Create app.py

app.py

@@ -0,0 +1,20 @@
+import gradio as gr
+import librosa
+from transformers import pipeline
+
+pipe = pipeline("audio-classification", model="lewtun/distilhubert-finetuned-gtzan-og")
+
+
+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
+
+
+label = gr.outputs.Label()
+
+demo = gr.Interface(fn=classify_audio, inputs=gr.Audio(type="filepath"), outputs=label)
+demo.launch()