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app.py
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@@ -156,16 +156,6 @@ def update_ito_output(all_results, selected_step):
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return (args.sample_rate, current_output), ito_param_output, selected_result['log']
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# Define the path to the examples folder
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EXAMPLES_DIR = "examples/"
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example_files = [f for f in os.listdir(EXAMPLES_DIR) if f.endswith('.mp3')]
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# Create lists for input and reference examples
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input_examples = [f"input_{i}.mp3" for i in range(1, len(example_files)//2 + 1)]
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reference_examples = [f"reference_{i}.mp3" for i in range(1, len(example_files)//2 + 1)]
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""" APP display """
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with gr.Blocks() as demo:
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gr.Markdown("# ITO-Master: Inference Time Optimization for Mastering Style Transfer")
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@@ -173,26 +163,17 @@ with gr.Blocks() as demo:
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gr.Markdown("Interactive demo of Inference Time Optimization (ITO) for Music Mastering Style Transfer. \
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The mastering style transfer is performed by a differentiable audio processing model, and the predicted parameters are shown as the output. \
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Perform mastering style transfer with an input source audio and a reference mastering style audio. On top of this result, you can perform ITO to optimize the reference embedding $z_{ref}$ to further gain control over the output mastering style.")
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gr.Image("ito_snow.png", width=
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gr.Markdown("## Step 1: Mastering Style Transfer")
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with gr.Tab("Upload Audio"):
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with gr.Row():
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input_audio = gr.Audio(label="Source Audio $x_{in}$", interactive=not use_example_files.value)
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reference_audio = gr.Audio(label="Reference Style Audio $x_{ref}$", interactive=not use_example_files.value)
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# Dropdowns for selecting example files
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with gr.Row():
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input_example_dropdown = gr.Dropdown(label="Select Input Example", choices=input_examples, interactive=use_example_files.value)
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reference_example_dropdown = gr.Dropdown(label="Select Reference Example", choices=reference_examples, interactive=use_example_files.value)
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process_button = gr.Button("Process Mastering Style Transfer")
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gr.Markdown('<span style="color: lightgray; font-style: italic;">all output samples are normalized to -12dB</span>')
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gr.Markdown("all output samples are normalized to -12dB")
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with gr.Row():
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with gr.Column():
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@@ -200,27 +181,12 @@ with gr.Blocks() as demo:
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normalized_input = gr.Audio(label="Normalized Source Audio", type='numpy')
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param_output = gr.Textbox(label="Predicted Parameters", lines=5)
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def process_audio_with_examples(input_audio, reference_audio, input_example, reference_example, use_examples):
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if use_examples:
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if input_example:
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input_audio = sf.read(os.path.join(EXAMPLES_DIR, input_example))[0]
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if reference_example:
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reference_audio = sf.read(os.path.join(EXAMPLES_DIR, reference_example))[0]
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return process_audio(input_audio, reference_audio)
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process_button.click(
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inputs=[input_audio, reference_audio
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outputs=[output_audio, param_output, normalized_input]
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)
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# Update the interactivity of the audio inputs and dropdowns based on the checkbox
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use_example_files.change(
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lambda use_examples: (not use_examples, not use_examples),
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inputs=[use_example_files],
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outputs=[input_audio, reference_audio]
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)
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with gr.Tab("YouTube Audio"):
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with gr.Row():
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input_youtube_url = gr.Textbox(label="Input YouTube URL")
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return (args.sample_rate, current_output), ito_param_output, selected_result['log']
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""" APP display """
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with gr.Blocks() as demo:
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gr.Markdown("# ITO-Master: Inference Time Optimization for Mastering Style Transfer")
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gr.Markdown("Interactive demo of Inference Time Optimization (ITO) for Music Mastering Style Transfer. \
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The mastering style transfer is performed by a differentiable audio processing model, and the predicted parameters are shown as the output. \
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Perform mastering style transfer with an input source audio and a reference mastering style audio. On top of this result, you can perform ITO to optimize the reference embedding $z_{ref}$ to further gain control over the output mastering style.")
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gr.Image("ito_snow.png", width=500, height=300, label="ITO pipeline")
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gr.Markdown("## Step 1: Mastering Style Transfer")
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with gr.Tab("Upload Audio"):
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with gr.Row():
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input_audio = gr.Audio(label="Source Audio $x_{in}$")
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reference_audio = gr.Audio(label="Reference Style Audio $x_{ref}$")
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process_button = gr.Button("Process Mastering Style Transfer")
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gr.Markdown('<span style="color: lightgray; font-style: italic;">all output samples are normalized to -12dB</span>')
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with gr.Row():
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with gr.Column():
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normalized_input = gr.Audio(label="Normalized Source Audio", type='numpy')
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param_output = gr.Textbox(label="Predicted Parameters", lines=5)
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process_button.click(
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process_audio,
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inputs=[input_audio, reference_audio],
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outputs=[output_audio, param_output, normalized_input]
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)
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with gr.Tab("YouTube Audio"):
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with gr.Row():
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input_youtube_url = gr.Textbox(label="Input YouTube URL")
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