init

app.py

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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-classifier")
+
+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"] ["jump_up.wav"], ["slap.wav"]],
+)
+
+demo.launch()

requirements.txt

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+torch
+transformers
+librosa
+numpy