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Awesome GitHub RepositoriesFlow-Matching Audio Diffusion
Generative architectures that use flow-matching to transform noise into continuous audio latents.
Distinct from Latent Diffusion Models: Specializes latent diffusion using flow-matching for audio signals instead of images
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Awesome Flow-Matching Audio Diffusion GitHub Repositories
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- facebookresearch/audiocraft
facebookresearch/audiocraft
23,379Voir sur GitHubAudiocraft is a deep learning audio library and machine learning framework designed for training, fine-tuning, and evaluating generative models for music and sound effects. It functions as a text-to-music generative model and a neural audio codec, providing the tools necessary to compress audio signals into discrete representations and synthesize high-fidelity waveforms from textual descriptions. The framework is distinguished by its ability to combine multiple conditioning signals, allowing for the generation of audio based on text prompts, melodic excerpts, or style-based audio clips. It al
Generates high-fidelity waveforms by learning a vector field that transforms noise into continuous audio latents.
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