Instructions to use sharpcoder/wav2vec2_bjorn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sharpcoder/wav2vec2_bjorn with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="sharpcoder/wav2vec2_bjorn")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("sharpcoder/wav2vec2_bjorn") model = AutoModelForCTC.from_pretrained("sharpcoder/wav2vec2_bjorn") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1af4c171634c361a4d6525ffa95692a0fa9c2b57e549f9899f49b4952ca8cf8d
- Size of remote file:
- 378 MB
- SHA256:
- c3223b0f3c2f81a63409be98f9db393cf77731c7b67c81573232f7f6560540b1
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