Overview

The asl denoising.published.1 model is a specialized speech-enhancement tool designed to isolate clean voice signals from noisy environments. For developers building VoIP, transcription, or accessibility apps, this model provides a programmatic way to reduce background interference before passing audio to downstream ASR (Automatic Speech Recognition) engines. Unlike general-purpose audio filters, it focuses on preserving speech intelligibility while suppressing non-stationary noise. It is released under the GPL-3.0 license, making it suitable for open-source integration, and is optimized for deployment in pipelines where audio quality directly impacts the accuracy of NLP tasks.

Highlights

Reduces background noise to improve ASR accuracy
Optimized for real-time speech enhancement pipelines
Open-source integration via GPL-3.0 licensing
Preserves voice clarity for downstream NLP tasks

Usage

Install

# Install Hugging Face transformers
pip install transformers torch

SDK Usage

# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("ilex-hub/asl_denoising.published.1")
tokenizer = AutoTokenizer.from_pretrained("ilex-hub/asl_denoising.published.1")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance

pip install -U huggingface_hub

CLI Download

Download the full repository

huggingface-cli download ilex-hub/asl_denoising.published.1

Download a single file to a local folder (e.g. config.json into ./dir)

huggingface-cli download ilex-hub/asl_denoising.published.1 config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('ilex-hub/asl_denoising.published.1')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://huggingface.co/ilex-hub/asl_denoising.published.1

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/ilex-hub/asl_denoising.published.1

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

pip install -U transformers torch

Load the model and run inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('ilex-hub/asl_denoising.published.1')
tokenizer = AutoTokenizer.from_pretrained('ilex-hub/asl_denoising.published.1')

Full Documentation

来源: HuggingFace

---
library_name: ilex
tags:

jax

equinox

ilex

neuroimaging

simultaneous

license: gpl-3.0
license_link: https://www.gnu.org/licenses/gpl-3.0.en.html
---

ASL Denoising Autoencoder (Hales et al. -- ASL difference image denoising) -- ASL DAE published checkpoint

Description

patrickhales/asl-denoising (Hales et al., *JMRI* 2020) is a
5-Conv2D U-Net-shaped denoising autoencoder for arterial spin
labelling (ASL) MRI difference images (dM). The model takes
single-repetition raw dM images (high-noise, single label-
control subtraction) and emits a denoised dM image
approximating the multi-repetition mean (low-noise, averaged
over typically 10 repetitions). The model also suppresses
transient artefacts from head motion, arterial-transit
variation, and spurious perfusion signals that survive a
single-rep subtraction.

v0 ships one variant (the upstream's released
`DaeTrainedModel.h5; trained on 28,820 dM images over 67 early-stopped epochs of a planned 100). The architecture is parameter-fixed at 149,441 trainable scalars.


Intended use
ASL difference-image denoising + transient-artefact suppression. Input: (1, 128, 128) z-normalised dM slice (the consumer applies the preprocessing constants from the bundle's _ilex.preprocessing block). Output: same shape, also z-normalised; the consumer un-normalises by output * std + mean. The model is shape-invariant for any (H, W) divisible by 4; the upstream's pipeline always resizes inputs to (128, 128) first.
Usage
python
from ilex.models.asl_denoising import AslDenoisingDAE
model = AslDenoisingDAE.from_pretrained('ilex-hub/asl_denoising.published.1')
Authors
Hales P. W. (Great Ormond Street Hospital / UCL Institute of Child Health)
Citation
Hales P. W., Pfeuffer J., Clark C. A. (2020). Combined denoising and suppression of transient artefacts in arterial spin labelling MRI using deep learning. Journal of Magnetic Resonance Imaging. doi:10.1002/jmri.27255.
References
Hales P. W., Pfeuffer J., Clark C. A. (2020). Combined denoising and suppression of transient artefacts in arterial spin labelling MRI using deep learning. *Journal of Magnetic Resonance Imaging*. doi 10.1002/jmri.27255.
Upstream code + weights -- github.com/patrickhales/asl-denoising (GPL-3.0).
License

HF Hub license tag: gpl-3.0


Effective terms: GPL-3.0-only. The upstream code + released Keras .h5 weights are licensed under GPL-3.0 at github.com/patrickhales/asl-denoising. The ilex JAX / Equinox port code is licensed under GPL-3.0 to preserve the upstream's terms; this port is NOT available under the Apache-2.0 track that covers ilex's permissive- licensed ports. Redistribution requires the GPL-3.0 source-code-availability obligation.
Upstream license reference: https://www.gnu.org/licenses/gpl-3.0.en.html
Copyright
patrickhales/asl-denoising is copyright (c) Patrick Hales,
GPL-3.0-licensed on the code + the released Keras .h5
weights. The ilex JAX / Equinox port code is licensed under
GPL-3.0 to preserve the upstream's license; this port is NOT
available under the Apache-2.0 track that covers ilex's
permissive-licensed ports.
Upstream source
Original weights / reference implementation: https://github.com/patrickhales/asl-denoising
Provenance

This artefact was produced by ilex's save/load pipeline. The architecture is implemented inilex.models.asl_denoising.AslDenoisingDAE` and the weights have been converted
from their upstream format. See the upstream source above
for the canonical reference.