webUI.py

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, DEMUCS_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()}
        models_url[DEMUCS_ARCH_TYPE] = online_data["demucs_download_list"]
        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"),
            DEMUCS_ARCH_TYPE: (DEMUCS_MODELS_DIR, ".yaml"),
        }
        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)
        elif arch == DEMUCS_ARCH_TYPE:
            pass

    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())
        elif arch == DEMUCS_ARCH_TYPE:
            model_update = self.model_choice.update(choices=choices, value=CHOOSE_MODEL, label=CHOOSE_DEMUCS_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())
        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 WebUI 🎵 </h1>")
            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/r3gm/Ultimate-Vocal-Remover-WebUI?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, DEMUCS_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='Rhythmic Transmutation Device', value=True, interactive=True) #label=GPU_CONVERSION_MAIN_LABEL, value=root.is_gpu_conversion_var.get(), 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("Settings Guide"):
                            pass
                        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)
                        with gr.TabItem("Download models"):

                            def md_url(url, text=None):
                                if text is None:
                                    text = url
                                return f"[{url}]({url})"

                            with gr.Row():
                                vr_models = self.models_url[VR_ARCH_TYPE]
                                self.vr_download_choice = gr.Dropdown(choices=list(vr_models.keys()), label=f"Select {VR_ARCH_TYPE} Model", interactive=True)
                                self.vr_download_url = gr.Markdown()
                                self.vr_download_choice.change(lambda model: md_url(vr_models[model]), inputs=self.vr_download_choice, outputs=self.vr_download_url)
                            with gr.Row(variant="panel"):
                                mdx_models = self.models_url[MDX_ARCH_TYPE]
                                self.mdx_download_choice = gr.Dropdown(choices=list(mdx_models.keys()), label=f"Select {MDX_ARCH_TYPE} Model", interactive=True)
                                self.mdx_download_url = gr.Markdown()
                                self.mdx_download_choice.change(lambda model: md_url(mdx_models[model]), inputs=self.mdx_download_choice, outputs=self.mdx_download_url)
                            with gr.Row(variant="panel"):
                                demucs_models: Dict[str, Dict] = self.models_url[DEMUCS_ARCH_TYPE]
                                self.demucs_download_choice = gr.Dropdown(choices=list(demucs_models.keys()), label=f"Select {DEMUCS_ARCH_TYPE} Model", interactive=True)
                                self.demucs_download_url = gr.Markdown()

                                self.demucs_download_choice.change(
                                    lambda model: "\n".join([
                                        "- " + md_url(url, text=filename) for filename, url in demucs_models[model].items()]),
                                    inputs=self.demucs_download_choice,
                                    outputs=self.demucs_download_url)

            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', 'Demucs']:
        if category == 'VR Arc':
            model_path = 'models/VR_Models'
        elif category == 'MDX-Net':
            model_path = 'models/MDX_Net_Models'
        elif category == 'Demucs':
            model_path = 'models/Demucs_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()