mudler / ced-gguf

huggingface.co
Total runs: 1.1M
24-hour runs: 58.5K
7-day runs: 372.6K
30-day runs: 1.0M
Model's Last Updated: June 22 2026
audio-classification

Introduction of ced-gguf

Model Details of ced-gguf

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.

Runs of mudler ced-gguf on huggingface.co

1.1M
Total runs
58.5K
24-hour runs
164.8K
3-day runs
372.6K
7-day runs
1.0M
30-day runs

More Information About ced-gguf huggingface.co Model

More ced-gguf license Visit here:

https://choosealicense.com/licenses/apache-2.0

ced-gguf huggingface.co

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https://huggingface.co/mudler/ced-gguf

ced-gguf install

ced-gguf is an open source model from GitHub that offers a free installation service, and any user can find ced-gguf on GitHub to install. At the same time, huggingface.co provides the effect of ced-gguf install, users can directly use ced-gguf installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

ced-gguf install url in huggingface.co:

https://huggingface.co/mudler/ced-gguf

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