> This pipeline is deprecated but it can still be used. However, we won’t test the pipeline anymore and won’t accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.

#
Dance Diffusion

Dance Diffusion is by Zach Evans.

Dance Diffusion is the first in a suite of generative audio tools for producers and musicians released by Harmonai.

> Make sure to check out the Schedulers guide to learn how to explore the tradeoff between scheduler speed and quality, and see the reuse components across pipelines section to learn how to efficiently load the same components into multiple pipelines.

##
DanceDiffusionPipeline

###
class diffusers.DanceDiffusionPipeline

< source >

( unet: UNet1DModel scheduler: SchedulerMixin )

Parameters

- unet (UNet1DModel) — A `UNet1DModel` to denoise the encoded audio.
- scheduler (SchedulerMixin) — A scheduler to be used in combination with `unet` to denoise the encoded audio latents. Can be one of IPNDMScheduler.

Pipeline for audio generation.

This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.).

####
__call__

< source >

( batch_size: int = 1 num_inference_steps: int = 100 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None audio_length_in_s: typing.Optional[float] = None return_dict: bool = True ) →
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` 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 instead of a plain tuple.

Returns

AudioPipelineOutput or `tuple`

If `return_dict` is `True`, 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:

    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 display 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 >

( 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.

< > Update on GitHub