Audio Classification
Transformers
PyTorch
TensorBoard
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use tae98/whisper-base.en-finetuned-gtzan with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tae98/whisper-base.en-finetuned-gtzan with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="tae98/whisper-base.en-finetuned-gtzan")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("tae98/whisper-base.en-finetuned-gtzan") model = AutoModelForAudioClassification.from_pretrained("tae98/whisper-base.en-finetuned-gtzan") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 75da6cdbdb3662f6cd9a4beb8812bf6e16c69e4c1e65b706222b35574020d77d
- Size of remote file:
- 82.9 MB
- SHA256:
- 13eb444ac576a8cc757008cc88425ed567e147657d6a64f932d901af1529b13b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.