Instructions to use facebook/data2vec-audio-base-100h with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/data2vec-audio-base-100h with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="facebook/data2vec-audio-base-100h")# Load model directly from transformers import AutoTokenizer, AutoModelForCTC tokenizer = AutoTokenizer.from_pretrained("facebook/data2vec-audio-base-100h") model = AutoModelForCTC.from_pretrained("facebook/data2vec-audio-base-100h") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 32c637b2d8407e2128ce18016583c09f4e14ad52954ea9da5a2c7e274b9cd241
- Size of remote file:
- 373 MB
- SHA256:
- 036a5ab3229328165bdd9a8178572f25c840dd64fd53d62357bf8feef2a87d9a
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