| import os | |
| import torch | |
| from s3prl.util.download import _urls_to_filepaths | |
| from .expert import UpstreamExpert as _UpstreamExpert | |
| def data2vec_custom(ckpt: str, refresh: bool = False, **kwargs): | |
| if ckpt.startswith("http"): | |
| ckpt = _urls_to_filepaths(ckpt, refresh=refresh) | |
| return _UpstreamExpert(ckpt, **kwargs) | |
| def data2vec_local(*args, **kwargs): | |
| return data2vec_custom(*args, **kwargs) | |
| def data2vec_url(*args, **kwargs): | |
| return data2vec_custom(*args, **kwargs) | |
| def data2vec(refresh=False, *args, **kwargs): | |
| """ | |
| The default model - Base | |
| refresh (bool): whether to download ckpt/config again if existed | |
| """ | |
| return data2vec_base_960(refresh=refresh, *args, **kwargs) | |
| def data2vec_base_960(refresh=False, *args, **kwargs): | |
| """ | |
| The Base model | |
| refresh (bool): whether to download ckpt/config again if existed | |
| """ | |
| kwargs[ | |
| "ckpt" | |
| ] = "https://huggingface.co/s3prl/converted_ckpts/resolve/main/audio_base_ls.pt" | |
| return data2vec_custom(refresh=refresh, *args, **kwargs) | |
| def data2vec_large_ll60k(refresh=False, *args, **kwargs): | |
| """ | |
| The Large model trained on Libri-light 60k hours of data | |
| refresh (bool): whether to download ckpt/config again if existed | |
| """ | |
| kwargs[ | |
| "ckpt" | |
| ] = "https://huggingface.co/s3prl/converted_ckpts/resolve/main/vox_pretrained.pt" | |
| return data2vec_custom(refresh=refresh, *args, **kwargs) | |