Code for the paper "Jukebox: A Generative Model for Music"
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Updated
Jun 19, 2024 - Python
Code for the paper "Jukebox: A Generative Model for Music"
Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)
Vector Quantized VAEs - PyTorch Implementation
Minimalist implementation of VQ-VAE in Pytorch
Easy generative modeling in PyTorch.
PyTorch implementation of VQ-VAE + WaveNet by [Chorowski et al., 2019] and VQ-VAE on speech signals by [van den Oord et al., 2017]
(CVPR 2023) Pytorch implementation of “T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representations”
VQ-VAE for Acoustic Unit Discovery and Voice Conversion
Vector-Quantized Contrastive Predictive Coding for Acoustic Unit Discovery and Voice Conversion
Pytorch implementation of stochastically quantized variational autoencoder (SQ-VAE)
CVPR 2021: "Generating Diverse Structure for Image Inpainting With Hierarchical VQ-VAE"
A Chainer implementation of VQ-VAE.
Python toolkit for speech processing
PyTorch implementation of VQ-VAE-2 from "Generating Diverse High-Fidelity Images with VQ-VAE-2"
Official Implement of Multi-Stage Multi-Codebook (MSMC) TTS
[ICML 2023] Official PyTorch Implementation of "Hierarchical Neural Coding for Controllable CAD Model Generation".
Pytorch implementation of "Group Latent Embedding for Vector Quantized Variational Autoencoder in Non-Parallel Voice Conversion" [Interspeech 2019]
Implementation of Vector Quantised VAE (VQ-VAE) with PixelCNN prior in Tensorflow.
A Bach music generator with Artificial Intelligence. This model is made by a VQ-VAE + Transformer (decoder-only). Sequences of midi 1 quarter length are compressed into 16 codebooks via VQ-VAE and a transformer learns how to generate the codebooks sequence to obtain a midi score.
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