
Audial Mcp
io.github.AudialAI由 GitHub 帳號 AudialAI 發布v0.7.0更新於 Nov 7, 2026
Audial audio tools: stems, song analysis, tags, lyrics, mastering, sample packs, MIDI, music, vocals
概覽
在 MCP 用戶端中呼叫 Audial 的託管音訊工具:分離音軌、歌曲分析、標籤、歌詞、母帶處理、MIDI、取樣包、預設與人聲演唱。
- 可以做什麼
- 把曲目分離成人聲、鼓、貝斯和其他聲部,並可參照參考曲目做母帶處理。
- 分析歌曲的 BPM、調性、段落、速度圖、音訊特徵,以及曲風、情緒和樂器標籤。
- 擷取帶時間軸的歌詞,並把音訊轉成 MIDI。
- 產生取樣包、生成音樂、翻唱與混音,並把單一音色轉成 Audial Synth 或 Serum 2 預設。
- 用參考音色演唱歌詞,可依音符控制音節與延音。
- 查詢流程與服務狀態,並瀏覽本機結果資料夾中的歷史結果。
- 適用情境
- 適合讓助理直接執行音樂製作與分析工作,例如分離音軌、描述曲目、母帶處理、製作 MIDI 或取樣包、生成人聲,而不必離開聊天用戶端。適合已有 Audial 訂閱的製作人與音訊開發者。
- 執行需求
- 在桌面用戶端上以 uvx 啟動的本機程序(需要 Python 與 uv)。需要具備有效訂閱的 Audial 帳號;首次使用時透過瀏覽器登入,伺服器或 CI 環境可設定 AUDIAL_API_KEY(舊版金鑰還需 AUDIAL_USER_ID)。選用設定:AUDIAL_RESULTS_DIR(預設 ~/Audial)、AUDIAL_JOB_TIMEOUT_S、AUDIAL_API_BASE_URL。需要連線至 Audial API 的網路。
安裝
在 SourceWeft 中
- 開啟 儀表板中的 Audial Mcp,將其新增到工作區。
- 為需要使用其工具的對話啟用該服務。
Desktop only,透過 STDIO。 STDIO 服務會啟動本機處理程序,因此需要 SourceWeft 桌面主機。
其他 MCP 客戶端
參照 儲存庫 中的啟動說明。
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
來源:README.md,提交 ba05150
工具
0版本歷史
3- v0.7.0最新Nov 7, 2026
- v0.5.0Oct 30, 2026
- v0.1.1Sep 29, 2026
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