Audio Classification
Transformers
TensorBoard
Safetensors
hubert
Generated from Trainer
Eval Results (legacy)
Instructions to use thelikhit/hubert-model-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thelikhit/hubert-model-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="thelikhit/hubert-model-v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("thelikhit/hubert-model-v2") model = AutoModelForAudioClassification.from_pretrained("thelikhit/hubert-model-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from thelikhit/hubert-model-v2: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/thelikhit/hubert-model-v2/resolve/main/training_args.bin
- Command line
-
hf download hf://thelikhit/hubert-model-v2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/thelikhit/hubert-model-v2/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- cb3f42827275318be55c9f0029fdbf5c0fa7bfd266baabb6cfda555a2cad87a1
- Size of remote file:
- 5.3 kB
- SHA256:
- 4deaaac84d711efff17dc5370c2252e2aa24206c62fbb82401f75ec560408cfa
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.