This repo contains the official PyTorch implementation of "Audio Super Resolution in the Spectral Domain" (ICASSP 2023)
VoiceRestore: Flow-Matching Transformers for Universal Speech Restoration
Official pytorch implementation of the paper: "Catch-A-Waveform: Learning to Generate Audio from a Single Short Example" (NeurIPS 2021)
Complete Production Environment for professional MiniMax Music 3 audio generation, including Prompt Generation, Metadata generation including Cover Art, Sound Enhancement to improve Minimax weaknesses, mastering, and saving in various file formats
The official PyTorch implementation of VM-ASR, a model designed for high-fidelity audio super-resolution.
FastWave is a lightweight diffusion model for general audio super-resolution (any -> 48 kHz). SOTA quality reconstruction metrics with just 1.3 M parameters and ~50 GFLOPs
This repository contains the source code for the implementation of two deep learning models concerning the audio super resolution task.
🎤 Enhance your apps with MLX Swift Audio, offering robust text-to-speech and speech-to-text features for seamless communication.
Batch audio super-resolution on Backblaze B2: ingest degraded audio, run the open-source AudioSR model locally to upscale it to 48 kHz WAV, and store originals plus enhanced renditions in B2. Next.js + FastAPI sample app, S3-compatible, no GPU required.
Easy to use audio super-resolution and bandwidth extension from CLI or as a Python package.
Audio super-resolution: 16kHz → 48kHz using MossFormer2 with MLX (Python & Swift)
Port of the music repair tool Apollo for Google Colab. Converting lossy MP3 compressed music to lossless music.
To associate your repository with the audio-super-resolution topic, visit your repo's landing page and select "manage topics."