Init

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+example.wav filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,12 +1,76 @@
 ---
-title: Demucs
-emoji: 📚
-colorFrom: green
-colorTo: yellow
+title: Demucs Stem Separator
+emoji: 🎵
+colorFrom: purple
+colorTo: pink
 sdk: gradio
 sdk_version: 6.3.0
+python_version: "3.10"
 app_file: app.py
 pinned: false
+license: mit
+tags:
+  - audio-separation
+  - stem-separation
+  - demucs
+  - mcp-server
+short_description: Separate audio into vocals, drums, bass, other stems
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🎵 Demucs Stem Separator
+
+Separate audio → **Vocals** | **Drums** | **Bass** | **Other**
+
+| Stem | Output |
+|------|--------|
+| 🎤 Vocals | Singing, voice |
+| 🥁 Drums | Percussion |
+| 🎸 Bass | Low frequencies |
+| 🎹 Other | Guitars, synths |
+
+**Formats:** MP3, WAV, FLAC, OGG, M4A
+
+---
+
+## API
+
+### Python Client
+
+```python
+from gradio_client import Client, handle_file
+
+client = Client("Luminia/demucs")
+vocals, drums, bass, other = client.predict(
+    audio_file=handle_file("song.wav"),
+    model_name="htdemucs",  # or "htdemucs_ft"
+    api_name="/separate"
+)
+```
+
+### REST API (curl)
+
+```bash
+# Submit
+curl -X POST "https://luminia-demucs.hf.space/gradio_api/call/separate" \
+  -H "Content-Type: application/json" \
+  -d '{"data": ["https://example.com/song.mp3", "htdemucs"]}'
+
+# Get result (use event_id from response)
+curl "https://luminia-demucs.hf.space/gradio_api/call/separate/{event_id}"
+```
+
+### MCP
+
+```json
+{
+  "mcpServers": {
+    "demucs": {
+      "url": "https://luminia-demucs.hf.space/gradio_api/mcp/"
+    }
+  }
+}
+```
+
+---
+
+[Demucs](https://github.com/facebookresearch/demucs) by Facebook Research

app.py

@@ -0,0 +1,111 @@
+"""Demucs Audio Stem Separator - Separate audio into vocals, drums, bass, other."""
+import os
+import tempfile
+import gradio as gr
+import torch
+import soundfile as sf
+import numpy as np
+import librosa
+from demucs.pretrained import get_model
+from demucs.apply import apply_model
+
+device = torch.device("cpu")
+_model_cache = {}
+
+
+def load_model(model_name: str):
+    """Load and cache Demucs model."""
+    if model_name not in _model_cache:
+        model = get_model(model_name)
+        model.to(device).eval()
+        _model_cache[model_name] = model
+    return _model_cache[model_name]
+
+
+def separate_stems(audio_file: str, model_name: str = "htdemucs", progress=gr.Progress()) -> tuple[str, str, str, str]:
+    """
+    Separate audio into stems using Demucs.
+
+    Args:
+        audio_file: Path to the input audio file (MP3, WAV, FLAC, OGG, M4A)
+        model_name: Demucs model - htdemucs (fast) or htdemucs_ft (better quality)
+
+    Returns:
+        Tuple of paths to separated stems: (vocals, drums, bass, other)
+    """
+    if audio_file is None:
+        raise gr.Error("Please upload an audio file")
+
+    progress(0.1, desc="Loading model...")
+    model = load_model(model_name)
+
+    progress(0.2, desc="Loading audio...")
+    audio_data, sr = sf.read(audio_file)
+    if audio_data.ndim == 1:
+        waveform = torch.from_numpy(audio_data.astype(np.float32)).unsqueeze(0)
+    else:
+        waveform = torch.from_numpy(audio_data.T.astype(np.float32))
+
+    target_sr = model.samplerate
+    if sr != target_sr:
+        progress(0.25, desc="Resampling...")
+        waveform_np = waveform.numpy()
+        resampled = [librosa.resample(waveform_np[ch], orig_sr=sr, target_sr=target_sr)
+                     for ch in range(waveform_np.shape[0])]
+        waveform = torch.from_numpy(np.stack(resampled))
+
+    if waveform.shape[0] == 1:
+        waveform = waveform.repeat(2, 1)
+    elif waveform.shape[0] > 2:
+        waveform = waveform[:2, :]
+
+    waveform = waveform.unsqueeze(0).to(device)
+
+    progress(0.3, desc="Separating stems...")
+    with torch.no_grad():
+        sources = apply_model(model, waveform, device=device, progress=True)
+
+    progress(0.9, desc="Saving...")
+    output_dir = tempfile.mkdtemp()
+    output_files = {}
+    for i, name in enumerate(model.sources):
+        stem = sources[0, i].cpu().numpy()
+        path = os.path.join(output_dir, f"{name}.wav")
+        sf.write(path, stem.T, target_sr)
+        output_files[name] = path
+
+    return output_files.get("vocals"), output_files.get("drums"), output_files.get("bass"), output_files.get("other")
+
+
+with gr.Blocks(title="Demucs Stem Separator") as demo:
+    gr.Markdown("# 🎵 Demucs Stem Separator\nSeparate audio → **Vocals** | **Drums** | **Bass** | **Other**")
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            audio_input = gr.Audio(label="Audio File", type="filepath", sources=["upload"])
+            model_choice = gr.Dropdown(["htdemucs", "htdemucs_ft"], value="htdemucs", label="Model",
+                                       info="htdemucs: Fast | htdemucs_ft: Better quality")
+            separate_btn = gr.Button("Separate Stems", variant="primary", size="lg")
+
+        with gr.Column(scale=2):
+            with gr.Row():
+                vocals = gr.Audio(label="🎤 Vocals", type="filepath")
+                drums = gr.Audio(label="🥁 Drums", type="filepath")
+            with gr.Row():
+                bass = gr.Audio(label="🎸 Bass", type="filepath")
+                other = gr.Audio(label="🎹 Other", type="filepath")
+
+    separate_btn.click(separate_stems, [audio_input, model_choice], [vocals, drums, bass, other], api_name="separate")
+
+    gr.Examples(
+        examples=[["example.wav", "htdemucs"]],
+        inputs=[audio_input, model_choice],
+        outputs=[vocals, drums, bass, other],
+        fn=separate_stems,
+        cache_examples=True,
+        cache_mode="lazy",
+        label="Example"
+    )
+
+if __name__ == "__main__":
+    demo.launch(mcp_server=True, show_error=True)

example.wav

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:837226b7f3ec5a51a5bb735c0aca0f354864001124aa535dc74f20af6ac14454
+size 2872398

requirements.txt

@@ -0,0 +1,7 @@
+--extra-index-url https://download.pytorch.org/whl/cpu
+torch
+demucs
+gradio>=6.3.0
+numpy
+soundfile
+librosa