Dance Diffusion by Zach Evans.
Dance Diffusion is the first in a suite of generative audio tools for producers and musicians to be released by Harmonai. For more info or to get involved in the development of these tools, please visit https://harmonai.org and fill out the form on the front page.
The original codebase of this implementation can be found here.
|pipeline_dance_diffusion.py||Unconditional Audio Generation||-|
class diffusers.DanceDiffusionPipeline< source >
( unet scheduler )
This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or 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
int, optional, defaults to 1) — The number of audio samples to generate.
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.
torch.Generator, optional) — One or a list of torch generator(s) to make generation deterministic.
float, optional, defaults to
self.unet.config.sample_size/self.unet.config.sample_rate) — The length of the generated audio sample in seconds. Note that the output of the pipeline, i.e.
sample_size, will be
bool, optional, defaults to
True) — Whether or not to return a AudioPipelineOutput instead of a plain tuple.
True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images.