| import gradio as gr | |
| import numpy as np | |
| import librosa | |
| from transformers import pipeline | |
| pipe = pipeline("audio-classification", model="TheDuyx/distilhubert-bass-classifier5") | |
| def classify_audio(filepath): | |
| audio, sampling_rate = librosa.load(filepath, sr=16_000) | |
| preds = pipe(audio) | |
| outputs = {} | |
| for p in preds: | |
| outputs[p["label"]] = p["score"] | |
| return outputs | |
| demo = gr.Interface( | |
| fn=classify_audio, | |
| inputs=gr.Audio(type="filepath"), | |
| outputs="label", | |
| examples=[["brass.wav"], ["growl.wav"], ["808.wav"], ["acid.wav"], ["slap.wav"]], | |
| ) | |
| demo.launch() |