Pretrained models

Asteroid provides pretrained models through the Asteroid community in Zenodo. Have a look at the Zenodo page to choose which model you want to use.

Enjoy having pretrained models? Please share your models if you train some, we made it simple with the asteroid-upload CLI, check the next sections.

Using them

Loading a pretrained model is super simple!

from asteroid.models import ConvTasNet
model = ConvTasNet.from_pretrained('mpariente/ConvTasNet_WHAM!_sepclean')

Use the search page if you want to narrow your search.

You can also load it with Hub

from torch import hub
model = hub.load('mpariente/asteroid', 'conv_tasnet', 'mpariente/ConvTasNet_WHAM!_sepclean')

Model caching

When using a from_pretrained method, the model is downloaded and cached. The cache directory is either the value in the $ASTEROID_CACHE environment variable, or ~/.cache/torch/asteroid.

Share your models

At the end of each sharing-enabled recipe, all the necessary infos are gathered into a file, the only thing that’s left to do is to run

asteroid-upload exp/your_exp_dir/publish_dir --uploader "Name Here"

Ok, not really. First you need to register to Zenodo (Sign in with GitHub: ok), create a token and use it with the --token option of the CLI, or by setting the ACCESS_TOKEN environment variable. If you plan to upload more models (and you should :innocent:), you can fill in your infos in uploader_info.yml at the root, like this.

uploader: Manuel Pariente
affiliation: INRIA
git_username: mpariente

Note about licenses

All Asteroid’s pretrained models are shared under the Attribution-ShareAlike 3.0 (CC BY-SA 3.0) license. This means that models are released under the same license as the original training data. If any non-commercial data is used during training (wsj0, WHAM’s noises etc..), the models are non-commercial use only. This is indicated in the bottom of the corresponding Zenodo page (ex: here).

Read the Docs v: v0.3.3
On Read the Docs
Project Home

Free document hosting provided by Read the Docs.