Instructions to use Jethuestad/dat259-wav2vec2-en2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jethuestad/dat259-wav2vec2-en2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Jethuestad/dat259-wav2vec2-en2")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Jethuestad/dat259-wav2vec2-en2") model = AutoModelForCTC.from_pretrained("Jethuestad/dat259-wav2vec2-en2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d5f97a3928c2c0b79f4bd8dc62b35c102b19a3be0113b883c59d911fa51e8d80
- Size of remote file:
- 378 MB
- SHA256:
- f21d79b6d54d47ea1eb0c2e86eac35763b4ed2c8283a11d32e9b272887572722
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