fix(ci): repair nightly backend dep bumps for renamed localai-org repos (#11012)

The "Bump Backend dependencies" workflow has failed every night for over
ten days. Four upstreams — ced.cpp, moss-transcribe.cpp, voice-detect.cpp
and rf-detr.cpp — moved from the mudler org to localai-org, so the GitHub
API answers 301 for the old slugs. ced.cpp additionally renamed its
default branch to main.

bump_deps.sh fetched without -L or -f and never checked the response, so
the redirect's JSON body was passed straight to sed, which died with
"unterminated `s' command". The loud failure was luck: an error body
without slashes would have been substituted into the Makefile as the new
pin, silently corrupting the version and shipping it in a bump PR.

Point the matrix at the new slugs and branch, and harden the script so a
bad response can never reach sed: follow redirects, fail on HTTP errors,
and require a bare 40-hex SHA before rewriting anything. Also refresh the
now-stale repository URLs in the backend Makefiles, test scripts,
backend/index.yaml and the docs.

Verified all 25 matrix entries resolve to a commit SHA and that the four
previously-failing jobs run end to end against the real API.

Assisted-by: Claude:claude-opus-4-8 [Claude Code]

Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com>

docs/content/features/audio-classification.md

@@ -7,7 +7,7 @@ url = "/features/audio-classification/"
 
 Sound-event classification (audio tagging) answers the question **"what am I hearing?"** - given an audio clip, it returns a list of scored [AudioSet](https://research.google.com/audioset/) labels (e.g. *Baby cry, infant cry*, *Glass breaking*, *Dog bark*, *Alarm*).
 
-LocalAI exposes this through the `/v1/audio/classification` endpoint, modelled after `/v1/audio/transcriptions`. The reference backend is **[ced.cpp](https://github.com/mudler/ced.cpp)** (CED, a 527-class AudioSet tagger), a small ViT over a log-mel spectrogram ported to ggml with full PyTorch parity. Apache-2.0 weights are redistributable as GGUF.
+LocalAI exposes this through the `/v1/audio/classification` endpoint, modelled after `/v1/audio/transcriptions`. The reference backend is **[ced.cpp](https://github.com/localai-org/ced.cpp)** (CED, a 527-class AudioSet tagger), a small ViT over a log-mel spectrogram ported to ggml with full PyTorch parity. Apache-2.0 weights are redistributable as GGUF.
 
 Because classification is exposed as a regular OpenAI-style endpoint, any HTTP client works - there is no Python dependency on the consumer side.
 

docs/content/features/object-detection.md

@@ -5,7 +5,7 @@ weight = 41
 url = "/features/object-detection/"
 +++
 
-LocalAI supports object detection and image segmentation through various backends. This feature allows you to identify and locate objects within images with high accuracy and real-time performance. Available backends include [RF-DETR](https://github.com/roboflow/rf-detr) (Python) and [rf-detr.cpp](https://github.com/mudler/rf-detr.cpp) (native C++/ggml) for object detection and segmentation, and [sam3.cpp](https://github.com/PABannier/sam3.cpp) for image segmentation (SAM 3/2/EdgeTAM).
+LocalAI supports object detection and image segmentation through various backends. This feature allows you to identify and locate objects within images with high accuracy and real-time performance. Available backends include [RF-DETR](https://github.com/roboflow/rf-detr) (Python) and [rf-detr.cpp](https://github.com/localai-org/rf-detr.cpp) (native C++/ggml) for object detection and segmentation, and [sam3.cpp](https://github.com/PABannier/sam3.cpp) for image segmentation (SAM 3/2/EdgeTAM).
 
 For detecting **faces** specifically, see the dedicated
 [Face Recognition](/features/face-recognition/) feature - its
@@ -138,7 +138,7 @@ You can browse and install this model through the LocalAI web interface or using
 ### RF-DETR Native Backend (rfdetr-cpp)
 
 The `rfdetr-cpp` backend is a native C++/ggml implementation of RF-DETR
-inference based on [rf-detr.cpp](https://github.com/mudler/rf-detr.cpp). It
+inference based on [rf-detr.cpp](https://github.com/localai-org/rf-detr.cpp). It
 runs as a Go gRPC service that dlopens a per-CPU-variant shared library, so
 there is no Python runtime on the inference path - startup is fast and the
 binary is self-contained.

docs/content/features/voice-recognition.md

@@ -16,7 +16,7 @@ The audio analog to [Face Recognition](/features/face-recognition/),
 served over the same `/v1/voice/*` HTTP API by two backends:
 
 - **`voice-detect` (recommended, default).** A standalone C++/ggml
-  engine ([voice-detect.cpp](https://github.com/mudler/voice-detect.cpp)):
+  engine ([voice-detect.cpp](https://github.com/localai-org/voice-detect.cpp)):
   no Python, no onnxruntime, no torch runtime. Each gallery entry is a
   single self-describing GGUF. This is the recommended option for new
   deployments.

docs/content/reference/compatibility-table.md

@@ -94,12 +94,12 @@ All backends listed here can be installed on demand from the [Backend Gallery]({
 | Backend | Description | Acceleration |
 |---------|-------------|-------------|
 | [RF-DETR](https://github.com/roboflow/rf-detr) | Real-time transformer-based object detection (Python) | CPU, CUDA 12/13, Intel SYCL, Metal, Jetson L4T |
-| [rf-detr.cpp](https://github.com/mudler/rf-detr.cpp) | Native RF-DETR object detection and instance segmentation in C/C++ using GGML | CPU, CUDA 12/13, Intel SYCL, Vulkan, Jetson L4T |
+| [rf-detr.cpp](https://github.com/localai-org/rf-detr.cpp) | Native RF-DETR object detection and instance segmentation in C/C++ using GGML | CPU, CUDA 12/13, Intel SYCL, Vulkan, Jetson L4T |
 | [locate-anything.cpp](https://github.com/mudler/locate-anything.cpp) | Open-vocabulary object detection and visual grounding (LocateAnything-3B) in C/C++ using GGML | CPU, CUDA 12/13, Intel SYCL, Vulkan, Jetson L4T |
 | [depth-anything.cpp](https://github.com/mudler/depth-anything.cpp) | Depth Anything 3 monocular metric depth + camera pose in C/C++ using GGML | CPU, CUDA 12/13, Intel SYCL, Vulkan, Jetson L4T |
 | [sam3.cpp](https://github.com/PABannier/sam3.cpp) | Segment Anything (SAM 3/2/EdgeTAM) with text/point/box prompts in C/C++ using GGML | CPU, CUDA 12/13, Intel SYCL, Vulkan, Jetson L4T |
 | [face-detect.cpp](https://github.com/mudler/face-detect.cpp) | Native face detection, recognition, embedding, demographics and anti-spoofing (SCRFD/ArcFace, YuNet/SFace) in C/C++ using GGML | CPU, CUDA 12/13, ROCm, Intel SYCL, Vulkan, Metal, Jetson L4T |
-| [voice-detect.cpp](https://github.com/mudler/voice-detect.cpp) | Native speaker (voice) recognition and voice analysis (ECAPA-TDNN, WeSpeaker, ERes2Net, CAM++, wav2vec2) in C/C++ using GGML | CPU, CUDA 12/13, ROCm, Intel SYCL, Vulkan, Metal, Jetson L4T |
+| [voice-detect.cpp](https://github.com/localai-org/voice-detect.cpp) | Native speaker (voice) recognition and voice analysis (ECAPA-TDNN, WeSpeaker, ERes2Net, CAM++, wav2vec2) in C/C++ using GGML | CPU, CUDA 12/13, ROCm, Intel SYCL, Vulkan, Metal, Jetson L4T |
 | [insightface](https://github.com/deepinsight/insightface) | Face verification, embedding, and anti-spoofing liveness (ONNX Runtime) | CPU, CUDA 12 |
 | [speaker-recognition](https://speechbrain.github.io/) | Speaker (voice) recognition via SpeechBrain ECAPA-TDNN | CPU, CUDA 12, Metal |
 

docs/content/whats-new.md

@@ -16,7 +16,7 @@ You can see the release notes [here](https://github.com/mudler/LocalAI/releases)
 - **April 2026**: [Audio Transform](/features/audio-transform/) - generic audio-in / audio-out endpoint with optional reference signal. First implementation: [LocalVQE](https://github.com/localai-org/LocalVQE) C++ backend (joint AEC + noise suppression + dereverberation, DeepVQE-style). Both batch (`POST /audio/transformations`) and bidirectional WebSocket streaming (`/audio/transformations/stream`). Studio "Transform" tab with synchronized waveform players for input / reference / output.
 - **April 2026**: [Face recognition backend](/features/face-recognition/) - `insightface`-powered 1:1 verification, 1:N identification, face embedding, face detection, and demographic analysis. Ships both a non-commercial `buffalo_l` model and an Apache 2.0 OpenCV Zoo alternative.
 - **May 2026**: [Speaker diarization](/features/audio-diarization/) - new `/v1/audio/diarization` endpoint returning "who spoke when" segments. Backed by `sherpa-onnx` (pyannote-3.0 + speaker embeddings + clustering) for pure diarization, and `vibevoice-cpp` for diarization bundled with long-form ASR. Supports `json` / `verbose_json` / `rttm` response formats.
-- **June 2026**: [Sound classification](/features/audio-classification/) - new `/v1/audio/classification` endpoint for audio tagging / sound-event classification, returning scored [AudioSet](https://research.google.com/audioset/) labels (baby cry, glass breaking, alarms, ...). Backed by [ced.cpp](https://github.com/mudler/ced.cpp), a 527-class AudioSet tagger ported to ggml.
+- **June 2026**: [Sound classification](/features/audio-classification/) - new `/v1/audio/classification` endpoint for audio tagging / sound-event classification, returning scored [AudioSet](https://research.google.com/audioset/) labels (baby cry, glass breaking, alarms, ...). Backed by [ced.cpp](https://github.com/localai-org/ced.cpp), a 527-class AudioSet tagger ported to ggml.
 - **June 2026**: [PII analyze / redact API](/features/middleware/#analyze--redact-api) - the PII detection pipeline (NER + restricted-regex pattern tiers) is now a standalone service: `POST /api/pii/analyze` returns detected entity spans and `POST /api/pii/redact` returns the sanitised text (or `400 pii_blocked`), without routing a chat request through the middleware. Events gain an `origin` (`middleware` / `proxy` / `pii_analyze` / `pii_redact`) so `/api/pii/events` can be filtered by source.
 - **July 2026**: [Model capabilities endpoint](/features/api-discovery/#model-capabilities) - `GET /v1/models/capabilities`, an additive superset of `/v1/models` that reports each model's `capabilities` plus its `input_modalities` / `output_modalities` (`text` / `image` / `audio` / `video`). Lets clients route attachments using inferred or explicitly declared model modalities instead of backend-name checks.
 - **June 2026**: Concurrent scoring and PII NER on llama.cpp - the `Score` (router classifier) and `TokenClassify` (PII NER) primitives now ride llama.cpp's server task queue instead of locking the context, so they run concurrently with chat/completion/embedding traffic and with each other. The `known_usecases` restriction that forced dedicated scorer/NER model configs on llama-cpp is lifted, repeated scoring calls reuse the prompt KV cache across candidates, and scoring inputs are no longer capped by the physical batch size.