sepformer wham16k enhancement

提供商speechbrain
分类speech-enhancement
许可证apache-2.0
下载量2.3K
星标0

简介

SepFormer WHAM16k 是一款由 SpeechBrain 团队开发的语音增强模型，专注于从嘈杂的环境背景中提取纯净的人声。它采用了先进的 Transformer 架构来处理时间-频率域的信号，能够有效抑制复杂的背景噪音并减少语音失真。对于需要处理低质量录音、会议记录预处理或语音识别前置降噪的开发者来说，这是一个非常实用的工具。该模型上手难度中等，通常集成在 SpeechBrain 框架中，可作为 ASR（自动语音识别）流水线的前端增强模块，显著提升识别率。

核心亮点

  • 基于 Transformer 架构，强力抑制复杂环境噪音
  • 专为 16kHz 采样率语音设计，适配主流音频标准
  • 可作为 ASR 前端，有效提升语音识别的准确率
  • Apache-2.0 开源协议，方便企业级商业集成

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-wham16k-enhancement")
tokenizer = AutoTokenizer.from_pretrained("speechbrain/sepformer-wham16k-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-wham16k-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-wham16k-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-wham16k-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-wham16k-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-wham16k-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-wham16k-enhancement')
tokenizer = AutoTokenizer.from_pretrained('speechbrain/sepformer-wham16k-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-wham16k-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-wham16k-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-wham16k-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-wham16k-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-wham16k-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-wham16k-enhancement')

完整文档

来源: HuggingFace

---
language: "en"
thumbnail:
tags:

  • audio-to-audio

  • Speech Enhancement

  • WHAM!

  • SepFormer

  • Transformer

  • pytorch

  • speechbrain

license: "apache-2.0"
datasets:
  • WHAM

metrics:
  • SI-SNR

  • PESQ

---

<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 WHAM! 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, and pretrained on WHAM! dataset with 16k sampling frequency, which is basically a version of WSJ0-Mix dataset with environmental noise and reverberation in 8k. For a better experience we encourage you to learn more about SpeechBrain. The given model performance is 14.3 dB SI-SNR on the test set of WHAM! dataset.

| Release | Test-Set SI-SNR | Test-Set PESQ |
|:-------------:|:--------------:|:--------------:|
| 06-30-22 | 13.8 | 2.20 |

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.pretrained import SepformerSeparation as separator
import torchaudio

model = separator.from_hparams(source="speechbrain/sepformer-wham16k-enhancement", savedir='pretrained_models/sepformer-wham16k-enhancement')


for custom file, change path

est_sources = model.separate_file(path='speechbrain/sepformer-wham16k-enhancement/example_wham16k.wav')

torchaudio.save("enhanced_wham16k.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.

Training

The training script is currently being worked on an ongoing pull-request.

We will update the model card as soon as the PR is merged.

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}
}


@article{subakan2023exploring,
  author={Subakan, Cem and Ravanelli, Mirco and Cornell, Samuele and Grondin, François and Bronzi, Mirko},
  journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing}, 
  title={Exploring Self-Attention Mechanisms for Speech Separation}, 
  year={2023},
  volume={31},
  pages={2169-2180},
}

About SpeechBrain

  • Website: https://speechbrain.github.io/
  • Code: https://github.com/speechbrain/speechbrain/
  • HuggingFace: https://huggingface.co/speechbrain/
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