Instructions to use alkiskoudounas/wav2vec2-base-fsc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alkiskoudounas/wav2vec2-base-fsc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="alkiskoudounas/wav2vec2-base-fsc")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("alkiskoudounas/wav2vec2-base-fsc") model = AutoModelForAudioClassification.from_pretrained("alkiskoudounas/wav2vec2-base-fsc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fcaa688205da41c90ded65012f469dac3c21486f7b92ff96abc88cf98919d574
- Size of remote file:
- 5.37 kB
- SHA256:
- 4ecd7eafab2eafdf5578ee5ef0979f2a969b62ea70e81ad9b550b84113005ea5
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