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:
- 847c4bbf46a957f575e94ba7a33677e044fb1e075f25a4ac84b1586c9fe8c3b9
- Size of remote file:
- 378 MB
- SHA256:
- f2c1073dcf9d2c86893e54ef4595e3b4d0d33f8734ae783ce62af97b636f6266
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