| 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() |
