Audio Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
audio-spectrogram-transformer
Generated from Trainer
Instructions to use bookbot/distil-ast-audioset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bookbot/distil-ast-audioset with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="bookbot/distil-ast-audioset")# Load model directly from transformers import AutoFeatureExtractor, AutoModelForAudioClassification extractor = AutoFeatureExtractor.from_pretrained("bookbot/distil-ast-audioset") model = AutoModelForAudioClassification.from_pretrained("bookbot/distil-ast-audioset", device_map="auto") - Notebooks
- Google Colab
- Kaggle
update model card README.md
Browse files
README.md
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---
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license: bsd-3-clause
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tags:
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- generated_from_trainer
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metrics:
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- f1
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# distil-ast-audioset-2
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This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the
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It achieves the following results on the evaluation set:
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- Loss: 0.3063
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- F1: 0.4876
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---
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license: bsd-3-clause
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tags:
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- audio-classification
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- generated_from_trainer
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metrics:
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- f1
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# distil-ast-audioset-2
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This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the bookbot/audioset dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3063
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- F1: 0.4876
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