app.py

import gradio as gr
from transformers import pipeline


model_id = "Teapack1/model_KWS"  # update with your model id
pipe = pipeline("audio-classification", model=model_id)


title = "Keyword Spotting Wav2Vec2"
description = "Gradio demo for finetuned Wav2Vec2 model on a custom dataset to perform keyword spotting task. Classes are scene 1, scene 2, scene 3, yes, no and stop."


def classify_audio(audio):
    predictions = audio_classifier(audio)
    return predictions

    
iface = gr.Interface(
    fn=classify_audio,
    inputs=gr.inputs.Audio(source="microphone", type="filepath", label="Record your audio"),
    outputs=gr.outputs.Label(),
    title="Audio Classification Demo",
    description="A simple demo to classify audio using a Hugging Face model."
)

iface.launch(debug=True, share=True)