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Create app.py
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app.py
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import gradio as gr
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import torch
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import demucs.separate
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import shlex
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import os
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import spaces
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# Check if CUDA is available
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device = "cuda" if torch.cuda.is_available() else "cpu"
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# Define the inference function
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@spaces.GPU
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def inference(audio_file, model_name, two_stems, mp3, mp3_bitrate):
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"""
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Performs inference using Demucs.
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Args:
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audio_file: The audio file to separate.
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model_name: The name of the Demucs model to use.
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two_stems: The name of the stem to separate (for two-stems mode).
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mp3: Whether to save the output as MP3.
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mp3_bitrate: The bitrate of the output MP3 file.
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Returns:
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A list of separated audio files.
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"""
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# Construct the command line arguments
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cmd = f"demucs -n {model_name} --clip-mode clamp --shifts=1"
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if two_stems:
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cmd += f" --two-stems={two_stems}"
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if mp3:
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cmd += f" --mp3 --mp3-bitrate={mp3_bitrate}"
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cmd += f" {audio_file.name}"
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# Run Demucs
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demucs.separate.main(shlex.split(cmd))
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# Get the output file paths
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output_dir = os.path.join("separated", model_name, os.path.splitext(os.path.basename(audio_file.name))[0])
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output_files = [os.path.join(output_dir, f) for f in os.listdir(output_dir) if os.path.isfile(os.path.join(output_dir, f))]
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return output_files
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# Define the Gradio interface
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iface = gr.Interface(
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fn=inference,
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inputs=[
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gr.Audio(source="upload", type="filepath"),
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gr.Dropdown(["htdemucs", "htdemucs_ft", "htdemucs_6s", "hdemucs_mmi", "mdx", "mdx_extra", "mdx_q", "mdx_extra_q"], label="Model Name"),
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gr.Dropdown(["vocals", "drums", "bass", "other"], label="Two Stems (Optional)", optional=True),
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gr.Checkbox(label="Save as MP3"),
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gr.Slider(128, 320, step=32, label="MP3 Bitrate", visible=False),
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],
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outputs=gr.Files(),
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title="Demucs Music Source Separation",
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description="Separate vocals, drums, bass, and other instruments from your music using Demucs.",
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)
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# Launch the Gradio interface
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iface.launch()
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