metricgan plus voicebank

提供商speechbrain
分类audio-to-audio
许可证apache-2.0
下载量1.1K
星标0

简介

MetricGAN+ VoiceBank 是由 SpeechBrain 团队开发的一款专注于语音质量增强的深度学习模型。它不同于传统的语音合成，而是通过对抗学习机制来预测语音的客观质量得分（如 PESQ），从而引导模型对受损语音进行修复。对于中国开发者而言，该模型非常适合用于处理电话录音降噪、网络通话语音修复等实际场景。上手难度中等，由于基于开源的 SpeechBrain 框架，开发者可以快速将其集成到现有的音频处理管线中，作为语音预处理的质量把控环节。

核心亮点

  • 基于对抗学习提升语音感知质量
  • 适配 VoiceBank-DEM 等语音增强数据集
  • 适用于电话录音修复与噪声消除场景
  • 基于 SpeechBrain 框架，集成难度低
  • Apache-2.0 协议，支持商业化部署

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("speechbrain/metricgan-plus-voicebank")
tokenizer = AutoTokenizer.from_pretrained("speechbrain/metricgan-plus-voicebank")

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/metricgan-plus-voicebank

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/metricgan-plus-voicebank 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/metricgan-plus-voicebank')

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/metricgan-plus-voicebank

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/metricgan-plus-voicebank

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/metricgan-plus-voicebank')
tokenizer = AutoTokenizer.from_pretrained('speechbrain/metricgan-plus-voicebank')

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/metricgan-plus-voicebank

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/metricgan-plus-voicebank 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/metricgan-plus-voicebank')

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/metricgan-plus-voicebank.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/metricgan-plus-voicebank.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/metricgan-plus-voicebank')

完整文档

来源: HuggingFace

---
language: "en"
tags:

  • audio-to-audio

  • speech-enhancement

  • PyTorch

  • speechbrain

license: "apache-2.0"
datasets:
  • Voicebank

  • DEMAND

metrics:
  • PESQ

  • STOI

inference: false
---

<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/>

MetricGAN-trained model for Enhancement

This repository provides all the necessary tools to perform enhancement with
SpeechBrain. For a better experience we encourage you to learn more about
SpeechBrain. The model performance is:

| Release | Test PESQ | Test STOI |
|:-----------:|:-----:| :-----:|
| 21-04-27 | 3.15 | 93.0 |

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.

Pretrained Usage

To use the mimic-loss-trained model for enhancement, use the following simple code:

python
import torch
import torchaudio
from speechbrain.inference.enhancement import SpectralMaskEnhancement

enhance_model = SpectralMaskEnhancement.from_hparams(
    source="speechbrain/metricgan-plus-voicebank",
    savedir="pretrained_models/metricgan-plus-voicebank",
)


Load and add fake batch dimension

noisy = enhance_model.load_audio(
    "speechbrain/metricgan-plus-voicebank/example.wav"
).unsqueeze(0)

Add relative length tensor

enhanced = enhance_model.enhance_batch(noisy, lengths=torch.tensor([1.]))

Saving enhanced signal on disk

torchaudio.save('enhanced.wav', enhanced.cpu(), 16000)

The system is trained with recordings sampled at 16kHz (single channel).
The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *enhance_file* if needed. Make sure your input tensor is compliant with the expected sampling rate if you use *enhance_batch* as in the example.

Inference on GPU

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

Training

The model was trained with SpeechBrain (d0accc8). To train it from scratch follows these steps: 1. Clone SpeechBrain:
bash
git clone https://github.com/speechbrain/speechbrain/
2. Install it:
code
cd speechbrain
pip install -r requirements.txt
pip install -e .

3. Run Training:

code
cd  recipes/Voicebank/enhance/MetricGAN
python train.py hparams/train.yaml --data_folder=your_data_folder

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 MetricGAN+

If you find MetricGAN+ useful, please cite:

code
@article{fu2021metricgan+,
  title={MetricGAN+: An Improved Version of MetricGAN for Speech Enhancement},
  author={Fu, Szu-Wei and Yu, Cheng and Hsieh, Tsun-An and Plantinga, Peter and Ravanelli, Mirco and Lu, Xugang and Tsao, Yu},
  journal={arXiv preprint arXiv:2104.03538},
  year={2021}
}

About SpeechBrain

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

Citing SpeechBrain

Please, cite SpeechBrain if you use it for your research or business.
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}
}
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