Ced GGUF
GGUF quantizations of the CED family (Consistent Ensemble Distillation, Xiaomi) - SOTA-tier audio-tagging models that classify everyday sounds (baby cry, footsteps, glass breaking, alarms, dog bark, ...) into the...
Modellquelle
- Art
- Other
- Dateigröße
- 5,92–327 MB
- Basismodell
- mispeech/ced-base
- Version
- vCed base f16
- Aktualisiert
- 22. Okt. 2026
- Quelle
- Hugging Face
Quellenbeschreibung
CED (GGUF) for ced.cpp / LocalAI
GGUF quantizations of the CED family (Consistent Ensemble Distillation, Xiaomi) - SOTA-tier audio-tagging models that classify everyday sounds (baby cry, footsteps, glass breaking, alarms, dog bark, ...) into the 527-class AudioSet ontology.
These files run with ced.cpp, a
standalone C++/ggml port (no Python, no
PyTorch at inference), and with LocalAI
via the ced backend. Converted from the mispeech/ced-* checkpoints
(Apache-2.0). CED is a plain AST/DeiT Vision Transformer over a log-mel
spectrogram; the port is numerically equal to the PyTorch reference.
Files
One self-contained GGUF per size + quant (config, 527 labels, and the mel filterbank/window are all embedded). Pick by your accuracy/size budget:
| size | params | f16 | q8_0 | f32 |
|---|---|---|---|---|
| tiny | 5.5M | ced-tiny-f16.gguf (11 MB) | ced-tiny-q8_0.gguf (6 MB) | - |
| mini | 9.6M | ced-mini-f16.gguf (19 MB) | ced-mini-q8_0.gguf (11 MB) | - |
| small | 22M | ced-small-f16.gguf (42 MB) | ced-small-q8_0.gguf (23 MB) | - |
| base | 86M | ced-base-f16.gguf (165 MB) | ced-base-q8_0.gguf (88 MB) | ced-base-f32.gguf (328 MB) |
tiny/q8_0 (6 MB) is ideal for Raspberry-Pi-class CPUs; base/f16 is the
accuracy default.
Parity vs PyTorch (ced-base, end-to-end probs)
| quant | max abs diff | top-5 tags |
|---|---|---|
| f32 | 1.7e-7 | identical |
| f16 | 6.4e-5 | identical |
| q8_0 | 6.0e-3 | identical |
Performance (CPU, ced-base, 10s clip, Ryzen 9 9950X3D, 4 threads)
| latency | realtime factor | peak RSS | |
|---|---|---|---|
| PyTorch (transformers, f32) | 155.7 ms | 65x | 717 MB |
| ced.cpp f16 | 100.6 ms | 100x | 189 MB |
| ced.cpp q8_0 | 117.2 ms | 86x | 111 MB |
ced.cpp f16 is ~1.55x faster than the PyTorch reference; q8_0 uses ~6.5x less memory.
Usage
ced-cli classify ced-base-f16.gguf clip.wav --top-k 5
# 0.87 Baby cry, infant cry
# 0.12 Crying, sobbing
In LocalAI: install the ced backend, configure a model with one of these
GGUFs, then call POST /v1/audio/classification (or stream over the realtime
websocket API for live recognition).
License
Model weights: Apache-2.0 (© Xiaomi Corporation; from the mispeech/ced-*
checkpoints). AudioSet labels are CC-BY-4.0. The ced.cpp inference code is MIT.
Quellen
1 Quelle
Quellen
1 QuelleVerifiziert 7. Aug.
Modellartefakte
9 Artefakte
Modellartefakte
9 ArtefakteQuellenauszüge
2 Auszüge
Quellenauszüge
2 Auszüge--- license: apache-2.0 library_name: ced.cpp pipeline_tag: audio-classification tags: - audio-classification - sound-event-detection - audio-tagging - audioset - ggml - gguf - ced base_model: - mispeech/ced-tiny - mispeech/ced-mini - mispeech/ced-small - mispeech/ced-base --- # CED (GGUF) for ced.cpp / LocalAI GGUF quantizations of the **CED** family (Consistent Ensemble Distillation, Xiaomi) - SOTA-tier audio-tagging models that classify everyday sounds (baby cry, footsteps, glass breaking, alarms, dog bark, ...) into the 527-class [AudioSet](https://research.google.com/audioset/) ontology. These files run with [**ced.cpp**](https://github.com/mudler/ced.cpp), a standalone C++/[ggml](https://github.com/ggml-org/ggml) port (no Python, no PyTorch at inference), and with [**LocalAI**](https://github.com/mudler/LocalAI) via the `ced` backend. Converted from the `mispeech/ced-*` checkpoints (Apache-2.0). CED is a plain AST/DeiT Vision Transformer over a log-mel spectrogram; the port is numerically equal to the PyTorch reference. ## Files One self-contained GGUF per size + quant (config, 527 labels, and the mel filterbank/window are all embedded). Pick by your accuracy/size budget: | size | params | f16 | q8_0 | f32 | |------|--------|-----|------|-----| | **tiny** | 5.5M | `ced-tiny-f16.gguf` (11 MB) | `ced-tiny-q8_0.gguf` (6 MB) | - | | **mini** | 9.6M | `ced-mini-f16.gguf` (19 MB) | `ced-mini-q8_0.gguf` (11 MB) | - | | **small** | 22M | `ced-small-f16.gguf` (42 MB) | `ced-small-q8_0.gguf` (23 MB)| - | | **base** | 86M | `ced-base-f16.gguf` (165 MB) | `ced-base-q8_0.gguf` (88 MB) | `ced-base-f32.gguf` (328 MB) | `tiny`/`q8_0` (6 MB) is ideal for Raspberry-Pi-class CPUs; `base`/`f16` is the accuracy default. ## Parity vs PyTorch (ced-base, end-to-end probs) | quant | max abs diff | top-5 tags | |-------|--------------|------------| | f32 | 1.7e-7 | identical | | f16 | 6.4e-5 | identical | | q8_0 | 6.0e-3 | identical | ## Performance (CPU, ced-base, 10s clip, Ryzen 9 9950X3D, 4 threads) | | latency | realtime factor | peak RSS | |---|---|---|---| | PyTorch (transformers, f32) | 155.7 ms | 65x | 717 MB | | ced.cpp f16 | 100.6 ms | 100x | 189 MB | | ced.cpp q8_0 | 117.2 ms | 86x | 111 MB | ced.cpp f16 is ~1.55x faster than the PyTorch reference; q8_0 uses ~6.5x less memory. ## Usage ```sh ced-cli classify ced-base-f16.gguf clip.wav --to...
Source context: 33354 downloads · 2 likes · Pipeline audio-classification · Library ced.cpp · Repo mudler/ced-gguf
