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:
- ffd2b0bc4c4a28f37a2a6bf452acb50a323bfc9ebe98bc75901ab9de21c67f11
- Size of remote file:
- 3.96 kB
- SHA256:
- 36ab0c262a69f7648a137d97d3754278df79ae154b20ec9c98cf872e2d66a533
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