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.
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).
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mudler ced-gguf online free url in huggingface.co:
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.