Publications (7)
AutoMixer: Checkpoint Artifacts as Automatic Data Mixers
Ernie Chang, Yang Li, Patrick Huber +4
In language model training, it is desirable to equip models with capabilities from various tasks. However, it is not clear how to directly obtain the right data mixtures for these…
MusicFlow: Cascaded Flow Matching for Text Guided Music Generation
K R Prajwal, Bowen Shi, Matthew Lee +8
We introduce MusicFlow, a cascaded text-to-music generation model based on flow matching. Based on self-supervised representations to bridge between text descriptions and music aud…
Self-Supervised Representations for Singing Voice Conversion
Tejas Jayashankar, Jilong Wu, Leda Sari +3
A singing voice conversion model converts a song in the voice of an arbitrary source singer to the voice of a target singer. Recently, methods that leverage self-supervised audio r…
Stack-and-Delay: a new codebook pattern for music generation
Gael Le Lan, Varun Nagaraja, Ernie Chang +5
In language modeling based music generation, a generated waveform is represented by a sequence of hierarchical token stacks that can be decoded either in an auto-regressive manner…
High Fidelity Text-Guided Music Editing via Single-Stage Flow Matching
Gael Le Lan, Bowen Shi, Zhaoheng Ni +9
We introduce MelodyFlow, an efficient text-controllable high-fidelity music generation and editing model. It operates on continuous latent representations from a low frame rate 48…
Simple and Controllable Music Generation
Jade Copet, Felix Kreuk, Itai Gat +5
We tackle the task of conditional music generation. We introduce MusicGen, a single Language Model (LM) that operates over several streams of compressed discrete music representati…