Create app.py

app.py

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+import os
+import shutil
+import subprocess
+import tempfile
+from pathlib import Path
+
+import gradio as gr
+import torchaudio
+import torch
+
+
+# ================================
+# CONFIG
+# ================================
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+MODEL = "htdemucs"  # best quality vocal separation
+
+
+# ================================
+# SPLIT FUNCTION
+# ================================
+def split_vocals(audio_file):
+    if audio_file is None:
+        return None, None, "❌ Please upload an audio file."
+
+    work_dir = tempfile.mkdtemp(prefix="demucs_")
+
+    try:
+        print(f"[Demucs] Input: {audio_file}")
+        print(f"[Demucs] Device: {DEVICE}")
+
+        # Run Demucs
+        result = subprocess.run(
+            [
+                "python", "-m", "demucs",
+                "--two-stems", "vocals",  # only split vocals vs everything else
+                "-n", MODEL,
+                "-o", work_dir,
+                audio_file,
+            ],
+            capture_output=True,
+            text=True,
+        )
+
+        if result.returncode != 0:
+            error = result.stderr[-1000:]  # last 1000 chars to avoid huge dumps
+            print(f"[Demucs] Error:\n{error}")
+            return None, None, f"❌ Demucs failed:\n{error}"
+
+        # Find output files
+        track_name = Path(audio_file).stem
+        demucs_out = Path(work_dir) / MODEL / track_name
+
+        vocals_src = demucs_out / "vocals.wav"
+        background_src = demucs_out / "no_vocals.wav"
+
+        if not vocals_src.exists() or not background_src.exists():
+            return None, None, "❌ Demucs ran but output files not found."
+
+        # Copy to stable paths
+        vocals_dst = str(Path(work_dir) / "vocals.wav")
+        background_dst = str(Path(work_dir) / "background.wav")
+
+        shutil.copy2(vocals_src, vocals_dst)
+        shutil.copy2(background_src, background_dst)
+
+        print(f"[Demucs] ✅ Done. Vocals: {vocals_dst}")
+        return vocals_dst, background_dst, "✅ Split complete!"
+
+    except Exception as e:
+        return None, None, f"❌ Exception: {str(e)}"
+
+
+# ================================
+# GRADIO UI
+# ================================
+with gr.Blocks(title="CleanSong AI — Demucs Splitter") as demo:
+
+    gr.Markdown("# 🎸 CleanSong AI — Vocal Splitter")
+    gr.Markdown(
+        "Upload a song → get the isolated **vocals** and **background** tracks back separately. "
+        "Powered by Demucs `htdemucs` model."
+    )
+
+    with gr.Row():
+        with gr.Column():
+            audio_input = gr.Audio(
+                label="Upload Song",
+                type="filepath",
+                sources=["upload"],
+            )
+            split_btn = gr.Button("🎸 Split Vocals", variant="primary", size="lg")
+
+        with gr.Column():
+            status = gr.Textbox(label="Status", interactive=False)
+            vocals_out = gr.Audio(label="Vocals Only", type="filepath")
+            background_out = gr.Audio(label="Background / Instrumental", type="filepath")
+
+    split_btn.click(
+        fn=split_vocals,
+        inputs=[audio_input],
+        outputs=[vocals_out, background_out, status],
+    )
+
+    gr.Markdown(
+        """
+        ---
+        **Part of the CleanSong AI pipeline.**
+        - Upload your song here first to get isolated vocals
+        - Feed the vocals into the Whisper Transcriber Space
+        - The background track is kept intact for the final remix
+        """
+    )
+
+if __name__ == "__main__":
+    demo.launch()