asteroid
is a Pytorch toolkit for audio source separation. It enables fast experimentation on common datasets with support for a large range of datasets and recipes to reproduce papers.
You can find asteroid
models by filtering at the left of the models page.
All models on the Hub come up with the following features:
For a full guide on loading pre-trained models, we recommend checking out the official guide.
All model classes (BaseModel
, ConvTasNet
, etc) have a from_pretrained
method that allows to load models from the Hub.
from asteroid.models import ConvTasNet
model = ConvTasNet.from_pretrained('mpariente/ConvTasNet_WHAM_sepclean')
If you want to see how to load a specific model, you can click Use in Adapter Transformers
and you will be given a working snippet that you can load it!
At the moment there is no automatic method to upload your models to the Hub, but the process to upload them is documented in the official guide.
All the recipes create all the needed files to upload a model to the Hub. The process usually involves the following steps:
git add
+ git commit
+ git push
).Once you do this, you can try out your model directly in the browser and share it with the rest of the community.