Instructions to use WWWxp/wav2vec2_spoof_dection1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WWWxp/wav2vec2_spoof_dection1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="WWWxp/wav2vec2_spoof_dection1")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("WWWxp/wav2vec2_spoof_dection1") model = AutoModelForAudioClassification.from_pretrained("WWWxp/wav2vec2_spoof_dection1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0d1d5927dbad3933a80adeb55359018e480dba88839ed5eedc283d63f3314f78
- Size of remote file:
- 378 MB
- SHA256:
- 68aeb6522b293d2e20b4805f2e8eeb5529a5bbd1c569ce90b84a12b7c9dd8a94
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