diffusers/docs/source/api/pipelines/audio_diffusion.mdx

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# Audio Diffusion

## Overview

[Audio Diffusion](https://github.com/teticio/audio-diffusion) by Robert Dargavel Smith.

Audio Diffusion leverages the recent advances in image generation using diffusion models by converting audio samples to
and from mel spectrogram images.

The original codebase of this implementation can be found [here](https://github.com/teticio/audio-diffusion), including
training scripts and example notebooks.

## Available Pipelines:

| Pipeline | Tasks | Colab
|---|---|:---:|
| [pipeline_audio_diffusion.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/audio_diffusion/pipeline_audio_diffusion.py) | *Unconditional Audio Generation* | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/teticio/audio-diffusion/blob/master/notebooks/audio_diffusion_pipeline.ipynb) |


## Examples:

### Audio Diffusion

```python
import torch
from IPython.display import Audio
from diffusers import DiffusionPipeline

device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = DiffusionPipeline.from_pretrained("teticio/audio-diffusion-256").to(device)

output = pipe()
display(output.images[0])
display(Audio(output.audios[0], rate=mel.get_sample_rate()))
```

### Latent Audio Diffusion

```python
import torch
from IPython.display import Audio
from diffusers import DiffusionPipeline

device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = DiffusionPipeline.from_pretrained("teticio/latent-audio-diffusion-256").to(device)

output = pipe()
display(output.images[0])
display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate()))
```

### Audio Diffusion with DDIM (faster)

```python
import torch
from IPython.display import Audio
from diffusers import DiffusionPipeline

device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = DiffusionPipeline.from_pretrained("teticio/audio-diffusion-ddim-256").to(device)

output = pipe()
display(output.images[0])
display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate()))
```

### Variations, in-painting, out-painting etc.

```python
output = pipe(
    raw_audio=output.audios[0, 0],
    start_step=int(pipe.get_default_steps() / 2),
    mask_start_secs=1,
    mask_end_secs=1,
)
display(output.images[0])
display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate()))
```

## AudioDiffusionPipeline
[[autodoc]] AudioDiffusionPipeline
    - __call__
    - encode
    - slerp


## Mel
[[autodoc]] Mel
    - audio_slice_to_image
    - image_to_audio