app.py

import gradio as gr
import torch
from model import ECAPA_gender

# Load the model
model = ECAPA_gender.from_pretrained('JaesungHuh/ecapa-gender')
model.eval()

def predict_gender(filepath):
    audio = model.load_audio(filepath)
    with torch.no_grad():
        output = model.forward(audio)
        probs = torch.softmax(output, dim=1)
        prob_dict = {'Human ' + model.pred2gender[i]: float(prob) for i, prob in enumerate(probs[0])}
    return prob_dict

audio_component = gr.Audio(type='filepath', label='Upload your audio file here')
label_component = gr.Label(label='Gender classification result')

examples = [f'{i:05d}.wav' for i in range(1, 13)]

demo = gr.Interface(fn=predict_gender, inputs=audio_component, outputs=label_component, examples=examples)
demo.launch()