Instructions to use mclemcrew/distilhubert-finetuned-audio-effect-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mclemcrew/distilhubert-finetuned-audio-effect-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="mclemcrew/distilhubert-finetuned-audio-effect-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("mclemcrew/distilhubert-finetuned-audio-effect-classification") model = AutoModelForAudioClassification.from_pretrained("mclemcrew/distilhubert-finetuned-audio-effect-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from mclemcrew/distilhubert-finetuned-audio-effect-classification: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/mclemcrew/distilhubert-finetuned-audio-effect-classification/resolve/main/training_args.bin
- Command line
-
hf download hf://mclemcrew/distilhubert-finetuned-audio-effect-classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/mclemcrew/distilhubert-finetuned-audio-effect-classification/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- 1991ce98b80d501c52c856b487e16fa247732deb2ae61d3eb6ea242dcd19149b
- Size of remote file:
- 5.11 kB
- SHA256:
- b09b37fd8d0a83b8c718a13dece520c0b4433b1569b43f6343faaf5123fd9f7e
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