Audio Classification
Transformers
TensorBoard
Safetensors
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use Keeth/test_audio_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Keeth/test_audio_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Keeth/test_audio_classification")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Keeth/test_audio_classification") model = AutoModelForAudioClassification.from_pretrained("Keeth/test_audio_classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 485f8bd540b5b47bb599eb73d2176420dcabcd33566c4ef0099ddf7764aa01cc
- Size of remote file:
- 378 MB
- SHA256:
- ed4be0e4d8c19618f1c648527e7691f19f1c1f45ab6571101861e1c674b32d88
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.