Audio Classification
Transformers
TensorBoard
Safetensors
hubert
Generated from Trainer
Eval Results (legacy)
Instructions to use iamhack/DH_o_m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use iamhack/DH_o_m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="iamhack/DH_o_m")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("iamhack/DH_o_m") model = AutoModelForAudioClassification.from_pretrained("iamhack/DH_o_m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3f8a415f0b45cbd0bac79be48193d37f8834691e761efca5b3832fdd85269347
- Size of remote file:
- 94.8 MB
- SHA256:
- af813713ff17ee99293a2a00e5561fe84f0b2bdff11e02efc3c3f69f36c39cc5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.