Audio Classification
Transformers
PyTorch
TensorBoard
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use anderloh/testV4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anderloh/testV4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="anderloh/testV4")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("anderloh/testV4") model = AutoModelForAudioClassification.from_pretrained("anderloh/testV4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 29e4b60519ed58e638e23ae9b2dbbba68de769a4fd6776c114f6d274ed03e8db
- Size of remote file:
- 4.98 kB
- SHA256:
- 79dc68acdd60fa3efcfcd68d874d2d0e26826c2a0112a3d465fd23395e165caa
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