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")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("seccily/wav2vec-lt-lite") model = AutoModelForCTC.from_pretrained("seccily/wav2vec-lt-lite", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from seccily/wav2vec-lt-lite: direct link, hf CLI and curl.
- Browser
- Download file 2.29 kB
-
https://huggingface.co/seccily/wav2vec-lt-lite/resolve/main/training_args.bin
- Command line
-
hf download hf://seccily/wav2vec-lt-lite/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/seccily/wav2vec-lt-lite/resolve/main/training_args.bin
2.29 kB
- Xet hash:
- 8fd17b9591b69e84eeecd6f18ffa4402d63afcf6f573bed9fc87b7ee33aefb35
- Size of remote file:
- 2.29 kB
- SHA256:
- cf0fb689bee313a15af127217b951f91b90bb216a2738829caad7029523c883a
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