audio/music generation and neural codec
A PyTorch library for audio generation and processing, featuring MusicGen (text-to-music), AudioGen (text-to-sound), and the EnCodec neural audio codec.
Same problem, same approach. Swapping one for another is a config change, not a rewrite.
Choose when you need a unified toolkit covering TTS/SVS in addition to music, with a more modular architecture.
Amphion (/æmˈfaɪən/) is a toolkit for Audio, Music, and Speech Generation. Its purpose is to support reproducible research and help junior researchers and engineers get started in the field of audio, music, and speech generation research and development.
Choose for top-tier local music generation with Mac/AMD/Intel support.
The most powerful local music generation model that outperforms almost all commercial alternatives, supporting Mac, AMD, Intel, and CUDA devices.
Solves the same problem with a different architecture or at a different layer. Expect to rewrite the integration.
Choose if you prefer diffusion-based audio models or need to leverage the diffusers ecosystem.
🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch.
Choose for a collection of music understanding and generation models, especially for music information retrieval tasks.
Muzic: Music Understanding and Generation with Artificial Intelligence
An earlier or more limited way to do the job. Still the right call when you need something small, proven, or CPU-only.
Use only if you need a flexible codec research toolkit and don't require recent maintenance.
FunCodec is a research-oriented toolkit for audio quantization and downstream applications, such as text-to-speech synthesis, music generation et.al.
Use as an earlier music generation baseline; superseded by ACE-Step-1.5.
ACE-Step: A Step Towards Music Generation Foundation Model
Choose for a codec with even lower token rate (40/75 tps) but narrower scope.
[ICLR 2025] SOTA discrete acoustic codec models with 40/75 tokens per second for audio language modeling
These projects were analysed and named audiocraft among their alternatives. The relationship is not symmetric — how audiocraft rates them is a separate judgement, made when audiocraft is analysed in its own right.
calls audiocraft “choose for SOTA audio/music generation models like MusicGen in a maintained library instead of NVIDIA speech synthesis examples.”
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
calls audiocraft “An audio/music generation library (MusicGen/EnCodec); choose it when your target is exclusively high-quality audio generation.”
🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch.