Instructions to use WWWxp/wav2vec2_spoof_dection_project with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WWWxp/wav2vec2_spoof_dection_project with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="WWWxp/wav2vec2_spoof_dection_project")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("WWWxp/wav2vec2_spoof_dection_project") model = AutoModelForAudioClassification.from_pretrained("WWWxp/wav2vec2_spoof_dection_project") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6165ce80f699f01b68f66d658c074850171872500d6be9dc6429f4868606a070
- Size of remote file:
- 3.64 kB
- SHA256:
- 23c28fa38a45a9136856c4448b97258c581fae4fc4459b44e5e5b2c8a2e15f20
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