Instructions to use alkiskoudounas/wav2vec2-base-fsc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alkiskoudounas/wav2vec2-base-fsc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="alkiskoudounas/wav2vec2-base-fsc")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("alkiskoudounas/wav2vec2-base-fsc") model = AutoModelForAudioClassification.from_pretrained("alkiskoudounas/wav2vec2-base-fsc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Updated README
Browse files
README.md
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# wav2vec2-base-FSC
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This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the
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It achieves the following results on the test set:
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- Accuracy: 0.994
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# wav2vec2-base-FSC
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This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the FSC dataset for the intent classification task.
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It achieves the following results on the test set:
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- Accuracy: 0.994
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