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stable-audio-3-medium-base

Stability AI · stable-audio-3-medium-base

> **Note:** This is the base (pre-trained) model intended for fine-tuning. If you are looking to generate audio directly, please use Stable Audio 3 Medium instead. `Stable Audio 3` is a family of fast latent diffusion models (small, medium, large) for variable length audio generation and editing. Since our models can generate several minutes of audio, variable-length generations are key to avoid the cost of producing full-length generations for short sounds. We also support inpainting, enabling targeted audio editing and the continuation of short recordings. Our latent diffusion models operate on top of a novel semantic-acoustic autoencoder that projects audio into a compact latent space, enabling efficient diffusion-based generation while preserving audio fidelity and encouraging semantic structure in the latent. Finally, we run adversarial post-training to both accelerate inference and improve generation quality, reducing the number of inference steps while improving fidelity and prompt adherence. Stable Audio 3 models are trained on licensed and Creative Commons data to generate music and sounds in less than a 2s on an H200 GPU and less than a few seconds on a MacBook Pro M4. We release the weights of small and medium, that can run on consumer-grade hardware, together with their training and inference pipeline. This model can be used with: 1. the `stable-audio-3` inference and fine-tuning library 2. the `stable-audio-tools` research library

번역 대기 중입니다. 원문을 표시합니다.

음성 합성license:other
컨텍스트 창미공개
입력 → 출력텍스트 → 오디오
모델 웨이트미확인
정보 업데이트2026/10/30 13:48:27
OVERVIEW

모델 소개

> **Note:** This is the base (pre-trained) model intended for fine-tuning. If you are looking to generate audio directly, please use Stable Audio 3 Medium instead. `Stable Audio 3` is a family of fast latent diffusion models (small, medium, large) for variable length audio generation and editing. Since our models can generate several minutes of audio, variable-length generations are key to avoid the cost of producing full-length generations for short sounds. We also support inpainting, enabling targeted audio editing and the continuation of short recordings. Our latent diffusion models operate on top of a novel semantic-acoustic autoencoder that projects audio into a compact latent space, enabling efficient diffusion-based generation while preserving audio fidelity and encouraging semantic structure in the latent. Finally, we run adversarial post-training to both accelerate inference and improve generation quality, reducing the number of inference steps while improving fidelity and prompt adherence. Stable Audio 3 models are trained on licensed and Creative Commons data to generate music and sounds in less than a 2s on an H200 GPU and less than a few seconds on a MacBook Pro M4. We release the weights of small and medium, that can run on consumer-grade hardware, together with their training and inference pipeline. This model can be used with: 1. the `stable-audio-3` inference and fine-tuning library 2. the `stable-audio-tools` research library

주요 특징

特点摘要待依据来源补齐,不凭模型名称推断能力。

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공개 평가 및 실행 설정

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

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출처 및 추적 기록

许可证:other。模型介绍与第三方评测均以原来源的具体版本和方法为准。