Audio Classification
Transformers
PyTorch
TensorBoard
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use taohoang/whisper-tiny-finetuned-gtzan with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use taohoang/whisper-tiny-finetuned-gtzan with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="taohoang/whisper-tiny-finetuned-gtzan")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("taohoang/whisper-tiny-finetuned-gtzan") model = AutoModelForAudioClassification.from_pretrained("taohoang/whisper-tiny-finetuned-gtzan") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a030054a39230e910fdf07a1dec73ef3231290c09106ec09f57ff23b8be99608
- Size of remote file:
- 3.96 kB
- SHA256:
- f2768a93b52aa6b9301efcd79ad99aaf256462d69bcae907bb6a0ddaf8e772fb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.