Instructions to use Pisit/wave2vec2-front with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Pisit/wave2vec2-front with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Pisit/wave2vec2-front")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Pisit/wave2vec2-front") model = AutoModelForCTC.from_pretrained("Pisit/wave2vec2-front", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2ab7eaded88def11e378f9e9ce4c5c3558e10485f80aba4b1de0eb26ea165316
- Size of remote file:
- 1.26 GB
- SHA256:
- 935103d0b8b33ed4cba854e7e0b5130d3a0d4e53de09aa10cbfad3a5155dfefc
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