> For the complete documentation index, see [llms.txt](https://boinc-ai.gitbook.io/diffusers/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://boinc-ai.gitbook.io/diffusers/api/pipelines/dance-diffusion.md).

# Dance Diffusion

## Dance Diffusion

[Dance Diffusion](https://github.com/Harmonai-org/sample-generator) is by Zach Evans.

Dance Diffusion is the first in a suite of generative audio tools for producers and musicians released by [Harmonai](https://github.com/Harmonai-org).

The original codebase of this implementation can be found at [Harmonai-org](https://github.com/Harmonai-org/sample-generator).

Make sure to check out the Schedulers [guide](https://huggingface.co/using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](https://huggingface.co/using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.

### DanceDiffusionPipeline

#### class diffusers.DanceDiffusionPipeline

[\<source>](https://github.com/huggingface/diffusers/blob/v0.21.0/src/diffusers/pipelines/dance_diffusion/pipeline_dance_diffusion.py#L28)

( unetscheduler )

Parameters

* **unet** ([UNet1DModel](https://huggingface.co/docs/diffusers/v0.21.0/en/api/models/unet#diffusers.UNet1DModel)) — A `UNet1DModel` to denoise the encoded audio.
* **scheduler** ([SchedulerMixin](https://huggingface.co/docs/diffusers/v0.21.0/en/api/schedulers/overview#diffusers.SchedulerMixin)) — A scheduler to be used in combination with `unet` to denoise the encoded audio latents. Can be one of [IPNDMScheduler](https://huggingface.co/docs/diffusers/v0.21.0/en/api/schedulers/ipndm#diffusers.IPNDMScheduler).

Pipeline for audio generation.

This model inherits from [DiffusionPipeline](https://huggingface.co/docs/diffusers/v0.21.0/en/api/pipelines/overview#diffusers.DiffusionPipeline). Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.).

**\_\_call\_\_**

[\<source>](https://github.com/huggingface/diffusers/blob/v0.21.0/src/diffusers/pipelines/dance_diffusion/pipeline_dance_diffusion.py#L48)

( batch\_size: int = 1num\_inference\_steps: int = 100generator: typing.Union\[torch.\_C.Generator, typing.List\[torch.\_C.Generator], NoneType] = Noneaudio\_length\_in\_s: typing.Optional\[float] = Nonereturn\_dict: bool = True ) → [AudioPipelineOutput](https://huggingface.co/docs/diffusers/v0.21.0/en/api/pipelines/dance_diffusion#diffusers.AudioPipelineOutput) or `tuple`

Parameters

* **batch\_size** (`int`, *optional*, defaults to 1) — The number of audio samples to generate.
* **num\_inference\_steps** (`int`, *optional*, defaults to 50) — The number of denoising steps. More denoising steps usually lead to a higher-quality audio sample at the expense of slower inference.
* **generator** (`torch.Generator`, *optional*) — A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic.
* **audio\_length\_in\_s** (`float`, *optional*, defaults to `self.unet.config.sample_size/self.unet.config.sample_rate`) — The length of the generated audio sample in seconds.
* **return\_dict** (`bool`, *optional*, defaults to `True`) — Whether or not to return a [AudioPipelineOutput](https://huggingface.co/docs/diffusers/v0.21.0/en/api/pipelines/dance_diffusion#diffusers.AudioPipelineOutput) instead of a plain tuple.

Returns

[AudioPipelineOutput](https://huggingface.co/docs/diffusers/v0.21.0/en/api/pipelines/dance_diffusion#diffusers.AudioPipelineOutput) or `tuple`

If `return_dict` is `True`, [AudioPipelineOutput](https://huggingface.co/docs/diffusers/v0.21.0/en/api/pipelines/dance_diffusion#diffusers.AudioPipelineOutput) is returned, otherwise a `tuple` is returned where the first element is a list with the generated audio.

The call function to the pipeline for generation.

Example:

Copied

```
from diffusers import DiffusionPipeline
from scipy.io.wavfile import write

model_id = "harmonai/maestro-150k"
pipe = DiffusionPipeline.from_pretrained(model_id)
pipe = pipe.to("cuda")

audios = pipe(audio_length_in_s=4.0).audios

# To save locally
for i, audio in enumerate(audios):
    write(f"maestro_test_{i}.wav", pipe.unet.sample_rate, audio.transpose())

# To dislay in google colab
import IPython.display as ipd

for audio in audios:
    display(ipd.Audio(audio, rate=pipe.unet.sample_rate))
```

### AudioPipelineOutput

#### class diffusers.AudioPipelineOutput

[\<source>](https://github.com/huggingface/diffusers/blob/v0.21.0/src/diffusers/pipelines/pipeline_utils.py#L126)

( audios: ndarray )

Parameters

* **audios** (`np.ndarray`) — List of denoised audio samples of a NumPy array of shape `(batch_size, num_channels, sample_rate)`.

Output class for audio pipelines.