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