Stars
Musicpy is a music programming language in Python designed to write music in very handy syntax through music theory and algorithms.
Implementation of MusicLM, a text to music model published by Google Research, with a few modifications.
The latent diffusion model for text-to-music generation.
multi-task and multi-track music transcription for everyone
Supplementary material of "Deep Unsupervised Drum Transcription", ISMIR 2019
Additional material for the paper ADTOF: A large dataset of non-synthetic music for automatic drum transcription
Automatic Drum Transcription software project.
Multilingual Voice Understanding Model
This project uses a variety of advanced voiceprint recognition models such as EcapaTdnn, ResNetSE, ERes2Net, CAM++, etc. It is not excluded that more models will be supported in the future. At the …
本项目使用了EcapaTdnn、ResNetSE、ERes2Net、CAM++等多种先进的声纹识别模型，同时本项目也支持了MelSpectrogram、Spectrogram、MFCC、Fbank等多种数据预处理方法
Omniscient Mozart, being able to transcribe everything in the music, including vocal, drum, chord, beat, instruments, and more.
GUI for a Vocal Remover that uses Deep Neural Networks.
Model for MDX23 music separation contest
Fine-tune the Whisper speech recognition model to support training without timestamp data, training with timestamp data, and training without speech data. Accelerate inference and support Web deplo…
High-Resolution Violin Transcription using Weak Labels
Port of OpenAI's Whisper model in C/C++
Synthesis of MIDI with DDSP (https://midi-ddsp.github.io/)
Isolate vocals, drums, bass, and other instrumental stems from any song
ScorePerformer: Expressive Piano Performance Rendering with Fine-Grained Control (ISMIR 2023)
Pytorch implementation of automatic music transcription method that uses a two-level hierarchical frequency-time Transformer architecture (hFT-Transformer).
High-Resolution Image Synthesis with Latent Diffusion Models
[NeurIPS'22] Squeezeformer: An Efficient Transformer for Automatic Speech Recognition
Code for the paper Hybrid Spectrogram and Waveform Source Separation
speech enhancement\speech seperation\sound source localization