Metadata-Version: 2.4
Name: audacity-mcp
Version: 0.1.4
Summary: MCP server for AI-driven audio editing in Audacity via mod-script-pipe
Author: Daniel Hodgetts
License-Expression: Apache-2.0
License-File: LICENSE
Keywords: ai,audacity,audio,mastering,mcp,model-context-protocol,transcription
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Multimedia :: Sound/Audio
Classifier: Topic :: Multimedia :: Sound/Audio :: Editors
Requires-Python: >=3.10
Requires-Dist: faster-whisper>=1.0.0
Requires-Dist: mcp[cli]>=1.0.0
Provides-Extra: dev
Requires-Dist: pytest-asyncio>=0.21; extra == 'dev'
Requires-Dist: pytest>=7.0; extra == 'dev'
Description-Content-Type: text/markdown

<h1 align="center">AudacityMCP</h1>

<p align="center">
  <strong>AI-powered audio editing in Audacity through the Model Context Protocol</strong>
</p>

<p align="center">
  <a href="#quick-start">Quick Start</a> &bull;
  <a href="docs/INSTALLATION.md">Installation</a> &bull;
  <a href="#pipelines">Pipelines</a> &bull;
  <a href="docs/TOOLS.md">Tool Reference</a> &bull;
  <a href="#troubleshooting">Troubleshooting</a> &bull;
  <a href="#support">Support</a>
</p>

---

AudacityMCP connects any MCP-compatible AI assistant to [Audacity](https://www.audacityteam.org/), giving it full control over audio editing through 131 tools spanning effects, cleanup, mastering, transcription, and more. Talk to your AI assistant and it edits your audio in real-time.

**No cloud. No API keys for audio processing. Everything runs locally through Audacity's named pipe interface.**

## What Can It Do?

```
You:  "Clean up this podcast recording"
AI:   Runs auto_cleanup_podcast → HPF 80Hz → noise reduction → compression → safe loudness check

You:  "Master this track for Spotify, it's EDM"
AI:   Runs auto_master_music style=edm → HPF 30Hz → click removal → compression 2.5:1 → bass +2dB → loudness check

You:  "This is a noisy live recording, fix it up"
AI:   Runs auto_cleanup_live → HPF 100Hz → click removal → noise reduction 18dB → compression 5:1

You:  "Transcribe this interview and add labels"
AI:   Runs transcribe_to_labels → faster-whisper transcription → Audacity labels at each timestamp

You:  "Add reverb to the vocals, then export as FLAC"
AI:   select region → reverb effect → export to FLAC
```

## Features

### 131 Tools Across 11 Categories

| Category | Tools | Highlights |
|----------|-------|------------|
| **Effects** | 30 | Reverb, echo, pitch shift, tempo change, EQ, phaser, distortion, paulstretch, HPF/LPF, bass & treble, tremolo, wahwah |
| **Cleanup & Mastering** | 18 | Noise reduction, compressor, limiter, 9 one-click pipelines, analysis tool |
| **Editing** | 13 | Cut, copy, paste, split, join, trim, silence, duplicate, undo, redo |
| **Project** | 12 | New, open, save, import/export (WAV, MP3, FLAC, OGG, AIFF) |
| **Track** | 15 | Add mono/stereo, remove, set properties, mix & render, mute/solo, pan, volume |
| **Selection** | 12 | Select all/none/region/tracks, zero crossing, cursor positioning |
| **Transport** | 7 | Play, stop, pause, record, play region, get position |
| **Analysis** | 6 | Contrast, clipping detection, spectrum, beat finder, sound labeling |
| **Generation** | 5 | Tone, noise, chirp, DTMF, rhythm track |
| **Transcription** | 7 | Full/selection transcribe, to labels, to SRT/VTT/TXT, model preload |
| **Labels** | 6 | Add, add at time, get all, edit, import/export |

---

## Pipelines

AudacityMCP includes 9 one-click pipelines for common audio tasks. Each pipeline is designed to be **safe for badly recorded audio** — it will never boost your audio dangerously. Pipelines clean up and improve your audio, then you can manually adjust loudness afterward if needed.

### How Pipelines Work

1. You tell the AI what you want (e.g. "clean up this podcast")
2. The AI picks the right pipeline and starts it
3. The pipeline runs in the background — you get a `job_id` back
4. Poll with `check_pipeline_status` every 15-30 seconds to monitor progress
5. When done, a popup appears in Audacity

> **Safety rule:** Pipelines only **reduce** peaks if they're too hot. They **never boost** loudness. If you want to hit a specific LUFS target (e.g. -14 for Spotify), ask the AI to run `loudness_normalize` after you've checked the results look good.

### Pipeline Reference

#### `auto_analyze_audio` — Analyze before processing

Measures your audio and recommends the best pipeline. Run this first if you're not sure what to do.

```
You: "Analyze this audio"
→ Returns: peak level, noise floor, clipping status, recommended pipeline
```

#### `auto_cleanup_audio` — Safe cleanup only

Cleans up noise and artifacts **without changing loudness at all**. Use when levels are already fine.

```
You: "Just clean up the noise, don't change the volume"
→ DC offset removal → HPF 80Hz → noise reduction → click removal (optional)
```

#### `auto_cleanup_podcast` — Podcast / voiceover

Professional broadcast processing for spoken word.

```
You: "Clean up this podcast recording"
→ DC offset → HPF 80Hz → noise reduction 12dB → compression 3:1 → safe loudness check
```

#### `auto_audiobook_mastering` — Audiobook (ACX/Audible)

Targets ACX requirements for audiobook distribution.

```
You: "Master this for ACX / Audible"
→ DC offset → HPF 80Hz → noise reduction 12dB → compression 2.5:1 → safe loudness check → peak cap -3dB
```

#### `auto_cleanup_interview` — Interview / dialogue

Light touch for conversations — preserves natural dynamics.

```
You: "Clean up this interview recording"
→ DC offset → HPF 80Hz → noise reduction 8dB → compression 2.5:1 → safe loudness check
```

#### `auto_cleanup_vocal` — Singing / studio vocal

Tuned for vocal recordings with presence EQ for clarity.

```
You: "Process this vocal recording"
→ DC offset → HPF 100Hz → noise reduction 10dB → compression 3:1 → presence EQ (+3dB treble, -1dB bass) → safe loudness check
```

#### `auto_cleanup_live` — Live / field / noisy recording

Aggressive cleanup for noisy environments. First 0.5s **must** be ambient noise for profiling.

```
You: "This is a noisy live recording, clean it up"
→ DC offset → HPF 100Hz → click removal → noise reduction 18dB → compression 5:1 → safe loudness check
```

#### `auto_master_music` — Music mastering

Genre-tuned mastering with 6 presets: `edm`, `hiphop`, `rock`, `pop`, `classical`, `acoustic`.

```
You: "Master this hip-hop track"
→ HPF 30Hz → click removal → compression 2:1 → bass +3dB treble +1dB → safe loudness check

You: "Master this for a classical album"
→ HPF 30Hz → click removal → compression 1.3:1 (very gentle) → no EQ → safe loudness check
```

| Preset | HPF | Compression | Bass EQ | Treble EQ |
|--------|-----|-------------|---------|-----------|
| **EDM** | 30 Hz | 2.5:1 / 80ms | +2 dB | +1 dB |
| **Hip-Hop** | 30 Hz | 2:1 / 100ms | +3 dB | +1 dB |
| **Rock** | 40 Hz | 2:1 / 100ms | 0 dB | +1 dB |
| **Pop** | 35 Hz | 2:1 / 80ms | +1 dB | +1.5 dB |
| **Classical** | 30 Hz | 1.3:1 / 200ms | 0 dB | 0 dB |
| **Acoustic** | 30 Hz | 1.5:1 / 150ms | 0 dB | 0 dB |

#### `auto_lofi_effect` — Creative lo-fi / vintage

Apply a warm, vintage lo-fi sound. Presets: `light`, `medium`, `heavy`.

```
You: "Give this a lo-fi vibe"
→ HPF → LPF (muffled highs) → bass/treble warmth → compression 2:1 → safe loudness check
```

### After a Pipeline: Adjusting Loudness

Pipelines intentionally leave loudness alone (they only reduce if peaks are clipping). To hit a streaming target:

```
You: "Now normalize this to -14 LUFS for Spotify"
→ AI uses loudness_normalize tool with lufs_level=-14

You: "Normalize to -16 LUFS for podcast"
→ AI uses loudness_normalize tool with lufs_level=-16
```

> **Why not do this automatically?** LUFS normalization can boost quiet/badly recorded audio by 10-20 dB, which blows it out. By separating cleanup from loudness, you get to check the results before the final loudness step.

---

### Local Transcription

Powered by [faster-whisper](https://github.com/SYSTRAN/faster-whisper) — runs entirely offline:

- 5 model sizes: `tiny`, `base`, `small`, `medium`, `large-v3`
- Transcribe full audio or just a selection
- Export as SRT, VTT, or plain text subtitles
- Auto-add Audacity labels at each spoken segment
- Language detection or specify 99+ languages

---

## Quick Start

### One-Click Install

Download and run the installer — it installs everything and configures Claude Desktop automatically:

- **Windows:** Download [`install.bat`](install.bat) → double-click it
- **macOS / Linux:** `curl -fsSL https://raw.githubusercontent.com/xDarkzx/Audacity-MCP/main/install.sh | bash`

The installer:
1. Checks Python is installed
2. Installs `audacity-mcp` from PyPI
3. Creates/updates your Claude Desktop config
4. Tells you exactly what to do next

### Manual Install (3 steps)

<details>
<summary>Click to expand manual installation steps</summary>

**Prerequisites:** Python 3.10+ and Audacity 3.x

**Step 1:** Enable Audacity's scripting plugin

Open Audacity → Edit → Preferences → Modules → set `mod-script-pipe` to **Enabled** → OK → **restart Audacity**

**Step 2:** Install from PyPI

```bash
pip install audacity-mcp
```

That's it. No git clone needed.

**Step 3:** Configure your MCP client

Add to your Claude Desktop config (`%APPDATA%\Claude\claude_desktop_config.json` on Windows, `~/Library/Application Support/Claude/claude_desktop_config.json` on macOS):

```json
{
  "mcpServers": {
    "audacity": {
      "command": "audacity-mcp"
    }
  }
}
```

Restart Claude Desktop.

> **Other MCP clients** (Claude Code, Cursor, etc.) — see the full [Installation Guide](docs/INSTALLATION.md)

</details>

### Use It

> **Audacity must be open first.** Open it yourself, load your audio, then chat with your AI client.

```
"Clean up this podcast recording"
"Master this track for Spotify, it's EDM"
"Transcribe this interview and add labels"
"Add reverb with a large room, then export as FLAC"
```

> See the full [Installation Guide](docs/INSTALLATION.md) for detailed setup on all platforms and MCP clients.

---

## Troubleshooting

### Connection Issues

| Problem | Fix |
|---------|-----|
| "Pipe not found" | Open Audacity first. Make sure `mod-script-pipe` is enabled in Edit → Preferences → Modules. Restart Audacity after enabling. |
| "Pipe timeout" | Audacity is busy. Wait for it to finish — some effects take minutes on long files. |
| Connection works once then fails | The pipe disconnected (Audacity crash or restart). Just try again — AudacityMCP auto-reconnects. |
| "Access denied" (Windows) | Audacity and your AI client must run as the same user. Don't mix admin and non-admin. |

### Pipeline Issues

| Problem | Fix |
|---------|-----|
| Pipeline blows out / clips the audio | This shouldn't happen anymore — pipelines only reduce peaks, never boost. If it does, undo (Ctrl+Z) and report the issue. |
| "A pipeline is already running" | Only one pipeline can run at a time. Use `check_pipeline_status` with your job_id to monitor the current one. |
| Pipeline finishes but audio is too quiet | That's by design — pipelines don't boost. Ask the AI: "Normalize to -14 LUFS" after checking results. |
| Noise reduction sounds metallic/warbled | The first 0.5 seconds of your track must be pure silence/room noise for profiling. If it's not, trim to add silence or use `auto_cleanup_audio` with `remove_noise=False`. |
| Pipeline step failed (in warnings) | Individual steps can fail without stopping the pipeline. Check the `warnings` field in `check_pipeline_status` for details. |

### Audio Quality Tips

| Want | Do This |
|------|---------|
| Remove background noise | Make sure the first 0.5s of your track is pure room tone (no speech/music). The pipeline uses this to build a noise profile. |
| Fix clipping | Run `auto_analyze_audio` first. If it detects clipping, use `auto_cleanup_audio` before other pipelines. |
| Hit -14 LUFS for Spotify | Run a cleanup pipeline first, check the results look good, then ask the AI to apply `loudness_normalize` at -14 LUFS. |
| Hit -16 LUFS for podcast | Same approach — cleanup first, LUFS second. |
| ACX audiobook compliance | Use `auto_audiobook_mastering`. It targets RMS -20 dB with a -3 dB peak cap. |
| Quick cleanup without changing volume | Use `auto_cleanup_audio` — it only removes noise and artifacts, no loudness changes. |

### General Issues

| Problem | Fix |
|---------|-----|
| "No module named faster_whisper" | Run `pip install faster-whisper`. Transcription is optional — everything else works without it. |
| Model download fails | Check internet and retry. Models cache locally after first download. |
| Pipes missing in /tmp (macOS/Linux) | Check Audacity is running and mod-script-pipe is enabled. Check Audacity's console for errors. |

---

## Architecture

```
┌──────────────┐     stdio      ┌──────────────┐   named pipe   ┌──────────────┐
│  MCP Client  │◄──────────────►│ AudacityMCP  │◄──────────────►│   Audacity   │
│(AI assistant)│    (JSON-RPC)  │   FastMCP    │  (commands)    │              │
└──────────────┘                └──────────────┘                └──────────────┘
                                       │
                                       ├── server/main.py          (entry point)
                                       ├── server/audacity_client.py (pipe I/O)
                                       ├── server/tool_registry.py  (auto-loader)
                                       └── server/tools/            (11 modules)
```

### Key Design Decisions

- **Named pipes, not TCP** — Direct IPC to Audacity's `mod-script-pipe`. No network exposure, no port conflicts.
- **Zero `exec`/`eval`** — Every operation maps to a static handler with input validation. No arbitrary code execution.
- **Cross-platform** — Windows uses Win32 API via ctypes, Unix uses standard file I/O.
- **Async throughout** — All tool handlers are `async`. Blocking pipe I/O runs in an executor pool with configurable timeouts.
- **Safe pipelines** — Pipelines measure audio before making loudness decisions. They only reduce, never boost.
- **Dynamic tool registration** — Drop a module in `server/tools/`, export a `register(mcp)` function, and it's automatically discovered.

### Project Structure

```
AudacityMCP/
├── server/
│   ├── main.py                 # FastMCP server entry point
│   ├── audacity_client.py      # Cross-platform named pipe client
│   ├── tool_registry.py        # Auto-discovers and registers tool modules
│   └── tools/
│       ├── analysis_tools.py   # Audio analysis (contrast, spectrum, beats)
│       ├── cleanup_tools.py    # Noise reduction, mastering, 9 pipelines
│       ├── edit_tools.py       # Cut, copy, paste, split, join, trim
│       ├── effects_tools.py    # Reverb, echo, pitch, EQ, filters
│       ├── generate_tools.py   # Tone, noise, chirp, DTMF generation
│       ├── label_tools.py      # Label management
│       ├── project_tools.py    # Project/file operations
│       ├── selection_tools.py  # Selection and cursor control
│       ├── track_tools.py      # Track management
│       ├── transcription_tools.py  # Whisper-based transcription
│       └── transport_tools.py  # Playback and recording control
├── shared/
│   ├── constants.py            # Pipe paths, timeouts, allowed formats
│   ├── error_codes.py          # Typed error codes (pipe/command/validation)
│   └── pipe_protocol.py        # Command formatting and response parsing
├── tests/
│   └── ...                     # 60 tests
├── docs/
│   ├── INSTALLATION.md         # Detailed setup guide
│   └── TOOLS.md                # Complete tool reference
└── pyproject.toml
```

---

## Development

```bash
# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest tests/ -x -q
```

### Adding New Tools

1. Create a module in `server/tools/` (or add to an existing one)
2. Export a `register(mcp: FastMCP)` function
3. Define your tools with `@mcp.tool()` decorators
4. That's it — the tool registry auto-discovers it on startup

```python
# server/tools/my_tools.py
from mcp.server.fastmcp import FastMCP
from shared.error_codes import AudacityMCPError, ErrorCode


def register(mcp: FastMCP):
    from server.main import client

    @mcp.tool()
    async def my_custom_effect(intensity: float = 0.5) -> dict:
        """Apply my custom effect to the selected audio."""
        if not 0 <= intensity <= 1:
            raise AudacityMCPError(ErrorCode.VALUE_OUT_OF_RANGE, "intensity must be 0-1")
        return await client.execute_long("MyEffect", Intensity=intensity)
```

See [CONTRIBUTING.md](CONTRIBUTING.md) for full guidelines.

---

## Support

If AudacityMCP has saved you time or helped with your audio projects, consider buying me lunch:

<p align="center">
  <a href="https://www.paypal.com/send?recipient=daehonz@gmail.com">
    <img src="https://img.shields.io/badge/PayPal-Buy_Me_Lunch-00457C?style=for-the-badge&logo=paypal&logoColor=white" alt="PayPal" />
  </a>
</p>
buymeacoffee.com
Your support helps keep this project maintained and free for everyone.

---

## Documentation

- **[Installation Guide](docs/INSTALLATION.md)** — Detailed setup for Windows, macOS, Linux
- **[Tool Reference](docs/TOOLS.md)** — Complete reference for all 131 tools with parameters
- **[Contributing](CONTRIBUTING.md)** — How to add tools and contribute

## License

Apache License 2.0 — see [LICENSE](LICENSE) for details.

Built by [Daniel Hodgetts](https://github.com/xDarkzx).
