feat: add music genre classifier using DistilHuBERT

- Load lewtun/distilhubert-finetuned-gtzan model
- Create Gradio interface for audio classification
- Return top 5 genre predictions in JSON format
- Enable API endpoint at /api/predict

app.py

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+import gradio as gr
+from transformers import pipeline
+import numpy as np
+
+# 加载音乐分类模型
+model_name = "lewtun/distilhubert-finetuned-gtzan"
+classifier = pipeline("audio-classification", model=model_name)
+
+def classify_audio(audio_path):
+    """
+    分类音频文件并返回前5个预测结果
+    """
+    try:
+        # 使用 pipeline 进行预测
+        predictions = classifier(audio_path, top_k=5)
+        
+        # 格式化结果
+        results = {
+            "top1": predictions[0],
+            "top5": predictions
+        }
+        
+        return results
+    except Exception as e:
+        return {"error": str(e)}
+
+# 创建 Gradio 界面
+demo = gr.Interface(
+    fn=classify_audio,
+    inputs=gr.Audio(type="filepath", label="Upload Music File"),
+    outputs=gr.JSON(label="Classification Results"),
+    title="Music Genre Classifier",
+    description="Upload a music file to classify its genre using DistilHuBERT fine-tuned on GTZAN dataset.",
+    examples=[],
+    api_name="predict"  # 这将创建一个 /api/predict endpoint
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio==4.44.0
+transformers==4.45.0
+torch==2.4.0
+torchaudio==2.4.0
+librosa==0.10.2
+soundfile==0.12.1