Instructions to use puranjanaaa/wav_analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use puranjanaaa/wav_analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="puranjanaaa/wav_analysis")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("puranjanaaa/wav_analysis") model = AutoModelForAudioClassification.from_pretrained("puranjanaaa/wav_analysis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from puranjanaaa/wav_analysis: direct link, hf CLI and curl.
- Browser
- Download file 3.31 kB
-
https://huggingface.co/puranjanaaa/wav_analysis/resolve/main/training_args.bin
- Command line
-
hf download hf://puranjanaaa/wav_analysis/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/puranjanaaa/wav_analysis/resolve/main/training_args.bin
3.31 kB
- Xet hash:
- 1693a90044f6eed527573ece2d46086cf06aa221d1944ea7c1150fbaf71860e1
- Size of remote file:
- 3.31 kB
- SHA256:
- 10593f8cf6bdf134a33393202ea3774c1bb70b5752cd0f3de27dca8da80b02cc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.