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app.py
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@@ -60,79 +60,6 @@
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# import os
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# import gradio as gr
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# from scipy.io.wavfile import write
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# import subprocess
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# import torch
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# # Assuming audio_separator is available in your environment
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# from audio_separator import Separator
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# def inference(audio, vocals, bass, drums, other, piano, guitar, lead_vocals, backing_vocals):
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# # Initially, show the loading GIF
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# loading_gif_path = "7RwF.gif"
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# gr.Image(loading_gif_path,visible=True)
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# os.makedirs("out", exist_ok=True)
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# audio_path = 'test.wav'
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# write(audio_path, audio[0], audio[1])
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# device = 'cuda' if torch.cuda.is_available() else 'cpu'
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# print(f"Using device: {device}")
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# try:
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# command = f"python3 -m demucs.separate -n htdemucs_6s -d {device} {audio_path} -o out"
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# process = subprocess.run(command, shell=True, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
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# print("Demucs script output:", process.stdout.decode())
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# except subprocess.CalledProcessError as e:
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# print("Error in Demucs script:", e.stderr.decode())
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# return [gr.Audio(visible=False)] * 8 + [loading_gif_path]
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# try:
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# separator = Separator("./out/htdemucs_6s/test/vocals.wav", model_name='UVR_MDXNET_KARA_2', use_cuda=device=='cuda', output_format='wav')
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# primary_stem_path, secondary_stem_path = separator.separate()
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# except Exception as e:
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# print("Error in custom separation:", str(e))
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# return [gr.Audio(visible=False)] * 8 + [loading_gif_path]
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# stem_paths = {
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# "vocals": "./out/htdemucs_6s/test/vocals.wav" if vocals else None,
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# "bass": "./out/htdemucs_6s/test/bass.wav" if bass else None,
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# "drums": "./out/htdemucs_6s/test/drums.wav" if drums else None,
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# "other": "./out/htdemucs_6s/test/other.wav" if other else None,
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# "piano": "./out/htdemucs_6s/test/piano.wav" if piano else None,
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# "guitar": "./out/htdemucs_6s/test/guitar.wav" if guitar else None,
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# "lead_vocals": primary_stem_path if lead_vocals else None,
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# "backing_vocals": secondary_stem_path if backing_vocals else None
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# }
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# # Once processing is done, hide the GIF by returning a transparent image
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# gr.Image(visible=False)
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# return [gr.Audio(stem_paths[stem], visible=bool(stem_paths[stem])) for stem in stem_paths]
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# # Define checkboxes for each stem
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# checkbox_labels = ["Full Vocals", "Bass", "Drums", "Other", "Piano", "Guitar", "Lead Vocals", "Backing Vocals"]
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# checkboxes = [gr.components.Checkbox(label=label) for label in checkbox_labels]
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# # Gradio Interface
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# title = "Source Separation Demo"
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# description = "Music Source Separation in the Waveform Domain. Upload your audio to begin."
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# iface = gr.Interface(
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# inference,
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# [gr.components.Audio(type="numpy", label="Input")] + checkboxes,
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# [gr.Audio(label=label, visible=False) for label in checkbox_labels],
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# title=title,
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# description=description,
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# )
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# iface.launch()
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import os
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import gradio as gr
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from scipy.io.wavfile import write
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# Assuming audio_separator is available in your environment
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from audio_separator import Separator
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def start_loading():
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global show_loading_gif
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show_loading_gif = True
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def stop_loading():
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global show_loading_gif
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show_loading_gif = False
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def process_audio(audio, vocals, bass, drums, other, piano, guitar, lead_vocals, backing_vocals):
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# Audio processing logic
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os.makedirs("out", exist_ok=True)
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audio_path = 'test.wav'
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write(audio_path, audio[0], audio[1])
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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try:
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command = f"python3 -m demucs.separate -n htdemucs_6s -d {device} {audio_path} -o out"
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process = subprocess.run(command, shell=True, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
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except subprocess.CalledProcessError as e:
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try:
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separator = Separator("./out/htdemucs_6s/test/vocals.wav", model_name='UVR_MDXNET_KARA_2', use_cuda=device=='cuda', output_format='wav')
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primary_stem_path, secondary_stem_path = separator.separate()
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except Exception as e:
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# Generate paths for the stems
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stem_paths = {
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"vocals": "./out/htdemucs_6s/test/vocals.wav" if vocals else None,
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"bass": "./out/htdemucs_6s/test/bass.wav" if bass else None,
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@@ -184,23 +105,9 @@ def process_audio(audio, vocals, bass, drums, other, piano, guitar, lead_vocals,
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"backing_vocals": secondary_stem_path if backing_vocals else None
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}
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global show_loading_gif
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# Start loading
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start_loading()
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# Call the main processing function
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audio_outputs = process_audio(audio, vocals, bass, drums, other, piano, guitar, lead_vocals, backing_vocals)
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# Stop loading
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stop_loading()
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# Return the outputs along with the loading GIF state
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loading_gif_path = "7RwF.gif" if show_loading_gif else ""
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return loading_gif_path, *audio_outputs
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# Define checkboxes for each stem
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checkbox_labels = ["Full Vocals", "Bass", "Drums", "Other", "Piano", "Guitar", "Lead Vocals", "Backing Vocals"]
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iface = gr.Interface(
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inference,
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[gr.components.Audio(type="numpy", label="Input")] + checkboxes,
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[gr.
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title=title,
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description=description,
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)
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iface.launch()
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import os
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import gradio as gr
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from scipy.io.wavfile import write
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# Assuming audio_separator is available in your environment
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from audio_separator import Separator
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def inference(audio, vocals, bass, drums, other, piano, guitar, lead_vocals, backing_vocals):
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status_message = "Processing..."
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os.makedirs("out", exist_ok=True)
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audio_path = 'test.wav'
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write(audio_path, audio[0], audio[1])
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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print(f"Using device: {device}")
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try:
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command = f"python3 -m demucs.separate -n htdemucs_6s -d {device} {audio_path} -o out"
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process = subprocess.run(command, shell=True, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
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print("Demucs script output:", process.stdout.decode())
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except subprocess.CalledProcessError as e:
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print("Error in Demucs script:", e.stderr.decode())
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return [gr.Audio(visible=False)] * 8 + [loading_gif_path]
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try:
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separator = Separator("./out/htdemucs_6s/test/vocals.wav", model_name='UVR_MDXNET_KARA_2', use_cuda=device=='cuda', output_format='wav')
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primary_stem_path, secondary_stem_path = separator.separate()
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except Exception as e:
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print("Error in custom separation:", str(e))
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return [gr.Audio(visible=False)] * 8 + [loading_gif_path]
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stem_paths = {
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"vocals": "./out/htdemucs_6s/test/vocals.wav" if vocals else None,
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"bass": "./out/htdemucs_6s/test/bass.wav" if bass else None,
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"backing_vocals": secondary_stem_path if backing_vocals else None
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}
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# Once processing is done, hide the GIF by returning a transparent image
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return [gr.Audio(stem_paths[stem], visible=bool(stem_paths[stem])) for stem in stem_paths], "Done! Successfully processed."
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# Define checkboxes for each stem
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checkbox_labels = ["Full Vocals", "Bass", "Drums", "Other", "Piano", "Guitar", "Lead Vocals", "Backing Vocals"]
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iface = gr.Interface(
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inference,
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[gr.components.Audio(type="numpy", label="Input")] + checkboxes,
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[gr.Audio(label=label, visible=False) for label in checkbox_labels] + [gr.Label()],
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title=title,
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description=description,
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)
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iface.launch()
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