UVR5 / webUI.py
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import os
import json
import librosa
import soundfile
import numpy as np
import gradio as gr
from UVR_interface import root, UVRInterface, VR_MODELS_DIR, MDX_MODELS_DIR
from gui_data.constants import *
from typing import List, Dict, Callable, Union
class UVRWebUI:
def __init__(self, uvr: UVRInterface, online_data_path: str) -> None:
self.uvr = uvr
self.models_url = self.get_models_url(online_data_path)
self.define_layout()
self.input_temp_dir = "__temp"
self.export_path = "out"
if not os.path.exists(self.input_temp_dir):
os.mkdir(self.input_temp_dir)
def get_models_url(self, models_info_path: str) -> Dict[str, Dict]:
with open(models_info_path, "r") as f:
online_data = json.loads(f.read())
models_url = {}
for arch, download_list_key in zip([VR_ARCH_TYPE, MDX_ARCH_TYPE], ["vr_download_list", "mdx_download_list"]):
models_url[arch] = {model: NORMAL_REPO+model_path for model, model_path in online_data[download_list_key].items()}
return models_url
def get_local_models(self, arch: str) -> List[str]:
model_config = {
VR_ARCH_TYPE: (VR_MODELS_DIR, ".pth"),
MDX_ARCH_TYPE: (MDX_MODELS_DIR, ".onnx"),
}
try:
model_dir, suffix = model_config[arch]
except KeyError:
raise ValueError(f"Unkown arch type: {arch}")
return [os.path.splitext(f)[0] for f in os.listdir(model_dir) if f.endswith(suffix)]
def set_arch_setting_value(self, arch: str, setting1, setting2):
if arch == VR_ARCH_TYPE:
root.window_size_var.set(setting1)
root.aggression_setting_var.set(setting2)
elif arch == MDX_ARCH_TYPE:
root.mdx_batch_size_var.set(setting1)
root.compensate_var.set(setting2)
def arch_select_update(self, arch: str) -> List[Dict]:
choices = self.get_local_models(arch)
if arch == VR_ARCH_TYPE:
model_update = self.model_choice.update(choices=choices, value=CHOOSE_MODEL, label=SELECT_VR_MODEL_MAIN_LABEL)
setting1_update = self.arch_setting1.update(choices=VR_WINDOW, label=WINDOW_SIZE_MAIN_LABEL, value=root.window_size_var.get())
setting2_update = self.arch_setting2.update(choices=VR_AGGRESSION, label=AGGRESSION_SETTING_MAIN_LABEL, value=root.aggression_setting_var.get())
elif arch == MDX_ARCH_TYPE:
model_update = self.model_choice.update(choices=choices, value=CHOOSE_MODEL, label=CHOOSE_MDX_MODEL_MAIN_LABEL)
setting1_update = self.arch_setting1.update(choices=BATCH_SIZE, label=BATCHES_MDX_MAIN_LABEL, value=root.mdx_batch_size_var.get())
setting2_update = self.arch_setting2.update(choices=VOL_COMPENSATION, label=VOL_COMP_MDX_MAIN_LABEL, value=root.compensate_var.get())
else:
raise gr.Error(f"Unkown arch type: {arch}")
return [model_update, setting1_update, setting2_update]
def model_select_update(self, arch: str, model_name: str) -> List[Union[str, Dict, None]]:
if model_name == CHOOSE_MODEL:
return [None for _ in range(4)]
model, = self.uvr.assemble_model_data(model_name, arch)
if not model.model_status:
raise gr.Error(f"Cannot get model data, model hash = {model.model_hash}")
stem1_check_update = self.primary_stem_only.update(label=f"{model.primary_stem} Only")
stem2_check_update = self.secondary_stem_only.update(label=f"{model.secondary_stem} Only")
stem1_out_update = self.primary_stem_out.update(label=f"Output {model.primary_stem}")
stem2_out_update = self.secondary_stem_out.update(label=f"Output {model.secondary_stem}")
return [stem1_check_update, stem2_check_update, stem1_out_update, stem2_out_update]
def checkbox_set_root_value(self, checkbox: gr.Checkbox, root_attr: str):
checkbox.change(lambda value: root.__getattribute__(root_attr).set(value), inputs=checkbox)
def set_checkboxes_exclusive(self, checkboxes: List[gr.Checkbox], pure_callbacks: List[Callable], exclusive_value=True):
def exclusive_onchange(i, callback_i):
def new_onchange(*check_values):
if check_values[i] == exclusive_value:
return_values = []
for j, value_j in enumerate(check_values):
if j != i and value_j == exclusive_value:
return_values.append(not exclusive_value)
else:
return_values.append(value_j)
else:
return_values = check_values
callback_i(check_values[i])
return return_values
return new_onchange
for i, (checkbox, callback) in enumerate(zip(checkboxes, pure_callbacks)):
checkbox.change(exclusive_onchange(i, callback), inputs=checkboxes, outputs=checkboxes)
def process(self, input_audio, input_filename, model_name, arch, setting1, setting2, progress=gr.Progress()):
def set_progress_func(step, inference_iterations=0):
progress_curr = step + inference_iterations
progress(progress_curr)
sampling_rate, audio = input_audio
audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
if len(audio.shape) > 1:
audio = librosa.to_mono(audio.transpose(1, 0))
input_path = os.path.join(self.input_temp_dir, input_filename)
soundfile.write(input_path, audio, sampling_rate, format="wav")
self.set_arch_setting_value(arch, setting1, setting2)
seperator = uvr.process(
model_name=model_name,
arch_type=arch,
audio_file=input_path,
export_path=self.export_path,
is_model_sample_mode=root.model_sample_mode_var.get(),
set_progress_func=set_progress_func,
)
primary_audio = None
secondary_audio = None
msg = ""
if not seperator.is_secondary_stem_only:
primary_stem_path = os.path.join(seperator.export_path, f"{seperator.audio_file_base}_({seperator.primary_stem}).wav")
audio, rate = soundfile.read(primary_stem_path)
primary_audio = (rate, audio)
msg += f"{seperator.primary_stem} saved at {primary_stem_path}\n"
if not seperator.is_primary_stem_only:
secondary_stem_path = os.path.join(seperator.export_path, f"{seperator.audio_file_base}_({seperator.secondary_stem}).wav")
audio, rate = soundfile.read(secondary_stem_path)
secondary_audio = (rate, audio)
msg += f"{seperator.secondary_stem} saved at {secondary_stem_path}\n"
os.remove(input_path)
return primary_audio, secondary_audio, msg
def define_layout(self):
with gr.Blocks() as app:
self.app = app
gr.HTML("<h1> 🎵 Ultimate Vocal Remover 5.6 for Hugging Face 🎵 </h1>")
gr.Markdown("## Space created by [Not Eddy (Spanish Mod)](http://discord.com/users/274566299349155851) in [AI HUB](https://discord.gg/aihub) server.")
gr.Markdown("## You can use a GPU version in this [Colab](https://colab.research.google.com/github/Eddycrack864/Ultimate-Vocal-Remover-5.6-for-Google-Colab/blob/main/Ultimate_Vocal_Remover_5_6_for_Google_Colab.ipynb). If you liked the space and colab you can give it a 💖 and star my repo on [GitHub](https://github.com/Eddycrack864/UVR5-5.6-for-Colab).")
gr.Markdown("### Thanks to: [Hina](https://github.com/hinabl), [r3gm](https://github.com/R3gm) and [Anjok07](https://github.com/Anjok07)")
gr.Markdown("### You can donate to the original UVR5 project [here](https://www.buymeacoffee.com/uvr5):")
gr.Markdown("### This is an experimental demo with CPU. Duplicate the space for use in private.")
gr.Markdown(
"[![Duplicate this Space](https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-sm-dark.svg)](https://huggingface.co/spaces/Eddycrack864/UVR5?duplicate=true)\n\n"
)
with gr.Tabs():
with gr.TabItem("Process"):
with gr.Row():
self.arch_choice = gr.Dropdown(
choices=[VR_ARCH_TYPE, MDX_ARCH_TYPE], value=VR_ARCH_TYPE, # choices=[VR_ARCH_TYPE, MDX_ARCH_TYPE, DEMUCS_ARCH_TYPE], value=VR_ARCH_TYPE,
label=CHOOSE_PROC_METHOD_MAIN_LABEL, interactive=True)
self.model_choice = gr.Dropdown(
choices=self.get_local_models(VR_ARCH_TYPE), value=CHOOSE_MODEL,
label=SELECT_VR_MODEL_MAIN_LABEL+' 👋Select a model', interactive=True)
with gr.Row():
self.arch_setting1 = gr.Dropdown(
choices=VR_WINDOW, value=root.window_size_var.get(),
label=WINDOW_SIZE_MAIN_LABEL+' 👋Select one', interactive=True)
self.arch_setting2 = gr.Dropdown(
choices=VR_AGGRESSION, value=root.aggression_setting_var.get(),
label=AGGRESSION_SETTING_MAIN_LABEL, interactive=True)
with gr.Row():
self.use_gpu = gr.Checkbox(
label=GPU_CONVERSION_MAIN_LABEL, value=root.is_gpu_conversion_var.get(), interactive=True) #label='Rhythmic Transmutation Device', value=True, interactive=True)
self.primary_stem_only = gr.Checkbox(
label=f"{PRIMARY_STEM} only", value=root.is_primary_stem_only_var.get(), interactive=True)
self.secondary_stem_only = gr.Checkbox(
label=f"{SECONDARY_STEM} only", value=root.is_secondary_stem_only_var.get(), interactive=True)
self.sample_mode = gr.Checkbox(
label=SAMPLE_MODE_CHECKBOX(root.model_sample_mode_duration_var.get()),
value=root.model_sample_mode_var.get(), interactive=True)
with gr.Row():
self.input_filename = gr.Textbox(label="Input filename", value="temp.wav", interactive=True)
with gr.Row():
self.audio_in = gr.Audio(label="Input audio", interactive=True)
with gr.Row():
self.process_submit = gr.Button(START_PROCESSING, variant="primary")
with gr.Row():
self.primary_stem_out = gr.Audio(label=f"Output {PRIMARY_STEM}", interactive=False)
self.secondary_stem_out = gr.Audio(label=f"Output {SECONDARY_STEM}", interactive=False)
with gr.Row():
self.out_message = gr.Textbox(label="Output Message", interactive=False, show_progress=False)
with gr.TabItem("Settings"):
with gr.Tabs():
with gr.TabItem("Additional Settigns"):
self.wav_type = gr.Dropdown(choices=WAV_TYPE, label="Wav Type", value="PCM_16", interactive=True)
self.mp3_rate = gr.Dropdown(choices=MP3_BIT_RATES, label="MP3 Bitrate", value="320k",interactive=True)
self.arch_choice.change(
self.arch_select_update, inputs=self.arch_choice,
outputs=[self.model_choice, self.arch_setting1, self.arch_setting2])
self.model_choice.change(
self.model_select_update, inputs=[self.arch_choice, self.model_choice],
outputs=[self.primary_stem_only, self.secondary_stem_only, self.primary_stem_out, self.secondary_stem_out])
self.checkbox_set_root_value(self.use_gpu, 'is_gpu_conversion_var')
self.checkbox_set_root_value(self.sample_mode, 'model_sample_mode_var')
self.set_checkboxes_exclusive(
[self.primary_stem_only, self.secondary_stem_only],
[lambda value: root.is_primary_stem_only_var.set(value), lambda value: root.is_secondary_stem_only_var.set(value)])
self.process_submit.click(
self.process,
inputs=[self.audio_in, self.input_filename, self.model_choice, self.arch_choice, self.arch_setting1, self.arch_setting2],
outputs=[self.primary_stem_out, self.secondary_stem_out, self.out_message])
def launch(self, **kwargs):
self.app.queue().launch(**kwargs)
uvr = UVRInterface()
uvr.cached_sources_clear()
webui = UVRWebUI(uvr, online_data_path='models/download_checks.json')
print(webui.models_url)
model_dict = webui.models_url
import os
import wget
for category, models in model_dict.items():
if category in ['VR Arc', 'MDX-Net']:
if category == 'VR Arc':
model_path = 'models/VR_Models'
elif category == 'MDX-Net':
model_path = 'models/MDX_Net_Models'
for model_name, model_url in models.items():
cmd = f"aria2c --optimize-concurrent-downloads --console-log-level=error --summary-interval=10 -j5 -x16 -s16 -k1M -c -d {model_path} -Z {model_url}"
os.system(cmd)
print("Models downloaded successfully.")
webui = UVRWebUI(uvr, online_data_path='models/download_checks.json')
webui.launch()