Update gui.py

gui.py

@@ -510,26 +510,26 @@ def create_interface():
                         roformer_stem2 = gr.Audio(label="🥁 Stem 2", type="filepath", interactive=False)
 
             # Auto Ensemble Sekmesi
-            with gr.Tab("🎚️ Auto Ensemble"):
-                with gr.Group(elem_classes="dubbing-theme"):
-                    gr.Markdown("### Ensemble Processing")
-                    with gr.Row():
-                        ensemble_audio = gr.Audio(label="🎧 Upload Audio", type="filepath", interactive=True)
-                        url_ensemble = gr.Textbox(label="🔗 Or Paste URL", placeholder="YouTube or audio URL", interactive=True)
-                        download_ensemble = gr.Button("⬇️ Download", variant="secondary")
-                    ensemble_exclude_stems = gr.Textbox(label="🚫 Exclude Stems", placeholder="e.g., vocals, drums (comma-separated)", interactive=True)
-                    with gr.Row():
-                        ensemble_category = gr.Dropdown(label="📚 Category", choices=list(ROFORMER_MODELS.keys()), value="Instrumentals", interactive=True)
-                        ensemble_models = gr.CheckboxGroup(label="🛠️ Models", choices=list(ROFORMER_MODELS["Instrumentals"].keys()), interactive=True)
-                    with gr.Row():
-                        ensemble_seg_size = gr.Slider(32, 4000, value=256, step=32, label="📏 Segment Size", interactive=True)
-                        ensemble_overlap = gr.Slider(2, 10, value=8, step=1, label="🔄 Overlap", interactive=True)
-                        ensemble_use_tta = gr.Checkbox(value=False, label="🔍 Use TTA", interactive=True)
-                    ensemble_method = gr.Dropdown(label="⚙️ Ensemble Method", choices=['avg_wave', 'median_wave', 'max_wave', 'min_wave', 'avg_fft', 'median_fft', 'max_fft', 'min_fft'], value='avg_wave', interactive=True)
-                    ensemble_weights = gr.Textbox(label="⚖️ Weights", placeholder="e.g., 1.0, 1.0 (comma-separated)", interactive=True)
-                    ensemble_button = gr.Button("🎛️ Run Ensemble!", variant="primary")
-                    ensemble_output = gr.Audio(label="🎶 Ensemble Result", type="filepath", interactive=False)
-                    ensemble_status = gr.Textbox(label="📢 Status", interactive=False)
+            #with gr.Tab("🎚️ Auto Ensemble"):
+            #    with gr.Group(elem_classes="dubbing-theme"):
+            #        gr.Markdown("### Ensemble Processing")
+            #        with gr.Row():
+            #            ensemble_audio = gr.Audio(label="🎧 Upload Audio", type="filepath", interactive=True)
+            #            url_ensemble = gr.Textbox(label="🔗 Or Paste URL", placeholder="YouTube or audio URL", interactive=True)
+            #            download_ensemble = gr.Button("⬇️ Download", variant="secondary")
+            #        ensemble_exclude_stems = gr.Textbox(label="🚫 Exclude Stems", placeholder="e.g., vocals, drums (comma-separated)", interactive=True)
+            #        with gr.Row():
+            #            ensemble_category = gr.Dropdown(label="📚 Category", choices=list(ROFORMER_MODELS.keys()), value="Instrumentals", interactive=True)
+            #            ensemble_models = gr.CheckboxGroup(label="🛠️ Models", choices=list(ROFORMER_MODELS["Instrumentals"].keys()), interactive=True)
+            #        with gr.Row():
+            #            ensemble_seg_size = gr.Slider(32, 4000, value=256, step=32, label="📏 Segment Size", interactive=True)
+            #            ensemble_overlap = gr.Slider(2, 10, value=8, step=1, label="🔄 Overlap", interactive=True)
+            #            ensemble_use_tta = gr.Checkbox(value=False, label="🔍 Use TTA", interactive=True)
+            #        ensemble_method = gr.Dropdown(label="⚙️ Ensemble Method", choices=['avg_wave', 'median_wave', 'max_wave', 'min_wave', 'avg_fft', 'median_fft', 'max_fft', 'min_fft'], value='avg_wave', interactive=True)
+            #        ensemble_weights = gr.Textbox(label="⚖️ Weights", placeholder="e.g., 1.0, 1.0 (comma-separated)", interactive=True)
+            #        ensemble_button = gr.Button("🎛️ Run Ensemble!", variant="primary")
+            #        ensemble_output = gr.Audio(label="🎶 Ensemble Result", type="filepath", interactive=False)
+            #        ensemble_status = gr.Textbox(label="📢 Status", interactive=False)
 
         gr.HTML("<div class='footer'>Powered by Audio-Separator 🌟🎶 | Made with ❤️</div>")
 
@@ -541,16 +541,16 @@ def create_interface():
             inputs=[roformer_audio, roformer_model, roformer_seg_size, roformer_override_seg_size, roformer_overlap, roformer_pitch_shift, model_file_dir, output_dir, output_format, norm_threshold, amp_threshold, batch_size, roformer_exclude_stems],
             outputs=[roformer_stem1, roformer_stem2]
         )
-        ensemble_category.change(update_ensemble_models, inputs=[ensemble_category], outputs=[ensemble_models])
-        download_ensemble.click(fn=download_audio, inputs=[url_ensemble], outputs=[ensemble_audio])
-        ensemble_button.click(
-            lambda *args: auto_ensemble_process(
-                *args[:-1],
-                weights=[float(w.strip()) for w in args[-1].split(',')] if args[-1] else None
-            ),
-            inputs=[ensemble_audio, ensemble_models, ensemble_seg_size, ensemble_overlap, output_format, ensemble_use_tta, model_file_dir, output_dir, norm_threshold, amp_threshold, batch_size, ensemble_method, ensemble_exclude_stems, ensemble_weights],
-            outputs=[ensemble_output, ensemble_status]
-        )
+      #  ensemble_category.change(update_ensemble_models, inputs=[ensemble_category], outputs=[ensemble_models])
+      #  download_ensemble.click(fn=download_audio, inputs=[url_ensemble], outputs=[ensemble_audio])
+      #  ensemble_button.click(
+      #      lambda *args: auto_ensemble_process(
+      #          *args[:-1],
+      #          weights=[float(w.strip()) for w in args[-1].split(',')] if args[-1] else None
+      #      ),
+      #      inputs=[ensemble_audio, ensemble_models, ensemble_seg_size, ensemble_overlap, output_format, ensemble_use_tta, model_file_dir, output_dir, norm_threshold, amp_threshold, batch_size, ensemble_method, ensemble_exclude_stems, ensemble_weights],
+      #      outputs=[ensemble_output, ensemble_status]
+      #  )
     
     return app