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Update app.py
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app.py
CHANGED
@@ -357,9 +357,9 @@ with gr.Blocks(
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with gr.Row():
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mdx_model = gr.Dropdown(label="Select the Model", choices=MDXNET_MODELS)
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with gr.Row():
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mdx_hop_length = gr.Slider(minimum=32, maximum=2048, step=32, value=1024, label="Hop Length", info"Usually called stride in neural networks; only change if you know what you're doing.")
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mdx_seg_size = gr.Slider(minimum=32, maximum=4000, step=32, value=256, label="Segment Size", info="Larger consumes more resources, but may give better results.")
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mdx_overlap = gr.Slider(minimum=0.001, maximum=0.999, step=0.001, value=0.25, label="Overlap", info"Amount of overlap between prediction windows. Higher is better but slower.")
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mdx_batch_size = gr.Slider(minimum=1, maximum=16, step=1, value=1, label="Batch Size", info="Larger consumes more RAM but may process slightly faster.", interactive=False)
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mdx_denoise = gr.Checkbox(value=False, label="Denoise", info="Enable denoising after separation.")
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with gr.Row():
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with gr.Row():
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mdx_model = gr.Dropdown(label="Select the Model", choices=MDXNET_MODELS)
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with gr.Row():
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mdx_hop_length = gr.Slider(minimum=32, maximum=2048, step=32, value=1024, label="Hop Length", info="Usually called stride in neural networks; only change if you know what you're doing.")
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mdx_seg_size = gr.Slider(minimum=32, maximum=4000, step=32, value=256, label="Segment Size", info="Larger consumes more resources, but may give better results.")
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mdx_overlap = gr.Slider(minimum=0.001, maximum=0.999, step=0.001, value=0.25, label="Overlap", info="Amount of overlap between prediction windows. Higher is better but slower.")
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mdx_batch_size = gr.Slider(minimum=1, maximum=16, step=1, value=1, label="Batch Size", info="Larger consumes more RAM but may process slightly faster.", interactive=False)
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mdx_denoise = gr.Checkbox(value=False, label="Denoise", info="Enable denoising after separation.")
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with gr.Row():
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