Sgmse

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sgmse provides PyTorch implementations of Score-based Generative Models (Diffusion Models) for advanced speech enhancement and dereverberation. It offers pretrained checkpoints and detailed usage instructions for researchers and developers.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is sgmse?

sgmse is an open-source repository offering official PyTorch implementations of Score-based Generative Models, also known as Diffusion Models, specifically tailored for speech enhancement and dereverberation tasks. It includes code for various research papers, allowing users to reproduce results and build upon existing models. The repository provides pretrained checkpoints for different datasets and tasks, such as speech enhancement on VoiceBank-DEMAND and WSJ0-CHiME3, and dereverberation on WSJ0-REVERB. It supports training and evaluation with options for various SDEs and backbone networks, catering to both 16 kHz and 48 kHz models. Detailed installation instructions and logging options (W&B or local CSV) are also provided, making it a valuable resource for researchers and practitioners in audio processing.

Best used for

Ideal for researchers and developers who need to implement and experiment with state-of-the-art diffusion models for audio processing. Especially valuable for tasks like reducing background noise in speech recordings, removing echo and reverberation, and exploring different generative model architectures for speech restoration.

Common actions

enhance speech audio

remove audio reverberation

implement diffusion models

train generative models

evaluate speech models

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Capabilities

Key features

Speech enhancement
Speech dereverberation
Diffusion models
PyTorch implementation
Pretrained checkpoints
Training scripts
Evaluation metrics

Target Audience

researchers in audio processingmachine learning engineersacademics studying diffusion modelsdevelopers working on speech applications

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What kind of speech processing tasks can sgmse handle?

sgmse is primarily designed for speech enhancement, which involves reducing noise from speech signals, and speech dereverberation, focused on removing echoes and reverberation from audio. It leverages advanced Score-based Generative Models (Diffusion Models) for these tasks.

Does sgmse provide pretrained models?

Yes, sgmse offers several pretrained checkpoints for various tasks and datasets. These include models trained on VoiceBank-DEMAND, WSJ0-CHiME3 for speech enhancement, and WSJ0-REVERB for dereverberation, as well as 48 kHz models and checkpoints for investigating training objectives.

What are the system requirements for running sgmse?

The repository recommends creating a new virtual environment with Python 3.11 and installing dependencies via `pip install -r requirements.txt`. While other Python versions might work, 3.11 is the tested version. It also supports Weights & Biases (W&B) for logging, which requires an account and login.

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Coding & Development › Open Source & Models

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