2 repositorios
Audio generation models that utilize non-audio inputs such as images or text to synthesize soundscapes.
Distinct from Audio Generation Models: Focuses on cross-modal input mapping specifically, whereas Audio Generation Models is the broader category for any audio synthesis.
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AudioGPT is an LLM-driven audio framework and processing suite that uses large language models to orchestrate neural audio pipelines. It functions as a multimodal audio generator and processing system, integrating a collection of pretrained models to handle speech synthesis, sound generation, and audio manipulation. The system is distinguished by its ability to generate audio from diverse inputs, including text and images, and its capacity to produce synchronized talking head videos. It also operates as a neural speech translator, converting spoken language between different tongues while pre
Implements a system that generates soundscapes and audio clips from visual images or natural language descriptions.
This repository provides a collection of reference implementations and code examples for training and deploying machine learning models using the MLX framework. It serves as a practical guide for executing distributed training, fine-tuning large language models, converting model weights, and implementing multimodal generative workflows. The project distinguishes itself through specialized examples for local hardware execution, featuring weight quantization to reduce memory usage and low-rank adaptation for parameter-efficient fine-tuning. It also includes scripts for transforming external mod
Implements generative workflows for producing text, images, audio, and video from mixed-modal inputs.