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:
- c645ccb2667d1e876a6d9e2dbb17d26957100451d764bf6e4acced48b99a2f50
- Size of remote file:
- 1.06 kB
- SHA256:
- f576bf7c10be402d8932a438f3d78556045ccb02923c783be064c5c01986b342
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