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PyTorch wrappers for using your model in audacity!
Noise supression using deep filtering
A scalable generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech)
Machine Learning and Computer Vision Engineer - Technical Interview Questions
This repo is meant to serve as a guide for Machine Learning/AI technical interviews.
Curated list of data science interview questions and answers
Official implementation of "Separate Anything You Describe"
AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head
Audiocraft is a library for audio processing and generation with deep learning. It features the state-of-the-art EnCodec audio compressor / tokenizer, along with MusicGen, a simple and controllable…
Hydra is a framework for elegantly configuring complex applications
Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
State-of-the-art deep learning based audio codec supporting both mono 24 kHz audio and stereo 48 kHz audio.
StoRM: A Diffusion-based Stochastic Regeneration Model for Speech Enhancement and Dereverberation
Score-based Generative Models (Diffusion Models) for Speech Enhancement and Dereverberation
Temporal Graph Convolutional Network for Real-time Speech Enhancement in PyTorch
Codes and template data for paper "Experiments with mmWave Automotive Radar Test-bed"
This book helps demystify the PhD admission process in Computer Science in US universities.
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 ca…
A computer science textbook
Machine Learning Interviews from FAANG, Snapchat, LinkedIn. I have offers from Snapchat, Coupang, Stitchfix etc. Blog: mlengineer.io.
An attempt to answer the age old interview question "What happens when you type google.com into your browser and press enter?"
Python code for "Probabilistic Machine learning" book by Kevin Murphy
A complete computer science study plan to become a software engineer.