| import gradio as gr | |
| from transformers import pipeline | |
| # Load model | |
| classifier = pipeline("audio-classification", model="yourusername/keyword-spotting-model") | |
| def predict(audio): | |
| preds = classifier(audio) | |
| return {p["label"]: p["score"] for p in preds} | |
| # Gradio UI | |
| gr.Interface( | |
| fn=predict, | |
| inputs=gr.Audio(source="microphone", type="filepath"), | |
| outputs=gr.Label(num_top_classes=3), | |
| title="🔊 Keyword Spotting", | |
| examples=["example1.wav", "example2.wav"] | |
| ).launch() |