Instructions to use seccily/wav2vec-lt-lite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use seccily/wav2vec-lt-lite with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="seccily/wav2vec-lt-lite")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("seccily/wav2vec-lt-lite") model = AutoModelForCTC.from_pretrained("seccily/wav2vec-lt-lite") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 20937237bf5b26a2ea2c505436f05f3105f77dcebbd0ac8e939254c8308cf51e
- Size of remote file:
- 1.26 GB
- SHA256:
- 2ab5b147423d66d121dc142612e8795733bb3b2051d9e0cec1f4d49dd19c6bba
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