Instructions to use facebook/data2vec-audio-large-10m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/data2vec-audio-large-10m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="facebook/data2vec-audio-large-10m")# Load model directly from transformers import AutoTokenizer, AutoModelForCTC tokenizer = AutoTokenizer.from_pretrained("facebook/data2vec-audio-large-10m") model = AutoModelForCTC.from_pretrained("facebook/data2vec-audio-large-10m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6928ef2584f600b424683787c346f89e5b90ded1ad22c9f2d20c8799b35cce1c
- Size of remote file:
- 1.25 GB
- SHA256:
- d8d6eaa9e4ea73d02c75b9fbe1c030efb0db15fdecefda8f2b2b29da62b1bb40
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