Audio Classification
Transformers
PyTorch
TensorBoard
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use anderloh/testV4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anderloh/testV4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="anderloh/testV4")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("anderloh/testV4") model = AutoModelForAudioClassification.from_pretrained("anderloh/testV4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e9882c835861b79deeeb74280f16798215fc8343646b108c9f0ca7061e6bc3e2
- Size of remote file:
- 52.2 MB
- SHA256:
- aea45770ab8a1813699d092cdbab8fe66d5efed86981e136f43abbe531275490
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.