Audio Classification
Transformers
TensorBoard
Safetensors
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use rendy-k/audio_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rendy-k/audio_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="rendy-k/audio_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("rendy-k/audio_classification") model = AutoModelForAudioClassification.from_pretrained("rendy-k/audio_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from rendy-k/audio_classification: direct link, hf CLI and curl.
- Browser
- Download file 4.6 kB
-
https://huggingface.co/rendy-k/audio_classification/resolve/main/training_args.bin
- Command line
-
hf download hf://rendy-k/audio_classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/rendy-k/audio_classification/resolve/main/training_args.bin
4.6 kB
- Xet hash:
- d1e3f8cc215fa2e58a3e3649605815b4a334a09d9e9b14926adf102ea0a3b461
- Size of remote file:
- 4.6 kB
- SHA256:
- 89142d7a832a396c1bd709fd14710800cc1efed038038f1236dd83e26925d88f
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