
Audial Mcp
io.github.AudialAI由 GitHub 账号 AudialAI 发布v0.7.0更新于 Nov 7, 2026
Audial audio tools: stems, song analysis, tags, lyrics, mastering, sample packs, MIDI, music, vocals
概览
在 MCP 客户端中调用 Audial 的托管音频工具:分离音轨、歌曲分析、标签、歌词、母带处理、MIDI、采样包、预设与人声演唱。
- 可以做什么
- 把曲目分离成人声、鼓、贝斯和其他声部,并可参照参考曲目做母带处理。
- 分析歌曲的 BPM、调性、段落、速度图、音频特征,以及流派、情绪和乐器标签。
- 提取带时间轴的歌词,并把音频转成 MIDI。
- 生成采样包、生成音乐、翻唱与混音,并把单个音色转成 Audial Synth 或 Serum 2 预设。
- 用参考音色演唱歌词,可按音符控制音节与延音。
- 查询流水线与服务状态,并浏览本地结果文件夹中的历史结果。
- 适用场景
- 适合让助手直接执行音乐制作与分析任务,例如分离音轨、描述曲目、母带处理、制作 MIDI 或采样包、生成人声,而无需离开聊天客户端。适合已有 Audial 订阅的制作人和音频开发者。
- 运行要求
- 在桌面客户端上以 uvx 启动的本地进程(需要 Python 与 uv)。需要带有效订阅的 Audial 账户;首次使用时通过浏览器登录,服务器或 CI 环境可设置 AUDIAL_API_KEY(旧版密钥还需 AUDIAL_USER_ID)。可选设置:AUDIAL_RESULTS_DIR(默认 ~/Audial)、AUDIAL_JOB_TIMEOUT_S、AUDIAL_API_BASE_URL。需要访问 Audial API 的网络。
安装
在 SourceWeft 中
- 打开 控制台中的 Audial Mcp,将其添加到工作区。
- 为需要使用其工具的对话启用该服务。
Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。
其他 MCP 客户端
参照 仓库 中的启动说明。
README
audial-mcp
Powered by Audial — run Audial's hosted audio tools from any MCP client: split stems, analyze audio, find a song's sections, tempo and bars and add audio features, tags or timed lyrics to that analysis, master tracks, build sample packs, convert audio to MIDI, generate music, turn a one-shot into an editable Audial Synth or Serum 2 preset, and sing lyrics in a reference voice. See the API reference for details on what each tool calls.
mcp-name: io.github.AudialAI/audial-mcp
Install
You need an Audial account (https://audialmusic.ai?utm_source=github&utm_medium=readme&utm_campaign=mcp) and
uv. There is no API key to copy:
the first time a tool runs, a sign-in page opens in your browser. Approve it and the tool
carries on.
Claude Code
Claude Desktop, one click
Download audial-mcp-<version>.mcpb from the latest release and double-click it. Claude Desktop fetches what it needs, so there is nothing else to install or configure.
Claude Desktop / Cursor / any client with a JSON config
Signing in
The sign-in is saved on your computer (~/.audial/credentials.json, readable only by you) and
shared with the audial command line and Python SDK, so audial login in a terminal signs
the MCP server in too. If you have not approved within two minutes the tool returns the
sign-in link instead; approve it and ask again. The account, sign_in and sign_out tools
show and change who is signed in, and each connected computer is listed, and can be
disconnected, under Apps & API Keys in your profile at audialmusic.ai.
On a server or in CI, where nobody can sign in, set AUDIAL_API_KEY to a key created on that
page. A key set this way takes priority over a saved sign-in.
Tools
Analysis and add-ons
segment analyses the mix in about half a minute: no stems needed. It returns the sections
(with their phrases) and a tempo map, plus an analysis_id. Beats and bars aren't listed one by
one: grid describes them -- where each constant-tempo piece starts, its BPM, and its bar and
beat. Every beat time is start + k × 60 / bpm. Most tracks have one piece.
Pass the analysis_id to audio_features, tag_audio, get_lyrics or generate_samples to
add that result to the analysis, whenever you like. Nothing is uploaded again. Each of them also
takes a file_path instead, and then runs segment on it first.
- Audio features need nothing more, so they can run straight after the analysis.
- Tags, lyrics and the sample pack read the track's stems. Audial makes them automatically after each analysis, so right after a new one these tools wait a few minutes for them.
- If a tool analysed a file and the next step failed or timed out, the error gives the new
analysis_id: call the tool again with it, rather than with the file.
An analysis counts as one run. Everything you add to it (features, tags, lyrics, a sample pack) is included.
generate_samples on a file runs as an Audial pipeline: it prepares the track (analysis and
stems together), then builds the pack, as one job. Each step is reported as it starts and
finishes ("prepare completed in 41 s; sample-pack running"). The pipeline id comes back as the
result's execution_id -- pass it to pipeline_status to check on a job whose call timed out.
segment with features (kept for older integrations) is a pipeline as well.
Per-note lyrics
text2vox lines lyrics up with a melody. lyrics_mode="per_note" is the mode that gives
exact control: the lyrics are pipe-delimited, one token per MIDI note, and a hold keeps
the previous syllable going through the next note.
That sings "amazing" across notes 1-3 and "grace" across notes 4-5, and needs a MIDI file with exactly nine notes. There is no rest token: a note that should be silent should not be in the MIDI. The token count must equal the note count, and the first token must be a syllable -- a lyric string that breaks either rule comes back as a tool error naming the note it is about ("note 1 has no syllable; the first note needs one (position 1)"), before anything is uploaded.
Cold starts
Audial's workers scale to zero, so the first job after an idle period spends extra time
starting one and loading the model -- a few minutes on a GPU tool; every job after it is fast.
The analysis and each add-on (audio_features, tag_audio, get_lyrics) run on workers of
their own, so the first add-on after a quiet spell can start up for a minute or more even when
segment was quick. Audial reports that phase on the job it is already polling, and every
tool here relays it as a progress notification and a log line:
It is the same sentence the Audial website, CLI and plugins show. service_status answers
the same question before you start anything, so a long wait can be explained in advance.
What leaves your machine
Audio and text you pass to a tool are uploaded to Audial's API (https://api.audialmusic.ai)
over HTTPS and processed on Audial's servers; results are downloaded into
AUDIAL_RESULTS_DIR (default ~/Audial). Nothing else is sent.
A tool given an analysis_id uploads nothing: it works on the audio Audial already has for
that analysis. Audial makes and keeps the track's stems with each analysis, like your
other results. To fill in artist tags, tag_audio has Audial's servers look up the track's
title and artist in a public music database; that lookup gets the title and artist, never the
audio.
Two other files are read if they exist, because the Audial SDK looks for them:
~/.audial/.audial_config.json and a .env in the directory the server was started from.
Credentials from your MCP client's config always win: audial-mcp snapshots its environment
before the SDK loads either file, so neither can redirect your credentials or the API host.
Every tool except list_results calls the Audial API, which requires an active Audial subscription on the account; without one the tool returns the API's subscription message.
Configuration
License
MIT. Source: https://github.com/AudialAI/audial-mcp
来源:README.md,提交 ba05150
工具
0版本历史
3- v0.7.0最新Nov 7, 2026
- v0.5.0Oct 30, 2026
- v0.1.1Sep 29, 2026
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