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import os |
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import re |
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import random |
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from scipy.io.wavfile import write |
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import gradio as gr |
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from Applio import * |
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from uvrmodel import * |
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def roformer_separator(roformer_audio, roformer_model, roformer_output_format, roformer_overlap): |
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files_list = [] |
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files_list.clear() |
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directory = "./outputs" |
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random_id = str(random.randint(10000, 99999)) |
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pattern = f"{random_id}" |
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os.makedirs("outputs", exist_ok=True) |
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write(f'{random_id}.wav', roformer_audio[0], roformer_audio[1]) |
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full_roformer_model = roformer_models[roformer_model] |
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prompt = f"audio-separator {random_id}.wav --model_filename {full_roformer_model} --output_dir=./outputs --output_format={roformer_output_format} --normalization=0.9 --mdxc_overlap={roformer_overlap}" |
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os.system(prompt) |
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for file in os.listdir(directory): |
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if re.search(pattern, file): |
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files_list.append(os.path.join(directory, file)) |
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stem1_file = files_list[0] |
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stem2_file = files_list[1] |
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return stem1_file, stem2_file |
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def mdxc_separator(mdx23c_audio, mdx23c_model, mdx23c_output_format, mdx23c_segment_size, mdx23c_overlap): |
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files_list = [] |
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files_list.clear() |
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directory = "./outputs" |
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random_id = str(random.randint(10000, 99999)) |
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pattern = f"{random_id}" |
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os.makedirs("outputs", exist_ok=True) |
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write(f'{random_id}.wav', mdx23c_audio[0], mdx23c_audio[1]) |
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prompt = f"audio-separator {random_id}.wav --model_filename {mdx23c_model} --output_dir=./outputs --output_format={mdx23c_output_format} --normalization=0.9 --mdxc_segment_size={mdx23c_segment_size} --mdxc_overlap={mdx23c_overlap}" |
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os.system(prompt) |
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for file in os.listdir(directory): |
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if re.search(pattern, file): |
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files_list.append(os.path.join(directory, file)) |
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stem1_file = files_list[0] |
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stem2_file = files_list[1] |
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return stem1_file, stem2_file |
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def mdxnet_separator(mdxnet_audio, mdxnet_model, mdxnet_output_format, mdxnet_segment_size, mdxnet_overlap, mdxnet_denoise): |
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files_list = [] |
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files_list.clear() |
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directory = "./outputs" |
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random_id = str(random.randint(10000, 99999)) |
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pattern = f"{random_id}" |
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os.makedirs("outputs", exist_ok=True) |
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write(f'{random_id}.wav', mdxnet_audio[0], mdxnet_audio[1]) |
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prompt = f"audio-separator {random_id}.wav --model_filename {mdxnet_model} --output_dir=./outputs --output_format={mdxnet_output_format} --normalization=0.9 --mdx_segment_size={mdxnet_segment_size} --mdx_overlap={mdxnet_overlap}" |
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if mdxnet_denoise: |
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prompt += " --mdx_enable_denoise" |
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os.system(prompt) |
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for file in os.listdir(directory): |
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if re.search(pattern, file): |
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files_list.append(os.path.join(directory, file)) |
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stem1_file = files_list[0] |
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stem2_file = files_list[1] |
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return stem1_file, stem2_file |
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def vrarch_separator(vrarch_audio, vrarch_model, vrarch_output_format, vrarch_window_size, vrarch_agression, vrarch_tta, vrarch_high_end_process): |
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files_list = [] |
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files_list.clear() |
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directory = "./outputs" |
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random_id = str(random.randint(10000, 99999)) |
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pattern = f"{random_id}" |
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os.makedirs("outputs", exist_ok=True) |
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write(f'{random_id}.wav', vrarch_audio[0], vrarch_audio[1]) |
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prompt = f"audio-separator {random_id}.wav --model_filename {vrarch_model} --output_dir=./outputs --output_format={vrarch_output_format} --normalization=0.9 --vr_window_size={vrarch_window_size} --vr_aggression={vrarch_agression}" |
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if vrarch_tta: |
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prompt += " --vr_enable_tta" |
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if vrarch_high_end_process: |
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prompt += " --vr_high_end_process" |
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os.system(prompt) |
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for file in os.listdir(directory): |
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if re.search(pattern, file): |
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files_list.append(os.path.join(directory, file)) |
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stem1_file = files_list[0] |
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stem2_file = files_list[1] |
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return stem1_file, stem2_file |
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def demucs_separator(demucs_audio, demucs_model, demucs_output_format, demucs_shifts, demucs_overlap): |
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files_list = [] |
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files_list.clear() |
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directory = "./outputs" |
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random_id = str(random.randint(10000, 99999)) |
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pattern = f"{random_id}" |
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os.makedirs("outputs", exist_ok=True) |
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write(f'{random_id}.wav', demucs_audio[0], demucs_audio[1]) |
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prompt = f"audio-separator {random_id}.wav --model_filename {demucs_model} --output_dir=./outputs --output_format={demucs_output_format} --normalization=0.9 --demucs_shifts={demucs_shifts} --demucs_overlap={demucs_overlap}" |
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os.system(prompt) |
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for file in os.listdir(directory): |
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if re.search(pattern, file): |
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files_list.append(os.path.join(directory, file)) |
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stem1_file = files_list[0] |
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stem2_file = files_list[1] |
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stem3_file = files_list[2] |
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stem4_file = files_list[3] |
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return stem1_file, stem2_file, stem3_file, stem4_file |
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with gr.Blocks(theme=applio, title="🎵 UVR5 UI 🎵") as app: |
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gr.Markdown("<h1> 🎵 UVR5 UI 🎵 </h1>") |
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gr.Markdown("If you liked this HF Space you can give me a ❤️") |
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gr.Markdown("Try UVR5 UI with GPU using Colab [here](https://colab.research.google.com/github/Eddycrack864/UVR5-UI/blob/main/UVR_UI.ipynb)") |
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with gr.Tabs(): |
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with gr.TabItem("BS/Mel Roformer"): |
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with gr.Row(): |
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roformer_model = gr.Dropdown( |
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label = "Select the Model", |
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choices=list(roformer_models.keys()), |
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interactive = True |
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) |
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roformer_output_format = gr.Dropdown( |
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label = "Select the Output Format", |
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choices = output_format, |
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interactive = True |
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) |
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with gr.Row(): |
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roformer_overlap = gr.Slider( |
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minimum = 2, |
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maximum = 4, |
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step = 1, |
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label = "Overlap", |
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info = "Amount of overlap between prediction windows.", |
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value = 4, |
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interactive = True |
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) |
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with gr.Row(): |
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roformer_audio = gr.Audio( |
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label = "Input Audio", |
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type = "numpy", |
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interactive = True |
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) |
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with gr.Row(): |
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roformer_button = gr.Button("Separate!", variant = "primary") |
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with gr.Row(): |
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roformer_stem1 = gr.Audio( |
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show_download_button = True, |
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interactive = False, |
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label = "Stem 1", |
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type = "filepath" |
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) |
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roformer_stem2 = gr.Audio( |
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show_download_button = True, |
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interactive = False, |
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label = "Stem 2", |
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type = "filepath" |
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) |
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roformer_button.click(roformer_separator, [roformer_audio, roformer_model, roformer_output_format, roformer_overlap], [roformer_stem1, roformer_stem2]) |
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with gr.TabItem("MDX23C"): |
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with gr.Row(): |
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mdx23c_model = gr.Dropdown( |
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label = "Select the Model", |
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choices = mdx23c_models, |
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interactive = True |
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) |
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mdx23c_output_format = gr.Dropdown( |
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label = "Select the Output Format", |
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choices = output_format, |
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interactive = True |
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) |
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with gr.Row(): |
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mdx23c_segment_size = gr.Slider( |
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minimum = 32, |
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maximum = 4000, |
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step = 32, |
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label = "Segment Size", |
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info = "Larger consumes more resources, but may give better results.", |
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value = 256, |
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interactive = True |
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) |
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mdx23c_overlap = gr.Slider( |
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minimum = 2, |
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maximum = 50, |
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step = 1, |
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label = "Overlap", |
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info = "Amount of overlap between prediction windows.", |
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value = 8, |
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interactive = True |
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) |
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with gr.Row(): |
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mdx23c_audio = gr.Audio( |
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label = "Input Audio", |
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type = "numpy", |
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interactive = True |
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) |
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with gr.Row(): |
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mdx23c_button = gr.Button("Separate!", variant = "primary") |
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with gr.Row(): |
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mdx23c_stem1 = gr.Audio( |
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show_download_button = True, |
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interactive = False, |
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label = "Stem 1", |
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type = "filepath" |
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) |
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mdx23c_stem2 = gr.Audio( |
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show_download_button = True, |
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interactive = False, |
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label = "Stem 2", |
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type = "filepath" |
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) |
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mdx23c_button.click(mdxc_separator, [mdx23c_audio, mdx23c_model, mdx23c_output_format, mdx23c_segment_size, mdx23c_overlap], [mdx23c_stem1, mdx23c_stem2]) |
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with gr.TabItem("MDX-NET"): |
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with gr.Row(): |
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mdxnet_model = gr.Dropdown( |
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label = "Select the Model", |
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choices = mdxnet_models, |
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interactive = True |
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) |
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mdxnet_output_format = gr.Dropdown( |
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label = "Select the Output Format", |
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choices = output_format, |
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interactive = True |
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) |
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with gr.Row(): |
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mdxnet_segment_size = gr.Slider( |
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minimum = 32, |
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maximum = 4000, |
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step = 32, |
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label = "Segment Size", |
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info = "Larger consumes more resources, but may give better results.", |
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value = 256, |
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interactive = True |
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) |
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mdxnet_overlap = gr.Dropdown( |
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label = "Overlap", |
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choices = mdxnet_overlap_values, |
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value = mdxnet_overlap_values[0], |
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interactive = True |
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) |
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mdxnet_denoise = gr.Checkbox( |
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label = "Denoise", |
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info = "Enable denoising during separation.", |
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value = True, |
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interactive = True |
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) |
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with gr.Row(): |
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mdxnet_audio = gr.Audio( |
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label = "Input Audio", |
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type = "numpy", |
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interactive = True |
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) |
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with gr.Row(): |
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mdxnet_button = gr.Button("Separate!", variant = "primary") |
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with gr.Row(): |
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mdxnet_stem1 = gr.Audio( |
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show_download_button = True, |
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interactive = False, |
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label = "Stem 1", |
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type = "filepath" |
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) |
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mdxnet_stem2 = gr.Audio( |
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show_download_button = True, |
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interactive = False, |
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label = "Stem 2", |
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type = "filepath" |
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) |
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mdxnet_button.click(mdxnet_separator, [mdxnet_audio, mdxnet_model, mdxnet_output_format, mdxnet_segment_size, mdxnet_overlap, mdxnet_denoise], [mdxnet_stem1, mdxnet_stem2]) |
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with gr.TabItem("VR ARCH"): |
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with gr.Row(): |
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vrarch_model = gr.Dropdown( |
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label = "Select the Model", |
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choices = vrarch_models, |
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interactive = True |
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) |
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vrarch_output_format = gr.Dropdown( |
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label = "Select the Output Format", |
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choices = output_format, |
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interactive = True |
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) |
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with gr.Row(): |
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vrarch_window_size = gr.Dropdown( |
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label = "Window Size", |
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choices = vrarch_window_size_values, |
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value = vrarch_window_size_values[0], |
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interactive = True |
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) |
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vrarch_agression = gr.Slider( |
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minimum = 1, |
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maximum = 50, |
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step = 1, |
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label = "Agression", |
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info = "Intensity of primary stem extraction.", |
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value = 5, |
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interactive = True |
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) |
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vrarch_tta = gr.Checkbox( |
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label = "TTA", |
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info = "Enable Test-Time-Augmentation; slow but improves quality.", |
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value = True, |
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visible = True, |
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interactive = True, |
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) |
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vrarch_high_end_process = gr.Checkbox( |
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label = "High End Process", |
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info = "Mirror the missing frequency range of the output.", |
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value = False, |
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visible = True, |
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interactive = True, |
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) |
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with gr.Row(): |
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vrarch_audio = gr.Audio( |
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label = "Input Audio", |
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type = "numpy", |
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interactive = True |
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) |
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with gr.Row(): |
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vrarch_button = gr.Button("Separate!", variant = "primary") |
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with gr.Row(): |
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vrarch_stem1 = gr.Audio( |
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show_download_button = True, |
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interactive = False, |
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type = "filepath", |
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label = "Stem 1" |
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) |
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vrarch_stem2 = gr.Audio( |
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show_download_button = True, |
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interactive = False, |
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type = "filepath", |
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label = "Stem 2" |
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) |
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vrarch_button.click(vrarch_separator, [vrarch_audio, vrarch_model, vrarch_output_format, vrarch_window_size, vrarch_agression, vrarch_tta, vrarch_high_end_process], [vrarch_stem1, vrarch_stem2]) |
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with gr.TabItem("Demucs"): |
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with gr.Row(): |
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demucs_model = gr.Dropdown( |
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label = "Select the Model", |
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choices = demucs_models, |
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interactive = True |
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) |
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demucs_output_format = gr.Dropdown( |
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label = "Select the Output Format", |
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choices = output_format, |
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interactive = True |
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) |
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with gr.Row(): |
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demucs_shifts = gr.Slider( |
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minimum = 1, |
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maximum = 20, |
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step = 1, |
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label = "Shifts", |
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info = "Number of predictions with random shifts, higher = slower but better quality.", |
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value = 2, |
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interactive = True |
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) |
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demucs_overlap = gr.Dropdown( |
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label = "Overlap", |
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choices = demucs_overlap_values, |
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value = demucs_overlap_values[0], |
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interactive = True |
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) |
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with gr.Row(): |
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demucs_audio = gr.Audio( |
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label = "Input Audio", |
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type = "numpy", |
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interactive = True |
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) |
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with gr.Row(): |
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demucs_button = gr.Button("Separate!", variant = "primary") |
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with gr.Row(): |
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demucs_stem1 = gr.Audio( |
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show_download_button = True, |
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interactive = False, |
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type = "filepath", |
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label = "Stem 1" |
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) |
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demucs_stem2 = gr.Audio( |
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show_download_button = True, |
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interactive = False, |
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type = "filepath", |
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label = "Stem 2" |
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) |
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with gr.Row(): |
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demucs_stem3 = gr.Audio( |
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show_download_button = True, |
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interactive = False, |
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type = "filepath", |
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label = "Stem 3" |
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) |
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demucs_stem4 = gr.Audio( |
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show_download_button = True, |
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interactive = False, |
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type = "filepath", |
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label = "Stem 4" |
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) |
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demucs_button.click(demucs_separator, [demucs_audio, demucs_model, demucs_output_format, demucs_shifts, demucs_overlap], [demucs_stem1, demucs_stem2, demucs_stem3, demucs_stem4]) |
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|
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with gr.TabItem("Credits"): |
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gr.Markdown( |
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""" |
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UVR5 UI created by **[Not Eddy (Spanish Mod)](http://discord.com/users/274566299349155851)** in **[AI HUB](https://discord.gg/aihub)** community. |
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* python-audio-separator by [beveradb](https://github.com/beveradb). |
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* Thanks to [Ilaria](https://github.com/TheStingerX) and [Mikus](https://github.com/cappuch) for the help with the code. |
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* Improvements by [Blane187](https://github.com/Blane187). |
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You can donate to the original UVR5 project here: |
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[!["Buy Me A Coffee"](https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png)](https://www.buymeacoffee.com/uvr5) |
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""" |
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) |
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app.queue() |
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app.launch(show_api=False) |