3 repositorios
Awesome GitHub RepositoriesAutoregressive Audio Diffusion
Diffusion models that predict continuous speech representations in an autoregressive manner to generate high-fidelity audio.
Distinct from Diffusion Models: Distinct from Diffusion Models: specifically applies the diffusion process to continuous audio speech representations.
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Awesome Autoregressive Audio Diffusion GitHub Repositories
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- openbmb/voxcpm
OpenBMB/VoxCPM
29,985Ver en GitHubVoxCPM is a multilingual speech synthesis system and text-to-speech inference server. It functions as an AI voice cloning tool and a synthetic voice designer, capable of generating natural speech across global languages and regional dialects using a GPU-accelerated audio generator. The project features a speech model fine-tuning framework that supports both full parameter updates and low-rank adaptation for customizing voice characteristics. It enables high-fidelity voice cloning from reference audio, including cross-lingual voice transfer and acoustic environment mimicry, as well as the crea
Uses a hierarchical diffusion-based autoregressive architecture to generate high-fidelity continuous speech representations.
Pythonaudiodeeplearningminicpm - svc-develop-team/so-vits-svc
svc-develop-team/so-vits-svc
28,097Ver en GitHubThis project is a singing voice conversion tool based on VITS generative modeling. It transforms the identity of a singing voice to a target speaker while preserving the original melody, lyrics, and intonation. The system distinguishes itself through hybrid voice synthesis, allowing for the blending of multiple speaker identities via linear model interpolation. It utilizes cluster-based feature retrieval to increase target voice similarity and employs a diffusion probabilistic model as a post-processor to remove electronic artifacts and improve vocal clarity. The software covers a broad rang
Applies a diffusion probabilistic model as a post-processor to remove electronic artifacts and improve vocal clarity.
Python - facebookresearch/audiocraft
facebookresearch/audiocraft
23,379Ver en 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
Reconstructs audio waveforms from discrete tokens by predicting multiple frequency bands simultaneously for higher fidelity.
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