Instructions to use facebook/data2vec-audio-base-10m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/data2vec-audio-base-10m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="facebook/data2vec-audio-base-10m")# Load model directly from transformers import AutoTokenizer, AutoModelForCTC tokenizer = AutoTokenizer.from_pretrained("facebook/data2vec-audio-base-10m") model = AutoModelForCTC.from_pretrained("facebook/data2vec-audio-base-10m") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f795a1e6a40b55807f6e17bcc637cf232eb32091e0e046ee336e62f96de91d25
- Size of remote file:
- 373 MB
- SHA256:
- 792243279d004d828fbafcbb3e7b55a14d3daf28bc57452e37968f75a8832824
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.