Audio Classification
Transformers
PyTorch
TensorBoard
Safetensors
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use pratap18/audio_classification_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pratap18/audio_classification_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="pratap18/audio_classification_model")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("pratap18/audio_classification_model") model = AutoModelForAudioClassification.from_pretrained("pratap18/audio_classification_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a82b3a00a9073f97974570af771992d341989864bf05496d2b91df51dc1a57b6
- Size of remote file:
- 378 MB
- SHA256:
- ca840b75cb84f03dc628c17ddd8524193a6d8fed35db490bf14ee60abc62b3bd
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