Create app.py

app.py

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+import torch
+import torchaudio
+import gradio as gr
+import os
+from demucs.pretrained import get_model
+from demucs.apply import apply_model
+
+# Load mdx_extra model
+model = get_model('mdx_extra')
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+output_dir = "separated_mdx_extra"
+os.makedirs(output_dir, exist_ok=True)
+
+def separate_audio(audio_path):
+    wav, sr = torchaudio.load(audio_path)
+    wav = wav.to(device)
+
+    sources = apply_model(model, wav[None], device=device)
+
+    sources_dict = {}
+    stems = ["vocals", "instrumental"]
+
+    for source, stem in zip(sources[0], stems):
+        stem_path = os.path.join(output_dir, f"{stem}.wav")
+        torchaudio.save(stem_path, source.cpu(), sr)
+        sources_dict[stem] = stem_path
+
+    return sources_dict["vocals"], sources_dict["instrumental"]
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=separate_audio,
+    inputs=gr.Audio(type="filepath"),
+    outputs=[gr.Audio(label="Vocals"), gr.Audio(label="Instrumental")],
+    title="AI Music Separator (Demucs)",
+    description="Upload a song, and AI will separate vocals and instrumental."
+)
+
+if __name__ == "__main__":
+    interface.launch()