| import gradio as gr | |
| import torch | |
| from transformers import pipeline | |
| # Load the model from Hugging Face space | |
| model = pipeline(task="audio-classification", model="model.safetensors") | |
| def classify_audio(audio_file): | |
| # Perform inference using the Hugging Face model | |
| result = model(audio_file.read()) | |
| return result[0]["label"] | |
| # Gradio Interface | |
| iface = gr.Interface( | |
| fn=classify_audio, | |
| inputs=gr.Audio(type="file", label="Upload or Record Audio"), | |
| outputs=gr.Textbox(), | |
| live=True, | |
| ) | |
| # Launch the Gradio interface | |
| iface.launch() | |