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:
- de1e919cc4ba46c02639076b4057145bd7d3a55028e38967bf9e970e687060de
- Size of remote file:
- 2.86 kB
- SHA256:
- 7c7c3044751cac1823231bc038dc89c3c85b9e0ef4b7d3b455618736d6d15a66
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