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from transformers import pipeline |
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model_id = "arham061/distilhubert-finetuned-PASCAL_Dataset_Augmented" |
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pipe = pipeline("audio-classification", model=model_id) |
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def classify_audio(filepath): |
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preds = pipe(filepath) |
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outputs = {} |
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for p in preds: |
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outputs[p["label"]] = p["score"] |
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return outputs |
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import gradio as gr |
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demo = gr.Interface( |
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fn=classify_audio, |
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inputs=gr.Audio(type="filepath"), |
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outputs="label", |
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examples = ['normal.wav', 'murmur.wav', 'extra_systole.wav', 'extra_hystole.wav', 'artifact.wav'], |
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) |
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demo.launch(debug=True) |