Audio Classification
Transformers
TensorBoard
Safetensors
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use engelbertavania/audio_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use engelbertavania/audio_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="engelbertavania/audio_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("engelbertavania/audio_classification") model = AutoModelForAudioClassification.from_pretrained("engelbertavania/audio_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from engelbertavania/audio_classification: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/engelbertavania/audio_classification/resolve/main/training_args.bin
- Command line
-
hf download hf://engelbertavania/audio_classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/engelbertavania/audio_classification/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- 496b7bfee5d85c1845c47fea977d5f1a46defab683138b9d74987aa4cb62e33f
- Size of remote file:
- 5.11 kB
- SHA256:
- 764db755e143c93d4f7d2aee5dd7a3067cdf2b57c593857f9e3796be087e5581
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