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1e0baec2a7
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>
62 lines
2.1 KiB
Markdown
62 lines
2.1 KiB
Markdown
+++
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disableToc = false
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title = "Sound Classification"
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weight = 32
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url = "/features/audio-classification/"
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+++
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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*).
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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.
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Because classification is exposed as a regular OpenAI-style endpoint, any HTTP client works - there is no Python dependency on the consumer side.
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## Endpoint
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```
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POST /v1/audio/classification
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Content-Type: multipart/form-data
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```
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| Field | Type | Description |
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|-------|------|-------------|
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| `file` | file (required) | audio file in any format `ffmpeg` accepts |
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| `model` | string (required) | name of the sound-classification-capable model (e.g. `ced-base-f16`) |
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| `top_k` | int | number of top tags to return (0 = backend default) |
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| `threshold` | float | drop tags scoring below this value |
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### Response
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```json
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{
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"model": "ced-base-f16",
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"detections": [
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{"index": 23, "label": "Baby cry, infant cry", "score": 0.87},
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{"index": 22, "label": "Crying, sobbing", "score": 0.41}
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]
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}
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```
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Detections are returned in score-descending order. Scores are per-class probabilities (multi-label, independent), so they do not sum to 1.
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## Example
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First install a classification model from the gallery (the example below uses `ced-base-f16`):
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```bash
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local-ai run ced-base-f16
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```
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```bash
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curl http://localhost:8080/v1/audio/classification \
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-H "Content-Type: multipart/form-data" \
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-F file="@/path/to/clip.wav" \
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-F model="ced-base-f16" \
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-F top_k=10
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```
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## See also
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- [Audio to Text]({{% relref "audio-to-text" %}}) - speech transcription
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- [Speaker Diarization]({{% relref "audio-diarization" %}}) - who spoke when
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