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音频音轨分离器 (Demucs)

使用 Meta 的 Demucs 神经网络模型,将音频文件分离为独立的音轨(人声、鼓、贝斯、其他)。

音频音轨分离器 (Demucs) 是什么?

该技能利用 Meta 的 Demucs 神经网络模型以及 Python 的 demucs 包,将音频文件分离为独立的音轨(人声、鼓、贝斯、其他)。它支持批量处理 WAV 和 MP3 文件,输出分隔后的音轨为 FLAC 或 WAV 格式。此外,该技能集成了 FFmpeg,可在分离后执行格式转换和响度匹配操作。此技能适用于 Claude Code、Cursor 等 AI 代理环境,用户可通过命令行或脚本调用该技能进行处理。其工作流程包括:加载音频、运行 Demucs 模型进行预测、提取各音轨分量,并利用 FFmpeg 进行后处理,最终输出独立的音频文件。该技能依赖 Python 3.8 以上版本,并提供了针对不同硬件(CPU/GPU)的环境配置选项。

兼容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/agentskillexchange/skills/tree/main/skills/audio-stem-separator-demucs

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文档

音频音轨分离器 (Demucs) 是做什么的?

使用 Meta 的 Demucs 神经网络模型(通过 demucs Python 包),将音频文件分离为独立的音轨(人声、鼓、贝斯、其他)。支持批量处理 WAV 和 MP3 文件,输出分隔后的音轨为 FLAC 或 WAV 格式,并且集成 FFmpeg 进行格式转换和分离后的响度匹配。

安装

根据您的环境选择上游安装或配置路径:

  • conda env update -f environment-cpu.yml # 如果没有 GPU
  • conda env update -f environment-cuda.yml # 如果有 GPU
  • conda activate demucs
  • pip install -e .

上游的要求和注意事项:

  • 需要自定义 CUDA 代码,但目前尚未准备好发布。
  • 至少需要 Python 3.8。仅用于分离时,请参阅 requirements_minimal.txt。
  • 每次看到 python3,请替换为 python.exe。应始终从以下目录运行命令:

基本使用入门说明:

来源

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