sets no Pdeathsig, so the orphans are reparented to init and survive — leaving dozens of stray mock-backend processes after an e2e run. Add Application.Shutdown(), which runs the same cleanup synchronously on the caller's stack and is idempotent via sync.Once. The context-cancel goroutine, the CLI signal handler, and the test suites all call it, so cleanup is deterministic and the duplicated teardown logic collapses to one place. The async goroutine remains as a safety net for callers that forget; sync.Once dedupes the double call. Wire e2e_suite_test and the two mock-backend Contexts in app_test to call Shutdown in their AfterSuite/AfterEach. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Richard Palethorpe <io@richiejp.com> |
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Richard Palethorpe
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6a80e23733 |
feat(middleware): Model routing, PII filtering, Cloud model proxies (#9802)
Add a routing middleware stack and a cloud-proxy backend. * cloud-proxy: a Go gRPC backend that forwards OpenAI- and Anthropic-shaped chat requests to upstream providers, with an optional translate mode (OpenAI request -> Anthropic /v1/messages -> OpenAI response) and full tool-calling support. * routing: admission control, content-aware model routing (embedding cache + classifier + rerank + Arch-Router score), PII detection/redaction (regex + NER) with streaming filter and OpenAI/Anthropic adapters, and a per-user/per-key billing recorder backed by GORM or in-memory storage. * middleware: UsageMiddleware records usage via the billing recorder, plus admission, route-model, usage-stamp and trace middlewares. * observability: BackendTrace ring buffer stores full request bodies (capped), MITM proxy emits structured trace events, and router classifier decisions surface at /api/router/decide. * gallery: Arch-Router-1.5B (Q4_K_M and Q8_0). * UI: cloud-proxy model-editor fields, classifier system-prompt and score-normalization config, and a Traces page rendering request bodies. Assisted-by: claude-code:claude-opus-4-7 [Read] [Edit] [Bash] Signed-off-by: Richard Palethorpe <io@richiejp.com> |
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LocalAI [bot]
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70cf8ac546 |
fix(backend): resolve relative draft_model paths against the models dir (#9680)
* fix(backend): resolve relative draft_model paths against the models dir The main model file and mmproj are joined with the configured models directory before reaching the backend, but draft_model was sent verbatim. With a relative draft_model in the YAML config, llama.cpp opens the path from the backend process's CWD and fails with "No such file or directory", forcing users to hard-code an absolute path. Mirror the existing mmproj resolution: if draft_model is relative, join it with modelPath. Absolute paths are passed through unchanged. Adds an e2e regression test against the mock backend that asserts the main model file, mmproj, and draft_model all arrive at the backend resolved to absolute paths. Closes #9675 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-7-1m [Read] [Edit] [Bash] [Write] * fix(backend): always join draft_model with models dir (drop IsAbs shortcut) The previous commit kept absolute draft_model paths intact via an IsAbs check. That left a path-traversal vector open: a user-supplied YAML config could set draft_model to /etc/passwd (or any other host file the backend process can read) and the path would be sent through unchanged. filepath.Join cleans the leading slash from absolute components, so joining unconditionally — the way mmproj already does — keeps the result rooted at the configured models directory regardless of input. Adds a second e2e spec that feeds an absolute draft_model into the mock backend and asserts the path is clamped under modelsPath. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-7-1m [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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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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Richard Palethorpe
|
13734ae9fa |
feat: Add Sherpa ONNX backend for ASR and TTS (#8523)
feat(backend): Add Sherpa ONNX backend and Omnilingual ASR Adds a new Go backend wrapping sherpa-onnx via purego (no cgo). Same approach as opus/stablediffusion-ggml/whisper — a thin C shim (csrc/shim.c + shim.h → libsherpa-shim.so) wraps the bits purego can't reach directly: nested struct config writes, result-struct field reads, and the streaming TTS callback trampoline. The Go side uses opaque uintptr handles and purego.NewCallback for the TTS callback. Supports: - VAD via sherpa-onnx's Silero VAD - Offline ASR: Whisper, Paraformer, SenseVoice, Omnilingual CTC - Online/streaming ASR: zipformer transducer with endpoint detection (AudioTranscriptionStream emits delta events during decode) - Offline TTS: VITS (LJS, etc.) - Streaming TTS: sherpa-onnx's callback API → PCM chunks on a channel, prefixed by a streaming WAV header Gallery entries: omnilingual-0.3b-ctc-q8-sherpa (1600-language offline ASR), streaming-zipformer-en-sherpa (low-latency streaming ASR), silero-vad-sherpa, vits-ljs-sherpa. E2E coverage: tests/e2e-backends for offline + streaming ASR, tests/e2e for the full realtime pipeline (VAD + STT + TTS). Assisted-by: claude-opus-4-7-1M [Claude Code] Signed-off-by: Richard Palethorpe <io@richiejp.com> |
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Ettore Di Giacinto
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13a6ed709c |
fix: thinking models with tools returning empty content (reasoning-only retry loop) (#9290)
When clients like Nextcloud or Home Assistant send requests with tools to thinking models (e.g. Gemma 4 with <|channel>thought tags), the response was empty despite the backend producing valid content. Root cause: the C++ autoparser puts clean content in both the raw Response and ChatDeltas. The Go-side PrependThinkingTokenIfNeeded then prepends the thinking start token to the already-clean content, causing ExtractReasoning to classify the entire response as unclosed reasoning. This made cbRawResult empty, triggering a retry loop that never succeeds. Two fixes: - inference.go: check ChatDeltas for content/tool_calls regardless of whether Response is empty, so skipCallerRetry fires correctly - chat.go: when ChatDeltas have content but no tool calls, use that content directly instead of falling back to the empty cbRawResult |
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Ettore Di Giacinto
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85be4ff03c |
feat(api): add ollama compatibility (#9284)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Ettore Di Giacinto
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773489eeb1 |
fix(chat): do not retry if we had chatdeltas or tooldeltas from backend (#9244)
* fix(chat): do not retry if we had chatdeltas or tooldeltas from backend Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix: use oai compat for llama.cpp Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix: apply to non-streaming path too Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * map also other fields Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Ettore Di Giacinto
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59108fbe32 |
feat: add distributed mode (#9124)
* feat: add distributed mode (experimental) Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix data races, mutexes, transactions Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactorings Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fixups Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix events and tool stream in agent chat Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * use ginkgo Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactoring and consolidation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactoring and consolidation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactoring and consolidation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactoring and consolidation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactoring and consolidation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactoring and consolidation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactoring and consolidation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactoring and consolidation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(cron): compute correctly time boundaries avoiding re-triggering Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * enhancements, refactorings Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * do not flood of healthy checks Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * do not list obvious backends as text backends Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * tests fixups Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactoring and consolidation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Drop redundant healthcheck Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * enhancements, refactorings Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Richard Palethorpe
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f9a850c02a |
feat(realtime): WebRTC support (#8790)
* feat(realtime): WebRTC support Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(tracing): Show full LLM opts and deltas Signed-off-by: Richard Palethorpe <io@richiejp.com> --------- Signed-off-by: Richard Palethorpe <io@richiejp.com> |
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Ettore Di Giacinto
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8818452d85 |
feat(ui): MCP Apps, mcp streaming and client-side support (#8947)
* Revert "fix: Add timeout-based wait for model deletion completion (#8756)"
This reverts commit
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Ettore Di Giacinto
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53276d28e7 |
feat(musicgen): add ace-step and UI interface (#8396)
* 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> |
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Andres
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b6459ddd57 |
feat(api): Add transcribe response format request parameter & adjust STT backends (#8318)
* WIP response format implementation for audio transcriptions (cherry picked from commit e271dd764bbc13846accf3beb8b6522153aa276f) Signed-off-by: Andres Smith <andressmithdev@pm.me> * Rework transcript response_format and add more formats (cherry picked from commit 6a93a8f63e2ee5726bca2980b0c9cf4ef8b7aeb8) Signed-off-by: Andres Smith <andressmithdev@pm.me> * Add test and replace go-openai package with official openai go client (cherry picked from commit f25d1a04e46526429c89db4c739e1e65942ca893) Signed-off-by: Andres Smith <andressmithdev@pm.me> * Fix faster-whisper backend and refactor transcription formatting to also work on CLI Signed-off-by: Andres Smith <andressmithdev@pm.me> (cherry picked from commit 69a93977d5e113eb7172bd85a0f918592d3d2168) Signed-off-by: Andres Smith <andressmithdev@pm.me> --------- Signed-off-by: Andres Smith <andressmithdev@pm.me> Co-authored-by: nanoandrew4 <nanoandrew4@gmail.com> Co-authored-by: Ettore Di Giacinto <mudler@users.noreply.github.com> |
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Ettore Di Giacinto
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4077aaf978 |
chore: re-enable e2e tests, fixups anthropic API tools support (#8296)
* chore(tests): add mock backend e2e tests Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Fixup anthropic tests Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * prepare e2e tests Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Drop repetitive tests Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Drop specific CI workflow Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fixup anthropic issues, move all e2e tests to use mocked backend Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Ettore Di Giacinto
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432513c3ba |
ci: add GPU tests (#1095)
* ci: test GPU Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci: show logs Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Debug * debug Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * split extra/core images Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * split extra/core images Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * consider runner host dir Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |