new ui

README.md

@@ -1,12 +1,80 @@
 ---
-title: HT Demucs Stem Separation 2025
-emoji: 👁
-colorFrom: gray
-colorTo: gray
+title: HT-Demucs Spleeter Music Stem Separation - AI Audio Source Separation 2025
+emoji: 🎵
+colorFrom: blue
+colorTo: purple
 sdk: gradio
 sdk_version: 5.39.0
 app_file: app.py
 pinned: false
+license: mit
+short_description: HT-Demucs and Spleeter AI music stem separation - separate vocals, drums, bass, piano, other instruments
+tags:
+  - music
+  - audio
+  - stem-separation
+  - htdemucs
+  - spleeter
+  - ai
+  - machine-learning
+  - vocals
+  - drums
+  - bass
+  - piano
+  - audio-processing
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🎵 HT-Demucs Spleeter Music Stem Separation - AI Audio Source Separation
+
+A powerful AI-powered music stem separation tool that runs both **HT-Demucs** and **Spleeter** models. Choose which models to run or compare both simultaneously to get the best quality stems for vocals, drums, bass, piano, and other instruments.
+
+## 🚀 Features
+
+### HT-Demucs Model
+- **Drums** - High-quality drum separation
+- **Bass** - Clean bass line extraction  
+- **Other** - All other instruments
+- **Vocals** - Vocal track isolation
+
+### Spleeter Model  
+- **Vocals** - Vocal track isolation
+- **Drums** - Drum track separation
+- **Bass** - Bass line extraction
+- **Other** - Other instruments
+- **Piano** - 🎹 **Piano separation (unique to Spleeter!)**
+
+## 🎯 Why Use Both Models?
+
+- **HT-Demucs**: Excellent for general stem separation with high quality
+- **Spleeter**: Provides piano separation that HT-Demucs doesn't offer
+- **Comparison**: Side-by-side results help you choose the best quality stems
+- **Flexibility**: Get the best of both worlds!
+
+## 🛠️ How to Use
+
+1. Upload your audio file (MP3, WAV, etc.)
+2. **Choose your models**: Select HT-Demucs, Spleeter, or both (default: both)
+3. Click "Separate Music"
+4. Compare the results and download the best stems for your project
+
+## 📊 Model Comparison
+
+| Feature | HT-Demucs | Spleeter |
+|---------|-----------|----------|
+| Vocals | ✅ High Quality | ✅ High Quality |
+| Drums | ✅ High Quality | ✅ High Quality |
+| Bass | ✅ High Quality | ✅ High Quality |
+| Other | ✅ High Quality | ✅ High Quality |
+| Piano | ❌ Not Available | ✅ **Available** |
+| Speed | ⚡ Fast | ⚡ Fast |
+
+## 🎼 Perfect For
+
+- Music producers comparing stem quality
+- Remix artists needing piano separation
+- Audio engineers testing different models
+- Anyone who wants the best possible stem separation
+
+---
+
+*Powered by HT-Demucs and Spleeter - Choose your best stems!*

app.py

@@ -6,28 +6,42 @@ import torchaudio
 from demucs.pretrained import get_model
 from demucs.apply import apply_model
 import os
-
-# --- Setup the model ---
-print("Setting up the model...")
+import tempfile
+import numpy as np
+from spleeter.separator import Separator
+from spleeter.audio.adapter import AudioAdapter
+import warnings
+warnings.filterwarnings("ignore")
+
+# --- Setup the models ---
+print("Setting up models...")
 device = 'cuda' if torch.cuda.is_available() else 'cpu'
 print(f"Using device: {device}")
 
-model = get_model(name="htdemucs")
-model = model.to(device)
-model.eval()
-print("Model loaded successfully.")
-
-# --- Separation function ---
-def separate_stems(audio_path):
+# Load HT-Demucs model
+print("Loading HT-Demucs model...")
+htdemucs_model = get_model(name="htdemucs")
+htdemucs_model = htdemucs_model.to(device)
+htdemucs_model.eval()
+print("HT-Demucs model loaded successfully.")
+
+# Load Spleeter model (5stems-16kHz)
+print("Loading Spleeter model...")
+spleeter_separator = Separator('spleeter:5stems-16kHz')
+spleeter_audio_adapter = AudioAdapter.default()
+print("Spleeter model loaded successfully.")
+
+# --- HT-Demucs separation function ---
+def separate_with_htdemucs(audio_path):
     """
-    Separates an audio file into drums, bass, other, and vocals.
-    Returns FILE PATHS (not base64).
+    Separates an audio file using HT-Demucs into drums, bass, other, and vocals.
+    Returns FILE PATHS.
     """
     if audio_path is None:
         return None, None, None, None, "Please upload an audio file."
 
     try:
-        print(f"Loading audio from: {audio_path}")
+        print(f"HT-Demucs: Loading audio from: {audio_path}")
         wav, sr = torchaudio.load(audio_path)
 
         if wav.shape[0] == 1:
@@ -36,14 +50,14 @@ def separate_stems(audio_path):
 
         wav = wav.to(device)
 
-        print("Applying the separation model...")
+        print("HT-Demucs: Applying the separation model...")
         with torch.no_grad():
-            sources = apply_model(model, wav[None], device=device, progress=True)[0]
-        print("Separation complete.")
+            sources = apply_model(htdemucs_model, wav[None], device=device, progress=True)[0]
+        print("HT-Demucs: Separation complete.")
 
         # Save stems temporarily
         stem_names = ["drums", "bass", "other", "vocals"]
-        output_dir = "separated_stems"
+        output_dir = "htdemucs_stems"
         os.makedirs(output_dir, exist_ok=True)
 
         output_paths = []
@@ -51,38 +65,189 @@ def separate_stems(audio_path):
             out_path = os.path.join(output_dir, f"{name}.wav")
             torchaudio.save(out_path, sources[i].cpu(), sr)
             output_paths.append(out_path)
-            print(f"✅ Saved {name} to {out_path}")
+            print(f"✅ HT-Demucs saved {name} to {out_path}")
 
-        return output_paths[0], output_paths[1], output_paths[2], output_paths[3], "✅ Separation successful!"
+        return output_paths[0], output_paths[1], output_paths[2], output_paths[3], "✅ HT-Demucs separation successful!"
 
     except Exception as e:
-        print(f"Error: {e}")
-        return None, None, None, None, f"❌ Error: {str(e)}"
+        print(f"HT-Demucs Error: {e}")
+        return None, None, None, None, f"❌ HT-Demucs Error: {str(e)}"
+
+# --- Spleeter separation function ---
+def separate_with_spleeter(audio_path):
+    """
+    Separates an audio file using Spleeter into vocals, drums, bass, other, and piano.
+    Returns FILE PATHS.
+    """
+    if audio_path is None:
+        return None, None, None, None, None, "Please upload an audio file."
+
+    try:
+        print(f"Spleeter: Loading audio from: {audio_path}")
+        
+        # Load audio with Spleeter
+        waveform, _ = spleeter_audio_adapter.load(audio_path)
+        
+        print("Spleeter: Applying the separation model...")
+        prediction = spleeter_separator.separate(waveform)
+        print("Spleeter: Separation complete.")
+
+        # Save stems temporarily
+        stem_names = ["vocals", "drums", "bass", "other", "piano"]
+        output_dir = "spleeter_stems"
+        os.makedirs(output_dir, exist_ok=True)
+
+        output_paths = []
+        for name in stem_names:
+            out_path = os.path.join(output_dir, f"{name}.wav")
+            # Convert to the right format and save
+            stem_audio = prediction[name]
+            spleeter_audio_adapter.save(out_path, stem_audio, 44100, 'wav', '16')
+            output_paths.append(out_path)
+            print(f"✅ Spleeter saved {name} to {out_path}")
+
+        return output_paths[0], output_paths[1], output_paths[2], output_paths[3], output_paths[4], "✅ Spleeter separation successful!"
+
+    except Exception as e:
+        print(f"Spleeter Error: {e}")
+        return None, None, None, None, None, f"❌ Spleeter Error: {str(e)}"
+
+# --- Combined separation function ---
+def separate_selected_models(audio_path, run_htdemucs, run_spleeter):
+    """
+    Separates an audio file using selected models (HT-Demucs, Spleeter, or both).
+    Returns stems from selected models.
+    """
+    if audio_path is None:
+        return [None] * 13, "Please upload an audio file."
+
+    if not run_htdemucs and not run_spleeter:
+        return [None] * 13, "❌ Please select at least one model to run."
+
+    try:
+        htdemucs_results = [None] * 5  # 4 stems + 1 status
+        spleeter_results = [None] * 6  # 5 stems + 1 status
+        status_messages = []
+        
+        # Run HT-Demucs if selected
+        if run_htdemucs:
+            print("Running HT-Demucs...")
+            htdemucs_results = separate_with_htdemucs(audio_path)
+            status_messages.append(htdemucs_results[-1])
+        
+        # Run Spleeter if selected
+        if run_spleeter:
+            print("Running Spleeter...")
+            spleeter_results = separate_with_spleeter(audio_path)
+            status_messages.append(spleeter_results[-1])
+        
+        # Combine results: HT-Demucs (4 stems) + Spleeter (5 stems) + status messages
+        all_results = list(htdemucs_results[:-1]) + list(spleeter_results[:-1]) + status_messages
+        
+        # Create combined status message
+        models_used = []
+        if run_htdemucs:
+            models_used.append("HT-Demucs")
+        if run_spleeter:
+            models_used.append("Spleeter")
+        
+        combined_status = f"🎵 {' + '.join(models_used)} completed!\n\n" + "\n".join(status_messages)
+        
+        return all_results + [combined_status]
+
+    except Exception as e:
+        print(f"Combined Error: {e}")
+        return [None] * 13, f"❌ Error: {str(e)}"
 
 # --- Gradio UI ---
 print("Creating Gradio interface...")
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# 🎵 Music Stem Separator with HT Demucs")
+with gr.Blocks(theme=gr.themes.Soft(), title="Music Stem Separator - HT-Demucs & Spleeter") as demo:
+    gr.Markdown("""
+    # 🎵 Music Stem Separator - HT-Demucs & Spleeter Comparison
+    
+    Upload your music and get stems from both **HT-Demucs** and **Spleeter** models!
+    
+    **HT-Demucs** provides: Drums, Bass, Other, Vocals  
+    **Spleeter** provides: Vocals, Drums, Bass, Other, **Piano** 🎹
+    
+    Compare the quality and choose the best stems for your needs!
+    """)
 
     with gr.Row():
+        with gr.Column(scale=1):
+            audio_input = gr.Audio(type="filepath", label="🎵 Upload Your Song")
+            
+            # Model selection toggles
+            gr.Markdown("### 🎛️ Select Models to Run")
+            with gr.Row():
+                htdemucs_toggle = gr.Checkbox(label="🎯 HT-Demucs", value=True, info="Drums, Bass, Other, Vocals")
+                spleeter_toggle = gr.Checkbox(label="🎵 Spleeter", value=True, info="Vocals, Drums, Bass, Other, Piano")
+            
+            separate_button = gr.Button("🚀 Separate Music", variant="primary", size="lg")
+            status_output = gr.Textbox(label="📊 Status", interactive=False, lines=4)
+
+    gr.Markdown("---")
+    
+    with gr.Row():
+        # HT-Demucs Results
         with gr.Column():
-            audio_input = gr.Audio(type="filepath", label="Upload Your Song")
-            separate_button = gr.Button("Separate Music", variant="primary")
-            status_output = gr.Textbox(label="Status", interactive=False)
-
+            gr.Markdown("### 🎯 HT-Demucs Results")
+            with gr.Row():
+                htdemucs_drums = gr.Audio(label="🥁 Drums", type="filepath")
+                htdemucs_bass = gr.Audio(label="🎸 Bass", type="filepath")
+            with gr.Row():
+                htdemucs_other = gr.Audio(label="🎼 Other", type="filepath")
+                htdemucs_vocals = gr.Audio(label="🎤 Vocals", type="filepath")
+        
+        # Spleeter Results
         with gr.Column():
-            gr.Markdown("### 🎧 Separated Stems")
-            drums_output = gr.Audio(label="Drums", type="filepath")
-            bass_output = gr.Audio(label="Bass", type="filepath")
-            other_output = gr.Audio(label="Other", type="filepath")
-            vocals_output = gr.Audio(label="Vocals", type="filepath")
-
+            gr.Markdown("### 🎵 Spleeter Results")
+            with gr.Row():
+                spleeter_vocals = gr.Audio(label="🎤 Vocals", type="filepath")
+                spleeter_drums = gr.Audio(label="🥁 Drums", type="filepath")
+            with gr.Row():
+                spleeter_bass = gr.Audio(label="🎸 Bass", type="filepath")
+                spleeter_other = gr.Audio(label="🎼 Other", type="filepath")
+            with gr.Row():
+                spleeter_piano = gr.Audio(label="🎹 Piano", type="filepath")
+
+    gr.Markdown("---")
+    
+    with gr.Row():
+        gr.Markdown("""
+        ### 📋 Model Comparison
+        
+        | Feature | HT-Demucs | Spleeter |
+        |---------|-----------|----------|
+        | **Vocals** | ✅ High Quality | ✅ High Quality |
+        | **Drums** | ✅ High Quality | ✅ High Quality |
+        | **Bass** | ✅ High Quality | ✅ High Quality |
+        | **Other** | ✅ High Quality | ✅ High Quality |
+        | **Piano** | ❌ Not Available | ✅ **Available** |
+        | **Speed** | ⚡ Fast | ⚡ Fast |
+        | **Quality** | 🏆 Excellent | 🏆 Excellent |
+        
+        **💡 Tip:** Use Spleeter when you need piano separation, HT-Demucs for other instruments!
+        """)
+
+    # Connect the button to the combined function
     separate_button.click(
-        fn=separate_stems,
-        inputs=audio_input,
-        outputs=[drums_output, bass_output, other_output, vocals_output, status_output]
+        fn=separate_selected_models,
+        inputs=[audio_input, htdemucs_toggle, spleeter_toggle],
+        outputs=[
+            htdemucs_drums, htdemucs_bass, htdemucs_other, htdemucs_vocals,  # HT-Demucs outputs
+            spleeter_vocals, spleeter_drums, spleeter_bass, spleeter_other, spleeter_piano,  # Spleeter outputs
+            status_output  # Status output
+        ]
     )
 
-    gr.Markdown("---\n<p style='text-align: center; font-size: small;'>Powered by HT Demucs</p>")
+    gr.Markdown("""
+    ---
+    <p style='text-align: center; font-size: small;'>
+    🚀 Powered by <strong>HT-Demucs</strong> & <strong>Spleeter</strong> | 
+    🎵 Compare and choose your best stems!
+    </p>
+    """)
 
-demo.launch(share=True)
+if __name__ == "__main__":
+    demo.launch(share=True)

requirements.txt

@@ -1 +1,3 @@
-git+https://github.com/adefossez/demucs
+git+https://github.com/adefossez/demucs
+spleeter==2.3.2
+tensorflow==2.13.0