emitSpeech now returns raw PCM (caller base64-encodes) so streamed segments
accumulate correctly. speechStreamer consumes streamed LLM tokens: it strips
reasoning via the streaming ReasoningExtractor, emits a transcript delta per
content fragment, and sentence-pipes content into emitSpeech so each sentence
is synthesized as soon as it's ready. Handler wiring (plain-content turns)
follows.
Assisted-by: Claude:claude-opus-4-8 go vet
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
applyPipelineThinking forces the LLM's ReasoningConfig.DisableReasoning when
pipeline.disable_thinking is set, which gRPCPredictOpts turns into the
enable_thinking=false backend metadata. Applied at newModel construction on
the per-session LLM config copy, so it doesn't leak to other model users and
needs no realtime-specific request plumbing.
Assisted-by: Claude:claude-opus-4-8 go vet
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Add emitTranscription and route commitUtterance through it. With
pipeline.streaming.transcription set it streams each transcript fragment as
a conversation.item.input_audio_transcription.delta via TranscribeStream
then a completed event; otherwise it preserves the single completed-event
unary behaviour. Returns the final transcript for response generation.
Assisted-by: Claude:claude-opus-4-8 go vet
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Replace the inline unary TTS block in the response handler with emitSpeech,
which streams a response.output_audio.delta per backend PCM chunk when
pipeline.streaming.tts is set and otherwise preserves the single-delta unary
behaviour. emitSpeech returns the accumulated base64 audio, stored on the
conversation item as before. Transcript and audio-done events stay in the
handler so later per-segment streaming can reuse emitSpeech.
Assisted-by: Claude:claude-opus-4-8 go vet
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
emitSpeech synthesizes a piece of text and forwards audio to the client,
streaming one output_audio.delta per backend PCM chunk when the pipeline
sets streaming.tts, or one delta for the whole utterance otherwise. WebRTC
gets raw PCM (it resamples internally); WebSocket gets base64 PCM at the
session rate. It emits no transcript/audio-done events so a streamed reply
can be split into multiple spoken segments sharing one response.
Adds fakeModel/fakeTransport test doubles for the realtime Model/Transport
interfaces, driving streaming assertions deterministically.
Assisted-by: Claude:claude-opus-4-8 go vet
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Add TTSStream and TranscribeStream to the realtime Model interface and
implement them on wrappedModel (delegating to backend.ModelTTSStream /
ModelTranscriptionStream) and transcriptOnlyModel. ttsStream adapts the
backend's WAV-framed stream (44-byte header carrying the sample rate, then
PCM) into raw PCM + sample rate for the realtime transports. Handler wiring
that consumes these (flag-gated) follows.
Assisted-by: Claude:claude-opus-4-8 go vet
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
streamSegmenter accumulates streamed LLM tokens and emits complete
sentence/clause segments (terminator+whitespace, or newline) so TTS can
synthesize each segment as it completes instead of waiting for the whole
reply. Pure helper; the streaming handler wiring consumes it next.
Assisted-by: Claude:claude-opus-4-8 go vet
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Add a nested pipeline.streaming.{llm,tts,transcription} block plus
pipeline.disable_thinking, with StreamLLM/StreamTTS/StreamTranscription/
ThinkingDisabled helpers. Pointer-bools so unset keeps the unary path;
existing configs are unaffected. Wiring into the realtime handler follows.
Assisted-by: Claude:claude-opus-4-8 go vet
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat: forward reasoning_effort to the backend so jinja models honor it
reasoning_effort was only mapped to the binary enable_thinking toggle and
otherwise reached Go-side templates — it was never sent to the backend. So
jinja-templated models whose chat template keys on reasoning_effort (gpt-oss
Harmony, LFM2.5) could not be driven by it: LFM2.5 ignores enable_thinking and
kept emitting <think>.
Forward the effective reasoning_effort to the backend as a chat_template_kwarg
(mirroring enable_thinking) in grpc-server.cpp, and put it in PredictOptions
metadata (gRPCPredictOpts). Add a config-level default: ModelConfig.reasoning_effort
and Pipeline.reasoning_effort, resolved by ModelConfig.ApplyReasoningEffort
(request value overrides config default, none->disable / level->enable, an
operator's reasoning.disable wins). request.go now uses that helper.
Assisted-by: Claude:claude-opus-4-8 go test, golangci-lint
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(realtime): set the pipeline LLM's reasoning_effort
Apply Pipeline.ReasoningEffort to the pipeline's LLM config when the realtime
model is built (per-session copy, overrides the LLM's own reasoning_effort),
and surface the resolved effort on the template input so Go-templated models
get it too. jinja models receive it via the backend metadata. This lets a
realtime pipeline disable thinking on models that only honor reasoning_effort
(e.g. LFM2.5), which enable_thinking can't.
Assisted-by: Claude:claude-opus-4-8 go test, golangci-lint
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>
The worker HTTP file-transfer server is authenticated by the registration
token via checkBearerToken, which fails open on an empty token: every
/v1/files, /v1/files-list and /v1/backend-logs request is then served
unauthenticated, granting read/write to the worker's models/staging/data
directories. The fail-open was also silent (the only auth log sat on the
unreachable reject branch), and the worker process never runs
DistributedConfig.Validate(), so the existing frontend warning did not
cover the component that exposes the server.
Mirror the NatsRequireAuth pattern: keep anonymous as the default but make
it loud and opt-in enforceable.
- Log a prominent warning when the file-transfer server starts tokenless.
- Add LOCALAI_REGISTRATION_REQUIRE_AUTH: DistributedConfig.Validate() errors
on an empty token (frontend) and the worker refuses to start (fail-fast,
before registration), so production can fail closed. Also satisfies the
F-003 suggestion to fail Validate() on distributed + empty token.
- Add LOCALAI_DISTRIBUTED_REQUIRE_AUTH umbrella switch implying both
RegistrationRequireAuth and NatsRequireAuth — one production knob locking
down the registration/file-transfer layer and the NATS bus together; the
granular flags remain available as single-layer overrides. Wired into the
frontend, supervisor worker, and agent worker (vLLM worker has neither a
NATS connection nor a file-transfer server, so it is left untouched).
- Document in distributed-mode.md (warning callout + flag tables).
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* fix(distributed): self-heal stale 'model not loaded' routing
In distributed mode the registry can list a model as loaded on a node
while the worker has evicted it (autonomous LRU eviction, an out-of-band
unload, etc.) yet the backend process survives. The router's cached-node
check only verifies the process is alive (probeHealth), so it routes there
and inference fails with "<backend>: model not loaded" — and stays broken
until the controller restarts and rebuilds its registry.
InFlightTrackingClient now reconciles this: when a tracked inference call
returns a model-not-loaded error, it drops the stale replica row
(RemoveNodeModel) so the next request reloads the model on a healthy node
instead of routing back to the evicted one. The original error is returned
unchanged; only the registry is corrected.
Assisted-by: Claude:claude-opus-4-8 go vet
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactor(distributed): typed model-not-loaded error via gRPC status code
Replace the controller-side error-string match with a shared, code-aware
helper. Go error types don't survive the gRPC boundary, so the signal is
carried as a status code (FailedPrecondition):
- pkg/grpc/grpcerrors: ModelNotLoaded(backend) constructor +
IsModelNotLoaded(err) checker (status-code first, message fallback for
backends not yet migrated).
- InFlightTrackingClient.reconcile now uses grpcerrors.IsModelNotLoaded.
- Migrate the Go backends that emit this error (parakeet-cpp, cloud-proxy,
rfdetr-cpp) to the typed constructor.
Acting on a false positive is harmless (the model is just reloaded).
Assisted-by: Claude:claude-opus-4-8 go vet
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>
FLAG_FACE_RECOGNITION and FLAG_SPEAKER_RECOGNITION already existed as
ModelConfigUsecase bitmask flags, and GuessUsecases already gate-checks
both backends by name — but BackendCapabilities had no entries for
either, so the UI could not classify them.
Also missing were the Method* constants for the five proto-defined RPCs
these backends implement (FaceVerify, FaceAnalyze, VoiceVerify,
VoiceEmbed, VoiceAnalyze) and the corresponding Usecase* strings
and UsecaseInfoMap entries needed to wire them into the rest of the
capability system.
Changes:
- Add MethodFaceVerify, MethodFaceAnalyze, MethodVoiceVerify,
MethodVoiceEmbed, MethodVoiceAnalyze GRPCMethod constants
- Add UsecaseFaceRecognition ("face_recognition") and
UsecaseSpeakerRecognition ("speaker_recognition") Usecase constants
- Add UsecaseInfoMap entries for both new usecases, referencing the
existing FLAG_FACE_RECOGNITION and FLAG_SPEAKER_RECOGNITION flags
- Register insightface: Embedding + Detect + FaceVerify + FaceAnalyze
- Register speaker-recognition: VoiceVerify + VoiceEmbed + VoiceAnalyze
Follows up on #10107 which left these two out because they needed new
constants first.
Assisted-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Signed-off-by: Adira Denis Muhando <dennisadira@gmail.com>
Distributed file-staging treated every model path field (ModelFile, etc.)
as a single regular file: it os.Open'd the path and streamed its fd as the
HTTP PUT body. For directory-based models — e.g. qwen3-tts-cpp, whose
weights and tokenizer ggufs live under one directory referenced by
parameters.model — opening the directory succeeds but reading its fd
returns EISDIR, so routing the model to a remote NATS worker failed with
"read /models/<model>: is a directory". Single-file models were unaffected,
so only multi-file pipelines (e.g. the realtime TTS stage) broke.
stageModelFiles now detects a directory path field and stages each
contained file individually (via the new stageDirectory helper), preserving
structure with the existing StagingKeyMapper and rewriting the field to the
remote directory (deriving ModelPath as before). countStageableFiles makes
the progress total count a directory's files so the staging tracker stays
accurate.
Assisted-by: Claude:claude-opus-4-8 go vet
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
The qwen3-tts.cpp backend honored the request `language` field only via exact lowercase two-letter codes in the C++ language_to_id table, silently defaulting to English for anything else (en-US, EN, english, ...).
Add normalizeLanguage() in the Go handler: lowercase + trim, strip the region/locale suffix (en-US, pt_BR, zh-Hans -> en/pt/zh), and resolve common English full names (english -> en). The canonical codes match the existing C++ table, so no C++ change is needed. Covered by a pure-Go Ginkgo spec. Also document the language field and accepted forms under the Qwen3-TTS docs.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
The OpenAI-compatible TTS endpoint accepts an `instructions` field, but it
was silently dropped at the HTTP->gRPC boundary: neither schema.TTSRequest
nor the gRPC TTSRequest proto carried it, so backends could only read such a
value from static YAML options (identical for every request). This blocked
per-line emotion/style and, for Qwen3-TTS VoiceDesign, limited a model config
to a single designed voice.
Plumb a generic per-request instruction string end to end, plus an optional
backend-specific params map:
- proto: add `optional string instructions` and `map<string,string> params`
to TTSRequest.
- schema: add Instructions (maps OpenAI `instructions`) and Params (LocalAI
extension) to schema.TTSRequest.
- core: thread both through ModelTTS/ModelTTSStream via a newTTSRequest helper
that attaches instructions only when non-empty (so backends can fall back to
YAML when unset); forward them from the /v1/audio/speech handler.
- qwen-tts: prefer the per-request instruction over the YAML `instruct` option
(used by both mode detection and generation) and merge per-request params.
- chatterbox: merge per-request params (coerced to float/int/bool) over YAML
options into generate() kwargs.
Fully backward compatible: empty instructions fall back to the YAML option and
backends that don't support style/voice instructions ignore the field.
Closes#10164
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
* feat(distributed): NATS JWT auth, TLS/mTLS options, and e2e coverage
Mint per-node NATS user JWTs at registration when LOCALAI_NATS_ACCOUNT_SEED
is set, and connect workers with scoped credentials from the register response.
Add optional LOCALAI_NATS_TLS_CA/CERT/KEY for private CA and mTLS alongside
tls:// URLs, plus test-e2e-distributed and NatsJWT container e2e specs.
Document JWT setup (nats-auth-setup.sh) and TLS env vars in distributed-mode.
Assisted-by: Grok:grok grok-build
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* fix(distributed): correct NATS JWT scoping and harden client auth
The JWT-auth path added in 46467cc7 had several gaps that fail silently
under LOCALAI_NATS_REQUIRE_AUTH:
- Agent-worker minted JWTs did not allow the subjects the agent worker
actually subscribes to (jobs.mcp-ci.new and nodes.<id>.backend.stop),
so MCP-CI jobs and backend-stop session cleanup were silently dropped.
Scope the agent permission set to those subjects.
- NATS subscription permission violations were swallowed (Subscribe
returned a live-but-dead subscription). Confirm subscriptions with a
server round-trip so a denial surfaces synchronously, and log async
permission errors.
- The backend worker connected anonymously when given a JWT without its
paired seed; reject the unpaired credential instead.
- The documented service-user permissions in nats-auth-setup.sh omitted
prefixcache.>, which the frontend publishes and subscribes; add it.
Also: add a credential-provider hook to the messaging client (consumed by
the follow-up credential-lifecycle change), drop the always-nil error from
NatsMessagingOptions, run go mod tidy (jwt/v2 and nkeys are now direct),
and gofmt the feature's files.
Tests: an agent-JWT e2e spec that connects to the enforcing NATS server
and exercises every subscription the agent worker makes, plus permission
allow-list coverage unit tests.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(distributed): acquire and auto-refresh worker NATS credentials
Workers fetched NATS credentials once at startup, which broke two cases
under JWT auth: a worker that registered while still pending admin
approval never received a minted JWT (it connected unauthenticated and
gave up), and a long-running worker's 24h JWT expired with no way to renew
it.
Introduce workerregistry.NATSCredentialManager, built on idempotent
re-registration (the frontend preserves the node row and mints a fresh JWT
each call):
- Acquire re-registers through admin approval until the node is approved
and credentials are minted (or returns the first success when auth is
not required, preserving anonymous-NATS behavior).
- RefreshLoop re-registers before the JWT expires (~75% of its lifetime),
updating the credentials served to the connection.
- Both are bounded (default 100 attempts / consecutive failures) and
return an error on exhaustion, so an unapprovable or unrenewable worker
exits non-zero and surfaces the problem instead of hanging or drifting
toward an expired credential.
The messaging client gains WithUserJWTProvider, fetching credentials on
each (re)connect so the connection transparently adopts a refreshed JWT
when the server expires the old one. RegisterFull exposes the approval
status and full response; Register delegates to it.
Both the backend worker and the agent worker are wired to this: explicit
env credentials are used as-is, minted credentials are acquired-with-wait
and refreshed, and a permanent refresh failure shuts the worker down so it
restarts and re-acquires.
Tests cover Acquire (wait-through-pending, bounded give-up, context
cancel), RefreshLoop (refresh-before-expiry, bounded failure, no-expiry
exit) and jwtExpiry decoding. Docs updated in distributed-mode.md.
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>
The direct (non-batched) transcription path handed the original upload
path straight to the C library via parakeet_capi_transcribe_path_json.
That loader only understands 16 kHz mono WAV/PCM, so any other format
(MP3, etc.) failed with "parakeet: failed to load audio: <file>".
Only the batched path converted the input (via decodeWavMono16k ->
utils.AudioToWav). Every other audio backend (whisper, crispasr)
converts unconditionally with utils.AudioToWav before handing the file
to its engine; the parakeet-cpp fallback was the lone exception.
Extract a convertToWavMono16k helper (reused by decodeWavMono16k) that
produces a 16 kHz mono WAV in a temp dir, and run the non-batched path
through it before calling the C loader. WAV inputs already in the target
format are passed through without ffmpeg.
Add specs covering the helper (decodable copy + cleanup, and an error on
a missing input) that need neither the model, the C library, nor ffmpeg.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
docs: fix distributed-mode diagram - workers coordinate via NATS, not PostgreSQL
The architecture diagram drew the worker-bound arrows from the PostgreSQL area of the control plane, implying workers connect to PostgreSQL. They do not: PostgreSQL is the frontends shared state, while workers coordinate over NATS (backend.install events) and receive LoadModel over gRPC from a frontend. Re-route the worker arrows to originate from the NATS chip.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
* docs: add 'how LocalAI works' architecture diagram
Add a blueprint-style architecture diagram: clients -> small core (API,
router, WebUI, agents) -> gRPC -> backend processes pulled on demand as
OCI images. Place it on the overview page and replace the stale external
architecture image on the reference page.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* docs: add blueprint diagrams across feature, distributed & getting-started docs
Add 24 architecture/flow/comparison diagrams (PNG + HTML source) under
docs/static/images/diagrams/, wired into their docs pages, from an
impact-vs-effort audit of the docs. Broaden the API surface on the
overview architecture diagram (OpenAI, Anthropic, ElevenLabs, Ollama,
and LocalAI's own API) and move the gRPC boundary label clear of the arrows.
Pages: distributed mode (architecture, scheduling, ds4 layer-split),
distributed inferencing, MLX, realtime, quantization, MCP, agents,
mitm & cloud proxy, middleware, reverse-proxy TLS, VRAM, voice & face
recognition, reranker, function calling, fine-tuning (recipe + jobs),
diarization, audio transform, quickstart, model resolution.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* docs: add composable-core diagram to README hero
Commit the composable-core card (small core + on-demand backend tiles)
alongside the other diagrams and reference it from the README hero via a
repo-relative path, so it renders on GitHub.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* docs: fix composable-core connectors/badge and federated-vs-worker layout
- composable-core: thicken the plug-in connectors so they read clearly, and
widen the SEPARATE IMAGE badge so its text no longer overflows the box.
- federated-vs-worker: shorten the WHOLE/SPLIT REQUEST pills to fit, and
replace the tangled node-to-node activation arrows with a clean fan-out
(request split across all sharded nodes), mirroring the federated panel.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
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>
Reframe the README hero and docs (homepage, overview, FAQ) around the
composable architecture: a small core, with backends built as dedicated
gRPC services around best-in-class engines, shipped as separate OCI
images and pulled on demand. Lead from strength: drop the "36+ backends"
kitchen-sink framing and the "All-in-One Complete AI Stack" / "single
binary that gives you everything" lines that read as a monolith.
- README: small-core differentiator; composable + open/extensible bullets
- _index.md: composable tagline; install only what you use
- overview.md: core vs on-demand backends; gRPC/OCI mechanics as benefits;
bring-your-own model and backend
- faq.md: "Do I need to install all the backends?" and
"Can I bring my own model or backend?"
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
The split_mode: tensor description claimed tensor parallelism requires
KV-cache quantization to be disabled. ggml-org/llama.cpp#23792 lifts that
restriction by extending the meta backend to preserve shape information
through KV-cache flatten/reshape, so cache_type_k/cache_type_v
quantization can be combined with -sm tensor on builds that include it.
Documentation only: no backend code, grpc-server.cpp comment, or
llama.cpp pin changes.
Assisted-by: Claude Code:claude-opus-4-8
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(parakeet-cpp): dynamic-batching scheduler (queue + dispatcher)
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(parakeet-cpp): dynamic batching for AudioTranscription via batched JSON C-API
Drop SingleThread; route unary transcription through the in-process batcher
which coalesces concurrent requests into one batched engine call. Streaming
stays mutually exclusive via engineMu. Adds batch_max_size / batch_max_wait_ms
options (size=1 disables; recommended on CPU).
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(parakeet-cpp): tear down dispatcher in Free; log batch config; preallocate; clarify stream lock
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(parakeet-cpp): Ginkgo batcher tests; optional batch C-API binding with per-request fallback
The batched JSON C-API symbol exists only in newer libparakeet.so (ABI >= 2);
probe it with Dlsym and register optionally so the backend still loads against
an older library, falling back to per-request transcription. Rewrites the
batcher unit tests as Ginkgo/Gomega specs (forbidigo bans t.Fatal in tests).
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(parakeet-cpp): debug-log coalesced batch size in runBatch
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(parakeet-cpp): default batch_max_size to 1 (batching opt-in)
Dynamic batching now defaults off (batch_max_size:1, one request at a
time). Raise batch_max_size to opt in: it is a large throughput win on
GPU under concurrent load, but on CPU and low-concurrency setups it only
adds latency, so off is the safer default. The startup log now states
whether batching is on or off, and the audio-to-text docs are updated to
match.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* chore(parakeet-cpp): bump parakeet.cpp to 8a7c482 (batched decode + B=1 fast-path)
parakeet.cpp PR #1 merged the batched encoder/decode and the B=1 encoder
fast-path to master. Point PARAKEET_VERSION at that commit so the backend
builds the batched C-API (parakeet_capi_transcribe_pcm_batch_json) that the
dynamic batcher calls; the prior pin (30a3075) predated it, so only the
per-request fallback path was exercised. Verified the shared lib builds with
the backend's CMake flags and exports the batch symbol.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
Pin texterrors==1.1.6 before nemo_toolkit[asr] in requirements-cublas13.txt.
The texterrors package (a NeMo transitive dependency) contains a compiled
C++ extension (texterrors_align.so) that may be built from source during
OCI image creation. When built on systems with GCC 14+ (e.g. Ubuntu 24.04),
the resulting binary requires GLIBCXX_3.4.32, which is not available in
the default LocalAI container (Ubuntu 22.04, GLIBCXX up to 3.4.30).
Pinning to 1.1.6 (the latest release) ensures:
- Reproducible builds across environments
- pip resolves the pre-built manylinux2014 wheel (needs only GLIBCXX_3.4.11)
instead of potentially building from source with a newer toolchain
Fixes#10056
Signed-off-by: 番茄摔成番茄酱 <fqscfqj@outlook.com>
