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