---
title: stable-audio-3-medium
canonical_url: "https://www.modelscope.cn/models/stabilityai/stable-audio-3-medium"
md_url: "https://www.modelscope.cn/models/stabilityai/stable-audio-3-medium.md"
repository: stabilityai/stable-audio-3-medium
last_updated: 2026-06-17
license: other
base_model:
  - stabilityai/stable-audio-3-medium-base
base_model_relation: finetune
parameters: 2.9B
tensor_type:
  - BF16
  - F32
library_name:
  - pytorch
  - safetensors
frameworks:
  - pytorch
language:
  - en
downloads: 928
stars: 2
tags:
  - audio-generation
  - music
  - sound-effects
  - diffusion
---

# stable-audio-3-medium

> stable-audio-3-medium - stabilityai 在 ModelScope 开源的模型。Stable Audio 3 Medium

stabilityai/stable-audio-3-medium 是 ModelScope 魔搭社区上的 2.9B 参数机器学习模型，采用 other 许可，基于 stabilityai/stable-audio-3-medium-base 构建。

- **Repository**: stabilityai/stable-audio-3-medium
- **License**: other
- **Parameters**: 2.9B
- **Base model**: stabilityai/stable-audio-3-medium-base
- **Tags**: audio-generation, music, sound-effects, diffusion
- **Downloads**: 928
- **Stars**: 2
- **Last updated**: 2026-06-17

Source: https://www.modelscope.cn/models/stabilityai/stable-audio-3-medium

---

# Stable Audio 3 Medium

Please note: For commercial use, please refer to [https://stability.ai/license](https://stability.ai/license)

## Model Description
`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.

## Usage

This model can be used with:
1. the [`stable-audio-3`](https://github.com/Stability-AI/stable-audio-3) inference and fine-tuning library
2. the [`stable-audio-tools`](https://github.com/Stability-AI/stable-audio-tools) research library

### Using with `stable-audio-3`
```python
from stable_audio_3 import StableAudioModel

model = StableAudioModel.from_pretrained("medium")
audio = model.generate(
    prompt=(
        "House music that encapsulates the feeling of being at a festival "
        "in the sunny weather with all your friends 124 BPM"
    ),
    duration=180
)
```

### Using with `stable-audio-tools`

```python
import torch
import torchaudio
from einops import rearrange
from stable_audio_tools import get_pretrained_model
from stable_audio_tools.inference.generation import generate_diffusion_cond_inpaint

device = "cuda" if torch.cuda.is_available() else "cpu"
if device == "cuda":
  model_half = True

# Download model
model, model_config = get_pretrained_model("stabilityai/stable-audio-3-medium")
sample_rate = model_config["sample_rate"]
sample_size = model_config["sample_size"]

model = model.to(device)
if model_half:
  model = model.to(torch.float16)
# Set up text and timing conditioning
conditioning = [{
    "prompt": (
        "A dream-like Synthpop instrumental that would accompany "
        "a dream-sequence in a surrealist movie 120 BPM"
    ),
    "seconds_total": 380
}]

# Generate stereo audio
output = generate_diffusion_cond_inpaint(
    model,
    steps=8,
    cfg_scale=1.0,
    conditioning=conditioning,
    sample_size=sample_size,
    sampler_type="pingpong",
    device=device
)

# Rearrange audio batch to a single sequence
output = rearrange(output, "b d n -> d (b n)")

# Peak normalize, clip, convert to int16, and save to file
output = output.to(torch.float32).div(torch.max(torch.abs(output))).clamp(-1, 1).mul(32767).to(torch.int16).cpu()
torchaudio.save("output.wav", output, sample_rate)
```


## Model Details
* **Model type**: `Stable Audio 3` is a latent diffusion model based on a transformer architecture.
* **Language(s)**: English
* **License**: [Stability AI Community License](https://huggingface.co/stabilityai/stable-audio-3/blob/main/LICENSE.md).
* **Commercial License**: to use this model commercially, please refer to [https://stability.ai/license](https://stability.ai/license)
* **Research Paper**: [https://arxiv.org/abs/2605.17991](https://arxiv.org/abs/2605.17991)

We use a publicly available pre-trained T5Gemma model ([t5gemma-b-b-ul2](https://huggingface.co/google/t5gemma-b-b-ul2)) for text conditioning. T5Gemma is redistributed under the [Gemma Terms of Use](LICENSE_GEMMA.md).

## Training dataset

### Datasets Used
Our dataset consists of 1,278,902 audio recordings, where 806,284 recordings are licensed from [AudioSparx](https://www.audiosparx.com/) and a further 472,618 are from [Freesound](https://freesound.org/). 
The Freesound portion consists of recordings licensed under CC-0, CC-BY, or CCSampling+. To ensure no copyrighted content was present in the Freesound data, music recordings were identified
using the PANNs [89] tagger. We flagged audio that activated music-related tags for at least 30s (threshold of 0.15),
that was sent to a trusted content detection company to verify the absence of copyrighted material. All identified copyrighted content was removed. After filtering, the Freesound part includes 266,324 CC-0, 194,840 CC-BY, and 11,454
CC-Sampling+ recordings. The same subset of Freesound audio we used to train Stable Audio Open: https://info.stability.ai/attributions.