sepformer dns4 16k enhancement

提供商speechbrain
分类speech-enhancement
许可证apache-2.0
下载量563
星标0

简介

SepFormer DNS4 16k 是一款由 SpeechBrain 提供的专业语音增强模型，专门用于从嘈杂的音频中分离并提取纯净的人声。它针对 16kHz 采样率进行了优化，能够有效抑制背景噪音和环境干扰。对于开发者而言，该模型非常适合集成到会议记录、语音助手或播客后期处理等需要提升语音清晰度的场景中。由于基于开源的 SpeechBrain 框架，上手难度适中，可直接作为音频预处理模块，与 ASR（语音识别）工具链配合使用，显著提升识别准确率。

核心亮点

  • 高效抑制环境噪声，提升人声纯净度
  • 适配 16kHz 采样率，满足主流语音处理需求
  • 可作为 ASR 前置插件，提高语音识别率
  • 基于 Apache-2.0 协议，商业集成灵活便捷

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("speechbrain/sepformer-dns4-16k-enhancement")
tokenizer = AutoTokenizer.from_pretrained("speechbrain/sepformer-dns4-16k-enhancement")

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 speechbrain/sepformer-dns4-16k-enhancement

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 speechbrain/sepformer-dns4-16k-enhancement 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('speechbrain/sepformer-dns4-16k-enhancement')

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/speechbrain/sepformer-dns4-16k-enhancement

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/speechbrain/sepformer-dns4-16k-enhancement

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('speechbrain/sepformer-dns4-16k-enhancement')
tokenizer = AutoTokenizer.from_pretrained('speechbrain/sepformer-dns4-16k-enhancement')

模型下载

我们推荐使用命令行或者 ModelScope 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 ModelScope：

操作指引 
pip install modelscope

命令行下载

下载完整模型库

下载完整模型库 
modelscope download --model speechbrain/sepformer-dns4-16k-enhancement

下载单个文件到指定本地文件夹（以下载 README.md 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 README.md 到当前路径下 dir 目录为例） 
modelscope download --model speechbrain/sepformer-dns4-16k-enhancement README.md --local_dir ./dir

更多更丰富的命令行下载选项，可参见具体文档

SDK 下载

SDK 下载 
# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('speechbrain/sepformer-dns4-16k-enhancement')

Git 下载

请确保 lfs 已经被正确安装

Git 下载 
git lfs install
git clone https://www.modelscope.cn/speechbrain/sepformer-dns4-16k-enhancement.git

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://www.modelscope.cn/speechbrain/sepformer-dns4-16k-enhancement.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook 快速开发

下载并安装 ModelScope library

下载并安装 ModelScope library 
pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

模型加载和推理

模型加载和推理 
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'speechbrain/sepformer-dns4-16k-enhancement')

完整文档

来源: HuggingFace

---
language:
- "en"
- "de"
- "ru"
- "fr"
- "it"
- "es"
thumbnail:
tags:

  • audio-to-audio

  • Speech Enhancement

  • DNS-4

  • SepFormer

  • Transformer

  • pytorch

  • speechbrain

  • Microsoft DNS Challenge

  • Deep Noise Suppression Challenge – ICASSP 2022

license: "apache-2.0"
datasets:
  • DNS-4

metrics:
  • SI-SNR

  • PESQ

  • SIG

  • BAK

  • OVRL

model-index:
  • name: sepformer-dns4-16k-enhancement

results:
- task:
name: Speech Enhancement
type: speech-enhancement
dataset:
name: DNS-4
type: deep-noise-suppression-challenge-icassp-2022
split: baseline-dev-set
args:
language: de
metrics:
- name: DNSMOS SIG
type: sig
value: '2.999'
- name: DNSMOS BAK
type: bak
value: '3.076'
- name: DNSMOS OVRL
type: ovrl
value: '2.437'
---

<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>

SepFormer trained on Microsoft DNS-4 (Deep Noise Suppression Challenge 4 – ICASSP 2022) for speech enhancement (16k sampling frequency)

This repository provides all the necessary tools to perform speech enhancement (denoising) with a SepFormer model, implemented with SpeechBrain. The model is trained on 1300HRS of Microsoft-DNS 4 dataset with 16k sampling frequency. For a better experience we encourage you to learn more about SpeechBrain. Evaluation on DNS4 2022 baseline dev set using DNSMOS are-

| Release | SIG | BAK | OVRL |
|:-------------:|:--------------:|:--------------:|:--------------:|
| 08-01-23 | 2.999 | 3.076 | 2.437 |

DNSMOS - deep noise suppression (DNS)- mean opinion score (MOS) is a non-intrusive evaluation metric. It computes 3 scores– SIG (speech quality), BAK (background noise quality), and OVRL (overall quality) each on a scale of 1 to 5, with 5 being the best quality.

Install SpeechBrain

First of all, please install SpeechBrain with the following command:

code
pip install speechbrain

Please notice that we encourage you to read our tutorials and learn more about SpeechBrain.

Perform speech enhancement on your own audio file

python
from speechbrain.inference.separation import SepformerSeparation as separator
import torchaudio

model = separator.from_hparams(source="speechbrain/sepformer-dns4-16k-enhancement", savedir='pretrained_models/sepformer-dns4-16k-enhancement')


for custom file, change path

est_sources = model.separate_file(path='speechbrain/sepformer-dns4-16k-enhancement/example_dns4-16k.wav')

torchaudio.save("enhanced_dns4-16k.wav", est_sources[:, :, 0].detach().cpu(), 16000)

Inference on GPU

To perform inference on the GPU, add run_opts={"device":"cuda"} when calling the from_hparams method.

You can find our training results (models, logs, etc) here.

Limitations

The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.

Referencing SpeechBrain

bibtex
@misc{speechbrain,
  title={{SpeechBrain}: A General-Purpose Speech Toolkit},
  author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio},
  year={2021},
  eprint={2106.04624},
  archivePrefix={arXiv},
  primaryClass={eess.AS},
  note={arXiv:2106.04624}
}

Referencing SepFormer

bibtex
@inproceedings{subakan2021attention,
      title={Attention is All You Need in Speech Separation}, 
      author={Cem Subakan and Mirco Ravanelli and Samuele Cornell and Mirko Bronzi and Jianyuan Zhong},
      year={2021},
      booktitle={ICASSP 2021}
}

Referencing ICASSP 2022 Deep Noise Suppression Challenge

bibtex
@inproceedings{dubey2022icassp,
  title={ICASSP 2022 Deep Noise Suppression Challenge},
  author={Dubey, Harishchandra and Gopal, Vishak and Cutler, Ross and Matusevych, Sergiy and Braun, Sebastian and Eskimez, Emre Sefik and Thakker, Manthan and Yoshioka, Takuya and Gamper, Hannes and Aichner, Robert},
  booktitle={ICASSP},
  year={2022}
}

About SpeechBrain

  • Website: https://speechbrain.github.io/
  • Code: https://github.com/speechbrain/speechbrain/
  • HuggingFace: https://huggingface.co/speechbrain/
查看官方来源