asl denoising.published.1

提供商ilex-hub
分类speech-enhancement
许可证gpl-3.0
下载量8
星标0

简介

asl denoising.published.1 是一款专注于语音增强（Speech Enhancement）的轻量化模型，旨在将嘈杂环境中的人声从背景噪音中精准分离。对于开发者而言，它非常适合集成到实时语音通话、播客后期处理或智能家居语音交互场景中。该模型上手难度较低，能够有效解决录音环境嘈杂导致的识别率下降问题，可作为 ASR（自动语音识别）流水线的前置预处理模块，显著提升后续语音转文字的准确度。

核心亮点

  • 高效去除背景杂音，还原清晰人声
  • 适配 ASR 前置处理，提升识别准确率
  • 适用于实时语音通信与音频后期增强
  • 采用 GPL-3.0 协议，开源且灵活部署

使用方法

安装依赖
# 安装 Hugging Face transformers
pip install transformers torch
SDK 使用
# 使用 transformers 加载模型
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 下载

我们推荐使用命令行或者 Hugging Face Hub SDK 来进行模型的下载。

操作指引：在下载前，请先通过如下命令安装 huggingface_hub：

操作指引 
pip install -U huggingface_hub

命令行下载

下载完整模型库

下载完整模型库 
huggingface-cli download ilex-hub/asl_denoising.published.1

下载单个文件到指定本地文件夹（以下载 config.json 到当前路径下 ./dir 目录为例）

下载单个文件到指定本地文件夹（以下载 config.json 到当前路径下 ./dir 目录为例） 
huggingface-cli download ilex-hub/asl_denoising.published.1 config.json --local-dir ./dir

更多命令行下载选项，可参见官方文档

SDK 下载

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

Git 下载

请确保 lfs 已经被正确安装

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

如果您希望跳过 lfs 大文件下载，可以使用如下命令

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

模型文件托管在 Hugging Face Hub，使用 HF CLI / SDK / Git 直接下载，不经过本站。

PyTorch / Transformers 使用

安装 Transformers

安装 Transformers 
pip install -U transformers torch

模型加载和推理

模型加载和推理 
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')

完整文档

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 in
ilex.models.asl_denoising.AslDenoisingDAE` and the weights have been converted
from their upstream format. See the upstream source above
for the canonical reference.

查看官方来源