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import gradio as gr |
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import torchaudio |
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from transformers import pipeline, AudioClassificationPipeline |
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pipe = pipeline("audio-classification", model="cogniveon/eeem069_heart_murmur_classification") |
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def predict(audio): |
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sampling_rate, data = audio |
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waveform = torch.tensor(data).float() |
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if sampling_rate != 16000: |
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resampler = torchaudio.transforms.Resample(sampling_rate, 16000) |
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waveform = resampler(waveform) |
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results = pipe(waveform.numpy()) |
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sorted_results = sorted(results, key=lambda x: x['score'], reverse=True) |
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label_scores = {item['label']: item['score'] for item in sorted_results} |
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return label_scores |
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gr.Interface( |
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fn=predict, |
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inputs="audio", |
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outputs="label" |
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).launch() |
