mudler-agentandEttore Di Giacinto
3185b6fcf8
* feat(parakeet-cpp): load bundle GGUF files and use their components by role A bundle GGUF holds several models (ASR, VAD, diarization, sound events, speaker encoder) in one file, each with its own licence. Detect a bundle at load through parakeet_capi_bundle_components_json and open components with parakeet_capi_load_component. The three symbols are probed together, so an older libparakeet.so still loads plain files as before. The only ASR component is the primary model; bundle_asr:<name> picks one when there are several. A Silero VAD component of the primary bundle is loaded without an option and serves /v1/vad and vad:true. The diar, ced and voice components load on request: diar_component, sound_component and speaker_component, or a companion option (diarization_model, sound_model, speaker_model, vad_model) that names a bundle, even the model file itself. vad_component picks a VAD component and implies vad:true. A role the bundle cannot fill fails the load with the component list, and a diarization or sound request on a model without that role names the bundle components. Every existing option and single-file model behaves as before. Assisted-by: Claude:claude-sonnet-5-5 [Claude Code] * chore(parakeet-cpp): bump parakeet.cpp to 781a973 Brings in the bundle GGUF format and its C-API (parakeet_capi_load_component, parakeet_capi_bundle_components_json, parakeet_capi_load_error). Assisted-by: Claude:claude-sonnet-5-5 [Claude Code] * feat(parakeet-cpp): gallery entries for the bundle GGUF files, docs Add parakeet-cpp-bundle-small (338 MB: Parakeet TDT+CTC 110M, Nemotron-3- Diarization, CED-Small, WeSpeaker ResNet34-LM, Silero VAD), -standard (1.1 GB, Parakeet TDT 0.6B v3 instead of the 110M model) and -moondream-redux (215 MB: packed Redux and Silero VAD, CPU only). One install serves transcription, VAD, diarization, sound events and speaker naming through the component options. The existing single-purpose entries stay. A bundle has no single licence, so the entries use license: other and state the licence and credit of each component in the description, with the upstream inconsistency of the CED licence. The docs get a section on bundles in audio-to-text with the entries, the roles, the options and the licence notice, and pointers from the VAD, diarization and sound classification pages. A gallery test checks the file names, checksums, usecases and options. Assisted-by: Claude:claude-sonnet-5-5 [Claude Code] --------- Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
3.7 KiB
+++ disableToc = false title = "Sound Classification" weight = 32 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 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 (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.
parakeet.cpp can also load a CED model (through third_party/ced.cpp) and serve /v1/audio/classification from the same backend used for ASR and diarization. It scores the clip in 10 s windows and averages each class's score across the windows before sorting and applying top_k/threshold - CED's own method for clips longer than one window. Install parakeet-cpp-ced-tiny or parakeet-cpp-ced-base from the gallery, or point parameters.model at a CED GGUF under backend: parakeet-cpp. A parakeet-cpp ASR model can also point sound_model at a CED GGUF to add live sound events during realtime transcription - see [Realtime API]({{% relref "openai-realtime" %}}).
Because classification is exposed as a regular OpenAI-style endpoint, any HTTP client works - there is no Python dependency on the consumer side.
In distributed mode, LocalAI stages uploaded audio and realtime sound-detection windows on the selected worker before classification. The API server and worker do not need a shared temporary directory.
Endpoint
POST /v1/audio/classification
Content-Type: multipart/form-data
| Field | Type | Description |
|---|---|---|
file |
file (required) | audio file in any format ffmpeg accepts |
model |
string (required) | name of the sound-classification-capable model (e.g. ced-base-f16) |
top_k |
int | number of top tags to return (0 = backend default) |
threshold |
float | drop tags scoring below this value |
Response
{
"model": "ced-base-f16",
"detections": [
{"index": 23, "label": "Baby cry, infant cry", "score": 0.87},
{"index": 22, "label": "Crying, sobbing", "score": 0.41}
]
}
Detections are returned in score-descending order. Scores are per-class probabilities (multi-label, independent), so they do not sum to 1.
Example
First install a classification model from the gallery (the example below uses ced-base-f16):
local-ai run ced-base-f16
curl http://localhost:8080/v1/audio/classification \
-H "Content-Type: multipart/form-data" \
-F file="@/path/to/clip.wav" \
-F model="ced-base-f16" \
-F top_k=10
The same request works unchanged against a parakeet-cpp CED model:
name: parakeet-ced-tiny
backend: parakeet-cpp
parameters:
model: ced-tiny-q8_0.gguf
known_usecases:
- sound_classification
curl http://localhost:8080/v1/audio/classification \
-H "Content-Type: multipart/form-data" \
-F file="@/path/to/clip.wav" \
-F model="parakeet-ced-tiny" \
-F top_k=10
The bundle entries parakeet-cpp-bundle-small and parakeet-cpp-bundle-standard also serve this endpoint: they hold CED-Small next to the transcription, VAD and diarization models (sound_component:ced). See [Bundle GGUF files]({{% relref "audio-to-text" %}}#bundle-gguf-files-several-models-in-one-file).
See also
- [Audio to Text]({{% relref "audio-to-text" %}}) - speech transcription
- [Speaker Diarization]({{% relref "audio-diarization" %}}) - who spoke when