mpariente 的倉庫

:link: Some useful websites for programmers.

最近提交 2017年6月8日

This repo contains the scripts, models and required files for the Interspeech 2020 Deep Noise Suppression (DNS) Challenge. We are open sourcing clean speech and noise files as well. Participants of this challenge will use the scripts from this repo to create data to train their noise suppressors. They will compare their method with our baseline noise suppressor and report the results.

最近提交 2020年2月13日

Implementation for paper "iMetricGAN: Intelligibility Enhancement for Speech-in-Noise using Generative Adversarial Network-based Metric Learning"

最近提交 2020年4月10日

An open source dataset for source separation

最近提交 2020年5月25日

First repository

最近提交 2018年10月29日

最近提交 2020年6月5日

Ranger - a synergistic optimizer using RAdam (Rectified Adam) and LookAhead in one codebase

最近提交 2020年3月30日

A Pure-Python Real-Time Audio Library

最近提交 2018年6月27日

A must-read paper for speech separation based on neural networks

最近提交 2020年3月16日

A Flexible and Powerful Parameter Server for large-scale machine learning

最近提交 2020年7月31日

simple audio I/O for pytorch

最近提交 2019年7月11日

Build the Linux Kernel and Modules on board the NVIDIA Jetson Nano Developer Kit

最近提交 2019年11月26日

https://github.com/DataTalksClub

最近提交 2026年1月19日

Data Engineering Zoomcamp is a free 9-week course on building production-ready data pipelines. The next cohort starts in January 2026. Join the course here 👇🏼

最近提交 2026年1月15日

🤗 Fast, efficient, open-access datasets and evaluation metrics in PyTorch, TensorFlow, NumPy and Pandas

最近提交 2020年12月4日

Real Time Speech Enhancement in the Waveform Domain (Interspeech 2020)We provide a PyTorch implementation of the paper Real Time Speech Enhancement in the Waveform Domain. In which, we present a causal speech enhancement model working on the raw waveform that runs in real-time on a laptop CPU. The proposed model is based on an encoder-decoder architecture with skip-connections. It is optimized on both time and frequency domains, using multiple loss functions. Empirical evidence shows that it is capable of removing various kinds of background noise including stationary and non-stationary noises, as well as room reverb. Additionally, we suggest a set of data augmentation techniques applied directly on the raw waveform which further improve model performance and its generalization abilities.

最近提交 2020年9月4日

End-to-End Speech Processing Toolkit

最近提交 2020年8月14日

We'll see what this becomes

最近提交 2022年5月22日

Header-only library for using Keras models in C++.

最近提交 2018年5月24日

This is the code for the "How to Deploy a Keras Model to Production" by Siraj Raval on Youtube

最近提交 2017年6月6日