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")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("sharpcoder/wav2vec2_bjorn") model = AutoModelForCTC.from_pretrained("sharpcoder/wav2vec2_bjorn", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from sharpcoder/wav2vec2_bjorn: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/sharpcoder/wav2vec2_bjorn/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://sharpcoder/wav2vec2_bjorn/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/sharpcoder/wav2vec2_bjorn/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- a6d64e317642940d50146ab2d776f2d4c4cc53dabffd14b4f426695dcd447c20
- Size of remote file:
- 378 MB
- SHA256:
- 124b58d6d3dbb0ca4f29d31fad8c5ad9a70bc43d141b954dd380d21dfebedc17
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