Instructions to use facebook/data2vec-audio-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/data2vec-audio-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="facebook/data2vec-audio-large")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("facebook/data2vec-audio-large") model = AutoModel.from_pretrained("facebook/data2vec-audio-large") - Notebooks
- Google Colab
- Kaggle
pretraining dataset is Libri-Light, not LibriSpeech
#2
by gaunernst - opened
As per paper (https://arxiv.org/pdf/2202.03555), table 2, data2vec audio large was pre-trained on Libri-Light.
GitHub page (https://github.com/facebookresearch/fairseq/tree/main/examples/data2vec) also shows that large variants were pre-trained on Libri-Light.
Datasets tag should be updated accordingly.