Initial commit

app.py

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+from transformers import pipeline
+import librosa
+import json
+import gradio as gr
+
+
+def audio_pipeline(file_path: str, top_k: int = 7) -> dict[str, float]:
+    y, _ = librosa.load(file_path, sr=config['sampling_rate'])
+    out = pipe(y, top_k=top_k)
+    print(out)
+    return {clas['label']: clas['score'] for clas in out}
+
+
+with open('config.json', 'r') as f:
+    config = json.load(f)
+
+pipe = pipeline("audio-classification", model=config['models_path'])
+
+demo = gr.Interface(
+    fn=audio_pipeline,
+    inputs=[gr.Audio(type="filepath"), gr.Slider(1, 10, 1,
+                                                 label="Top K Results")],
+    outputs=gr.Label(num_top_classes=7),
+    title="Music Mind Audio Classification",
+    description="Upload an .mp3 or .ogg audio file "
+    "to classify the content using a pre-trained model.")
+
+demo.launch(debug=True)

requirements.txt

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+torch
+datasets
+transformers[torch]
+evaluate
+numpy
+librosa
+soundfile