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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 66770cf8944cc0a2ef1b50000505fbb5223ab2eb9a89ff935d1bfa80f1cb1032
- Size of remote file:
- 378 MB
- SHA256:
- 192462f2f118d08fc39e02cd6d71eabac8ab5c1fc22692d47f5634eae9ec9c87
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