免费背景音人声分离解决方案MVSEP-MDX23,足以和Spleeter分庭抗礼

在音视频领域，把已经发布的混音歌曲或者音频文件逆向分离一直是世界性的课题。音波混合的物理特性导致在没有原始工程文件的情况下，将其还原和分离是一件很有难度的事情。

言及背景音人声分离技术，就不能不提Spleeter，它是一种用于音频源分离（音乐分离）的开源深度学习算法，由Deezer研究团队开发。使用的是一个性能取向的音源分离算法，并且为用户提供了已经预训练好的模型，能够开箱即用，这也是Spleeter泛用性高的原因之一，关于Spleeter，请移步：人工智能AI库Spleeter免费人声和背景音乐分离实践(Python3.10)，这里不再赘述。

MVSEP-MDX23背景音人声分离技术由Demucs研发，Demucs来自Facebook Research团队，它的发源晚于Spleeter，早于MDX-Net，并且经历过4个大版本的迭代，每一代的模型结构都被大改。Demucs的生成质量从v3开始大幅质变，一度领先行业平均水平，v4是现在最强的开源乐器分离单模型，v1和v2的网络模型被用作MDX-net其中的一部分。

本次我们基于MVSEP-MDX23来对音频的背景音和人声进行分离。

本地分离人声和背景音

如果本地离线运行MVSEP-MDX23，首先克隆代码：

git clone https://github.com/jarredou/MVSEP-MDX23-Colab_v2.git

随后进入项目并安装依赖：

cd MVSEP-MDX23-Colab_v2
pip3 install -r requirements.txt

随后直接进推理即可：

python3 inference.py --input_audio test.wav --output_folder ./results/

这里将test.wav进行人声分离，分离后的文件在results文件夹生成。

注意推理过程中会将分离模型下载到项目的models目录，极其巨大。

同时推理过程相当缓慢。

这里可以添加--single_onnx参数来提高推理速度，但音质上有一定的损失。

如果本地设备具备12G以上的显存，也可以添加--large_gpu参数来提高推理的速度。

如果本地没有N卡或者显存实在捉襟见肘，也可以通过--cpu参数来使用cpu进行推理，但是并不推荐这样做，因为本来就慢，用cpu就更慢了。

令人暖心的是，官方还利用Pyqt写了一个小的gui界面来提高操作友好度：

__author__ = 'Roman Solovyev (ZFTurbo), IPPM RAS'  

if __name__ == '__main__':
    import os  

    gpu_use = "0"
    print('GPU use: {}'.format(gpu_use))
    os.environ["CUDA_VISIBLE_DEVICES"] = "{}".format(gpu_use)  

import time
import os
import numpy as np
from PyQt5.QtCore import *
from PyQt5 import QtCore
from PyQt5.QtWidgets import *
import sys
from inference import predict_with_model  

root = dict()  

class Worker(QObject):
    finished = pyqtSignal()
    progress = pyqtSignal(int)  

    def __init__(self, options):
        super().__init__()
        self.options = options  

    def run(self):
        global root
        # Here we pass the update_progress (uncalled!)
        self.options['update_percent_func'] = self.update_progress
        predict_with_model(self.options)
        root['button_start'].setDisabled(False)
        root['button_finish'].setDisabled(True)
        root['start_proc'] = False
        self.finished.emit()  

    def update_progress(self, percent):
        self.progress.emit(percent)  

class Ui_Dialog(object):
    def setupUi(self, Dialog):
        global root  

        Dialog.setObjectName("Settings")
        Dialog.resize(370, 180)  

        self.checkbox_cpu = QCheckBox("Use CPU instead of GPU?", Dialog)
        self.checkbox_cpu.move(30, 10)
        self.checkbox_cpu.resize(320, 40)
        if root['cpu']:
            self.checkbox_cpu.setChecked(True)  

        self.checkbox_single_onnx = QCheckBox("Use single ONNX?", Dialog)
        self.checkbox_single_onnx.move(30, 40)
        self.checkbox_single_onnx.resize(320, 40)
        if root['single_onnx']:
            self.checkbox_single_onnx.setChecked(True)  

        self.pushButton_save = QPushButton(Dialog)
        self.pushButton_save.setObjectName("pushButton_save")
        self.pushButton_save.move(30, 120)
        self.pushButton_save.resize(150, 35)  

        self.pushButton_cancel = QPushButton(Dialog)
        self.pushButton_cancel.setObjectName("pushButton_cancel")
        self.pushButton_cancel.move(190, 120)
        self.pushButton_cancel.resize(150, 35)  

        self.retranslateUi(Dialog)
        QtCore.QMetaObject.connectSlotsByName(Dialog)
        self.Dialog = Dialog  

        # connect the two functions
        self.pushButton_save.clicked.connect(self.return_save)
        self.pushButton_cancel.clicked.connect(self.return_cancel)  

    def retranslateUi(self, Dialog):
        _translate = QtCore.QCoreApplication.translate
        Dialog.setWindowTitle(_translate("Settings", "Settings"))
        self.pushButton_cancel.setText(_translate("Settings", "Cancel"))
        self.pushButton_save.setText(_translate("Settings", "Save settings"))  

    def return_save(self):
        global root
        # print("save")
        root['cpu'] = self.checkbox_cpu.isChecked()
        root['single_onnx'] = self.checkbox_single_onnx.isChecked()
        self.Dialog.close()  

    def return_cancel(self):
        global root
        # print("cancel")
        self.Dialog.close()  

class MyWidget(QWidget):
    def __init__(self):
        super().__init__()
        self.initUI()  

    def initUI(self):
        self.resize(560, 360)
        self.move(300, 300)
        self.setWindowTitle('MVSEP music separation model')
        self.setAcceptDrops(True)  

    def dragEnterEvent(self, event):
        if event.mimeData().hasUrls():
            event.accept()
        else:
            event.ignore()  

    def dropEvent(self, event):
        global root
        files = [u.toLocalFile() for u in event.mimeData().urls()]
        txt = ''
        root['input_files'] = []
        for f in files:
            root['input_files'].append(f)
            txt += f + '\n'
        root['input_files_list_text_area'].insertPlainText(txt)
        root['progress_bar'].setValue(0)  

    def execute_long_task(self):
        global root  

        if len(root['input_files']) == 0 and 1:
            QMessageBox.about(root['w'], "Error", "No input files specified!")
            return  

        root['progress_bar'].show()
        root['button_start'].setDisabled(True)
        root['button_finish'].setDisabled(False)
        root['start_proc'] = True  

        options = {
            'input_audio': root['input_files'],
            'output_folder': root['output_folder'],
            'cpu': root['cpu'],
            'single_onnx': root['single_onnx'],
            'overlap_large': 0.6,
            'overlap_small': 0.5,
        }  

        self.update_progress(0)
        self.thread = QThread()
        self.worker = Worker(options)
        self.worker.moveToThread(self.thread)  

        self.thread.started.connect(self.worker.run)
        self.worker.finished.connect(self.thread.quit)
        self.worker.finished.connect(self.worker.deleteLater)
        self.thread.finished.connect(self.thread.deleteLater)
        self.worker.progress.connect(self.update_progress)  

        self.thread.start()  

    def stop_separation(self):
        global root
        self.thread.terminate()
        root['button_start'].setDisabled(False)
        root['button_finish'].setDisabled(True)
        root['start_proc'] = False
        root['progress_bar'].hide()  

    def update_progress(self, progress):
        global root
        root['progress_bar'].setValue(progress)  

    def open_settings(self):
        global root
        dialog = QDialog()
        dialog.ui = Ui_Dialog()
        dialog.ui.setupUi(dialog)
        dialog.exec_()  

def dialog_select_input_files():
    global root
    files, _ = QFileDialog.getOpenFileNames(
        None,
        "QFileDialog.getOpenFileNames()",
        "",
        "All Files (*);;Audio Files (*.wav, *.mp3, *.flac)",
    )
    if files:
        txt = ''
        root['input_files'] = []
        for f in files:
            root['input_files'].append(f)
            txt += f + '\n'
        root['input_files_list_text_area'].insertPlainText(txt)
        root['progress_bar'].setValue(0)
    return files  

def dialog_select_output_folder():
    global root
    foldername = QFileDialog.getExistingDirectory(
        None,
        "Select Directory"
    )
    root['output_folder'] = foldername + '/'
    root['output_folder_line_edit'].setText(root['output_folder'])
    return foldername  

def create_dialog():
    global root
    app = QApplication(sys.argv)  

    w = MyWidget()  

    root['input_files'] = []
    root['output_folder'] = os.path.dirname(os.path.abspath(__file__)) + '/results/'
    root['cpu'] = False
    root['single_onnx'] = False  

    button_select_input_files = QPushButton(w)
    button_select_input_files.setText("Input audio files")
    button_select_input_files.clicked.connect(dialog_select_input_files)
    button_select_input_files.setFixedHeight(35)
    button_select_input_files.setFixedWidth(150)
    button_select_input_files.move(30, 20)  

    input_files_list_text_area = QTextEdit(w)
    input_files_list_text_area.setReadOnly(True)
    input_files_list_text_area.setLineWrapMode(QTextEdit.NoWrap)
    font = input_files_list_text_area.font()
    font.setFamily("Courier")
    font.setPointSize(10)
    input_files_list_text_area.move(30, 60)
    input_files_list_text_area.resize(500, 100)  

    button_select_output_folder = QPushButton(w)
    button_select_output_folder.setText("Output folder")
    button_select_output_folder.setFixedHeight(35)
    button_select_output_folder.setFixedWidth(150)
    button_select_output_folder.clicked.connect(dialog_select_output_folder)
    button_select_output_folder.move(30, 180)  

    output_folder_line_edit = QLineEdit(w)
    output_folder_line_edit.setReadOnly(True)
    font = output_folder_line_edit.font()
    font.setFamily("Courier")
    font.setPointSize(10)
    output_folder_line_edit.move(30, 220)
    output_folder_line_edit.setFixedWidth(500)
    output_folder_line_edit.setText(root['output_folder'])  

    progress_bar = QProgressBar(w)
    # progress_bar.move(30, 310)
    progress_bar.setValue(0)
    progress_bar.setGeometry(30, 310, 500, 35)
    progress_bar.setAlignment(QtCore.Qt.AlignCenter)
    progress_bar.hide()
    root['progress_bar'] = progress_bar  

    button_start = QPushButton('Start separation', w)
    button_start.clicked.connect(w.execute_long_task)
    button_start.setFixedHeight(35)
    button_start.setFixedWidth(150)
    button_start.move(30, 270)  

    button_finish = QPushButton('Stop separation', w)
    button_finish.clicked.connect(w.stop_separation)
    button_finish.setFixedHeight(35)
    button_finish.setFixedWidth(150)
    button_finish.move(200, 270)
    button_finish.setDisabled(True)  

    button_settings = QPushButton('⚙', w)
    button_settings.clicked.connect(w.open_settings)
    button_settings.setFixedHeight(35)
    button_settings.setFixedWidth(35)
    button_settings.move(495, 270)
    button_settings.setDisabled(False)  

    mvsep_link = QLabel(w)
    mvsep_link.setOpenExternalLinks(True)
    font = mvsep_link.font()
    font.setFamily("Courier")
    font.setPointSize(10)
    mvsep_link.move(415, 30)
    mvsep_link.setText('Powered by <a href="https://mvsep.com">MVSep.com</a>')  

    root['w'] = w
    root['input_files_list_text_area'] = input_files_list_text_area
    root['output_folder_line_edit'] = output_folder_line_edit
    root['button_start'] = button_start
    root['button_finish'] = button_finish
    root['button_settings'] = button_settings  

    # w.showMaximized()
    w.show()
    sys.exit(app.exec_())  

if __name__ == '__main__':
    create_dialog()

效果如下：

界面虽然朴素，但相当实用，Spleeter可没给我们提供这个待遇。

Colab云端分离人声和背景音

托Google的福，我们也可以在Colab云端使用MVSEP-MDX23：

https://colab.research.google.com/github/jarredou/MVSEP-MDX23-Colab_v2/blob/v2.3/MVSep-MDX23-Colab.ipynb#scrollTo=uWX5WOqjU0QC

首先安装MVSEP-MDX23：

#@markdown #Installation
#@markdown *Run this cell to install MVSep-MDX23*
print('Installing... This will take 1 minute...')
%cd /content
from google.colab import drive
drive.mount('/content/drive')
!git clone https://github.com/jarredou/MVSEP-MDX23-Colab_v2.git &> /dev/null
%cd /content/MVSEP-MDX23-Colab_v2
!pip install -r requirements.txt &> /dev/null
# onnxruntime-gpu nightly fix for cuda12.2
!python -m pip install ort-nightly-gpu --index-url=https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ort-cuda-12-nightly/pypi/simple/
print('Installation done !')

随后编写推理代码：

#@markdown #Separation
from pathlib import Path
import glob  

%cd /content/MVSEP-MDX23-Colab_v2  

input = '/content/drive/MyDrive' #@param {type:"string"}
output_folder = '/content/drive/MyDrive/output' #@param {type:"string"}
#@markdown ---
#@markdown *Bigshifts=1 to disable that feature*  

BigShifts = 7 #@param {type:"slider", min:1, max:41, step:1}
#@markdown ---
overlap_InstVoc = 1 #@param {type:"slider", min:1, max:40, step:1}
overlap_VitLarge = 1 #@param {type:"slider", min:1, max:40, step:1}
#@markdown ---
weight_InstVoc = 8 #@param {type:"slider", min:0, max:10, step:1}
weight_VitLarge = 5 #@param {type:"slider", min:0, max:10, step:1}
#@markdown ---
use_VOCFT = False #@param {type:"boolean"}
overlap_VOCFT = 0.1 #@param {type:"slider", min:0, max:0.95, step:0.05}
weight_VOCFT = 2 #@param {type:"slider", min:0, max:10, step:1}
#@markdown ---
vocals_instru_only = True #@param {type:"boolean"}
overlap_demucs = 0.6 #@param {type:"slider", min:0, max:0.95, step:0.05}
#@markdown ---
output_format = 'PCM_16' #@param ["PCM_16", "FLOAT"]
if vocals_instru_only:
    vocals_only = '--vocals_only true'
else:
    vocals_only = ''  

if use_VOCFT:
    use_VOCFT = '--use_VOCFT true'
else:
    use_VOCFT = ''  

if Path(input).is_file():
  file_path = input
  Path(output_folder).mkdir(parents=True, exist_ok=True)
  !python inference.py \
        --large_gpu \
        --weight_InstVoc {weight_InstVoc} \
        --weight_VOCFT {weight_VOCFT} \
        --weight_VitLarge {weight_VitLarge} \
        --input_audio "{file_path}" \
        --overlap_demucs {overlap_demucs} \
        --overlap_VOCFT {overlap_VOCFT} \
        --overlap_InstVoc {overlap_InstVoc} \
        --overlap_VitLarge {overlap_VitLarge} \
        --output_format {output_format} \
        --BigShifts {BigShifts} \
        --output_folder "{output_folder}" \
        {vocals_only} \
        {use_VOCFT}  

else:
  file_paths = sorted([f'"{glob.escape(path)}"' for path in glob.glob(input + "/*")])[:]
  input_audio_args = ' '.join(file_paths)
  Path(output_folder).mkdir(parents=True, exist_ok=True)
  !python inference.py \
          --large_gpu \
          --weight_InstVoc {weight_InstVoc} \
          --weight_VOCFT {weight_VOCFT} \
          --weight_VitLarge {weight_VitLarge} \
          --input_audio {input_audio_args} \
          --overlap_demucs {overlap_demucs} \
          --overlap_VOCFT {overlap_VOCFT} \
          --overlap_InstVoc {int(overlap_InstVoc)} \
          --overlap_VitLarge {int(overlap_VitLarge)} \
          --output_format {output_format} \
          --BigShifts {BigShifts} \
          --output_folder "{output_folder}" \
          {vocals_only} \
          {use_VOCFT}

这里默认使用google云盘的目录，也可以修改为当前服务器的目录地址。

结语

MVSEP-MDX23 和 Spleeter 都是音频人声背景音分离软件，作为用户，我们到底应该怎么选择？

MVSEP-MDX23 基于 Demucs4 和 MDX 神经网络架构，可以将音乐分离成“bass”、“drums”、“vocals”和“other”四个部分。MVSEP-MDX23 在 2023 年的音乐分离挑战中获得了第三名，并且在 MultiSong 数据集上的质量比较中表现出色。它提供了 Python 命令行工具和 GUI 界面，支持 CPU 和 GPU 加速，可以在本地运行。

Spleeter 是由 Deezer 开发的开源音频分离库，它使用深度学习模型将音频分离成不同的音轨，如人声、伴奏等。Spleeter 提供了预训练的模型，可以在命令行或作为 Python 库使用。它的优势在于易用性和灵活性，可以根据需要分离不同数量的音轨。

总的来说，MVSEP-MDX23 在音频分离的性能和精度上表现出色，尤其适合需要高质量音频分离的专业用户。而 Spleeter 则更适合普通用户和开发者，因为它易于使用，并且具有更多的定制选项。