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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() |