Instructions to use AKulk/wav2vec2-base-timit-epochs5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AKulk/wav2vec2-base-timit-epochs5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="AKulk/wav2vec2-base-timit-epochs5")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("AKulk/wav2vec2-base-timit-epochs5") model = AutoModelForCTC.from_pretrained("AKulk/wav2vec2-base-timit-epochs5") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f13cff51671b050d83f4c71db79839eab1e2600a6628398c04c708bfbffc6300
- Size of remote file:
- 1.26 GB
- SHA256:
- 6c93cd93d54abc53ece5a45a22654b46045497b4599636021f954938e7a8782c
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