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 training_args.bin from sharpcoder/wav2vec2_bjorn: direct link, hf CLI and curl.
- Browser
- Download file 2.74 kB
-
https://huggingface.co/sharpcoder/wav2vec2_bjorn/resolve/main/training_args.bin
- Command line
-
hf download hf://sharpcoder/wav2vec2_bjorn/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/sharpcoder/wav2vec2_bjorn/resolve/main/training_args.bin
2.74 kB
- Xet hash:
- aa9c907bf860542139e5c58811f20f4ef7ca1a4338997336b7f48e0591c9ad00
- Size of remote file:
- 2.74 kB
- SHA256:
- d088aa47e4ea7b9e0e9873e040b77e2eb035cf9848d076792a823f0550eed203
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