Instructions to use bisoye/wav2vec2-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bisoye/wav2vec2-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="bisoye/wav2vec2-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("bisoye/wav2vec2-base") model = AutoModelForAudioClassification.from_pretrained("bisoye/wav2vec2-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from bisoye/wav2vec2-base: direct link, hf CLI and curl.
- Browser
- Download file 5.24 kB
-
https://huggingface.co/bisoye/wav2vec2-base/resolve/main/training_args.bin
- Command line
-
hf download hf://bisoye/wav2vec2-base/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/bisoye/wav2vec2-base/resolve/main/training_args.bin
5.24 kB
- Xet hash:
- 94e576fff1927c9538384e9157ee102255e1078140a84b814d25f9abfcc6946e
- Size of remote file:
- 5.24 kB
- SHA256:
- d89be64a2337bb8d33c64855076d2a8a5acb392c620c02d8e86d3b64421449a2
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