whisper-windows-mcp

A Windows-native MCP (Model Context Protocol) server that lets Claude Desktop transcribe audio and video files locally using whisper.cpp — with GPU acceleration, multilingual support, and batch processing. All transcription runs locally — no audio, video, or file paths ever leave your machine.

> Why does this exist?

> The popular `whisper-mcp` package was built for macOS and assumes a Unix environment. It does not work on Windows. This package was written specifically for Windows users who want local AI transcription integrated with Claude Desktop.

What you can do with it

Once installed, you can say things like this directly in Claude Desktop:

• *"Transcribe C:\Users\Me\Downloads\meeting.mp3"*
• *"Transcribe this folder of recordings and save each as a text file"*
• *"Generate Japanese and English subtitles for this video"*
• *"Start a batch transcription of everything in this folder"*
• *"How long will it take to transcribe these files?"*
• *"Check if GPU acceleration is working"*
• *"Transcribe this file in privacy mode"*

Requirements

1. Node.js 18 or later — nodejs.org

2. whisper.cpp binaries with Vulkan GPU support — see Step 1

3. A Whisper model file — see Step 2

4. FFmpeg — required for video files and non-WAV/MP3 audio

Step 1 — Install whisper.cpp binaries

Option A — Pre-built Vulkan release (recommended)

Download `whisper-vulkan-win-x64.zip` from the releases page.

This is a custom-compiled build with Vulkan GPU acceleration enabled. Works with AMD, NVIDIA, and Intel GPUs — no vendor-specific SDK required.

Extract to `C:\whisper\Release\`. You should end up with:

C:\whisper\Release\whisper-cli.exe
C:\whisper\Release\ggml-vulkan.dll
C:\whisper\Release\ggml.dll
C:\whisper\Release\ggml-base.dll
C:\whisper\Release\ggml-cpu.dll
C:\whisper\Release\whisper.dll

GPU acceleration is automatic — no additional configuration needed.

Option B — Build from source

Requires: Git, CMake, Visual Studio Build Tools 2022+ with "Desktop development with C++", Vulkan SDK from lunarg.com.

git clone https://github.com/ggml-org/whisper.cpp
cd whisper.cpp
cmake -B build -DGGML_VULKAN=ON -DCMAKE_BUILD_TYPE=Release
cmake --build build --config Release --target whisper-cli

Copy the binaries from `build\bin\Release\` to `C:\whisper\Release\`.

> Note: The official whisper.cpp Windows releases on GitHub do not include a Vulkan build. You must use the pre-built release above or compile from source with `-DGGML_VULKAN=ON`.

Step 2 — Download a Whisper model

Use `download_model` in Claude Desktop to install any of these directly. For English-only use: `large-v3-turbo` (GPU) or `medium.en-q5_0` (CPU) are the best starting points. For multilingual use: `large-v3-turbo` or `large-v3-turbo-q5_0` (CPU). English-only models (`*.en.bin`) output `[FOREIGN]` on non-English audio and cannot be used for other languages.

Step 3 — Install FFmpeg

FFmpeg is required for video files and non-native audio formats.

Install via winget:

winget install ffmpeg

Or download from ffmpeg.org and add to your PATH.

Verify:

ffmpeg -version

Step 4 — Install this MCP server

npm install -g whisper-windows-mcp

Step 5 — Configure Claude Desktop

Open Claude Desktop → Settings → Developer → Edit Config.

Add the `whisper` entry:

{
  "mcpServers": {
    "whisper": {
      "command": "npx",
      "args": ["-y", "whisper-windows-mcp"],
      "env": {
        "WHISPER_CLI_PATH": "C:\\whisper\\Release\\whisper-cli.exe",
        "WHISPER_MODEL": "C:\\whisper\\models\\ggml-medium.en.bin"
      }
    }
  }
}

Config file location: `C:\Users\YourName\AppData\Roaming\Claude\claude_desktop_config.json`

> Use double backslashes in all paths.

Save and fully restart Claude Desktop. You should see whisper listed with a green running badge in Settings → Developer.

Step 6 — Verify your setup

In Claude Desktop, ask:

> *"Check your whisper config"*

Then:

> *"Check your system hardware"*

This confirms your GPU is detected and Vulkan acceleration is active.

Available tools

`transcribe_audio`

Transcribe a single file. Supports blocking (default) or background mode for long files.

Output formats:

• `timestamps` — timestamped segments, e.g. `[00:00:01.230 --> 00:00:04.560] Hello world` (default)
• `text` — plain text, no time codes
• `json` — structured JSON (blocking mode only)
• `srt` — SubRip subtitle file saved next to source
• `vtt` — WebVTT subtitle file saved next to source
• `lrc` — LRC lyrics/karaoke format saved next to source
• `csv` — CSV with timestamps saved next to source

`check_progress`

Monitor a background transcription job started with `transcribe_audio` (background=true).

Returns elapsed time, last processed timestamp, and the full transcript when complete.

`start_batch`

Automated sequential batch transcription of all untranscribed files in a folder. Sorts by duration (shortest first), processes one at a time as background jobs, validates each output. Batch self-advances when each file finishes — no polling required.

`check_batch_progress`

Monitor a running batch. Automatically advances to the next file when the current one finishes. Returns overall progress, current file with timestamp, and any failed files.

`transcribe_batch` (interactive)

Process files one at a time with a preview and confirmation before each. Useful when you want to review as you go.

`generate_subtitles`

Generate subtitle files. Supports automatic language detection and English translation output. Outputs SRT (widest compatibility) or WebVTT (web and HTML5 video).

When both native and translation are requested, two files are saved next to the source:

• `filename.ja.srt` — original language
• `filename.en.srt` — English translation

> Whisper's built-in translation only translates to English. For other target languages, translate the subtitle file contents separately.

`analyze_media`

Analyze files before committing to transcription. Returns duration, size, codec, and estimated transcription time on CPU and GPU. For folders, shows all files in a sortable table with transcription status.

`check_config`

Verify whisper-cli.exe, the model file, and FFmpeg are all accessible. Run this first if anything is failing.

`list_models`

List all Whisper model files installed in your models directory. Shows filename, size, whether it is currently active, quantization status, and recommended use case. No network calls — reads local filesystem only.

`download_model`

Download a Whisper model directly from Hugging Face into your models directory. Only downloads from trusted Hugging Face namespaces. After downloading, use `switch_model` to activate it.

`switch_model`

Switch the active Whisper model for the current session without restarting Claude Desktop. Change is session-scoped — does not persist after restart. To make permanent, update `WHISPER_MODEL` in your config.

`check_system`

Detect GPU hardware and verify Vulkan acceleration is available. Reports GPU name, VRAM, whether `ggml-vulkan.dll` is present, and recommends the best model size for your hardware.

`whisper_server`

Start, stop, or check the persistent model server (whisper.cpp's `whisper-server`). While running, the active model stays resident in VRAM and every `transcribe_audio` / `transcribe_batch` call is served over localhost with no per-file model reload — a large speedup when transcribing many short files, where the one-time model-load cost otherwise dominates.

• ⚠️ The resident model holds GPU VRAM for the server's whole lifetime. Start it deliberately, do your work, then `stop` it to hand the GPU back to other applications sharing the card. Stopping performs a full kill so VRAM is actually released.
• `switch_model` while the server is running hot-swaps the resident model in place (no restart).
• Bound to `127.0.0.1` only — never exposed on the network.
• While the server is up, operations that need the one-shot CLI — background jobs, `start_batch`, `generate_subtitles`, `lrc`/`csv` output, and advanced per-call options the HTTP API doesn't honor (`beam_size`, `best_of`, `word_timestamps`, `diarize`, `tinydiarize`, `vad_model`, `offset_t`, `duration`) — are refused with a "stop the server first" message rather than silently ignored, so no second engine ever contends for the GPU.
• Requires `whisper-server.exe` (ships alongside `whisper-cli.exe`). Configure with `WHISPER_SERVER_PATH` / `WHISPER_SERVER_PORT` if needed.

Supported formats

GPU acceleration

The pre-built Vulkan release enables GPU acceleration automatically. Tested on AMD Radeon RX Vega 56 (GCN 5th gen). Any GPU with Vulkan 1.0+ support should work, including NVIDIA and Intel Arc.

Performance comparison (large-v3 model, ~14 minute audio file):

GPU utilization during transcription is typically 15–20%, dropping back to idle between files.

Supports Windows 10 and Windows 11. No Windows 11-specific configuration is required — the tool makes no Win32 API calls and runs on either OS.

Multilingual support

Whisper can auto-detect the spoken language and transcribe in that language. The built-in translation model translates to English only.

For best multilingual accuracy, use the `large-v3` model. English-specific models (`*.en.bin`) cannot detect or transcribe other languages.

Example — foreign language video with subtitles:

1. Ask Claude to generate subtitles with `language=auto` and `translate_to_english=true`

2. Whisper detects the language and generates a native-language SRT or VTT

3. A second pass generates an English translation

4. Load the SRT in VLC via Subtitle → Add Subtitle File, or use the VTT in any web player

Privacy and compliance

whisper-windows-mcp includes a built-in privacy architecture for sensitive and regulated content.

Audio and video never leave your machine. This guarantee is unconditional.

Transcript text is different — when returned inline in a tool response, it is processed by Claude's API. For most users this is expected behavior. For regulated content (medical, legal, financial, corporate), privacy mode prevents this.

Privacy mode restricts all tool responses to metadata only (filename, word count, save path). No transcript text is transmitted to Claude's API under any circumstances. Enable per-call with `privacy_mode=true` on any transcription tool, or globally via `WHISPER_PRIVACY_MODE=true` in your config.

Consent gate — on first use per session in standard mode, a full privacy disclosure is shown before any transcript text is returned. You must explicitly confirm before proceeding. Set `WHISPER_CONSENT_ACKNOWLEDGED=true` in your config to skip this for non-sensitive content.

See PRIVACY.md for full compliance guidance (HIPAA, GDPR, attorney-client privilege, FERPA, SOX, PCI-DSS).

Designed for free-tier users

This tool is built to minimize Claude API interactions. The entire transcription workflow — scan, analyze, queue, run, validate — is designed to require as few Claude interactions as possible. Heavy lifting is done locally on your machine.

Optional environment variables

Security

Binary verification. To verify the integrity of the whisper-cli.exe binary in the pre-built release, check its SHA256 hash in PowerShell:

Get-FileHash "C:\whisper\Release\whisper-cli.exe" -Algorithm SHA256

The expected hash for the v1.4.0 release binary is documented in the releases page.

Input validation. All file and folder paths are validated before use, on every tool that takes one — UNC paths (`\\server\share`) and directory traversal sequences (`..`) are rejected. Files over 10 GB are rejected to prevent resource exhaustion. `job_id` and `batch_id` are checked against the exact server-minted format before they are used to build any file path, so a crafted ID cannot traverse out of the jobs directory.

Transcript injection awareness. Audio files can contain spoken content that, when transcribed, resembles instructions. Claude's built-in defenses handle this, but it is worth knowing that transcript content is treated as data — never as instructions — by the MCP server itself. Because transcribed content can still influence which tools Claude calls next, path/ID validation is applied defensively rather than trusting the single-user assumption alone.

Model downloads are restricted. The `download_model` tool only downloads from two trusted Hugging Face namespaces (`ggerganov/whisper.cpp` and `ggml-org`). Arbitrary URLs are rejected. Redirects are validated against an allowlist before following. (Downloads are not yet verified against a per-model SHA256 digest — see SECURITY.md.)

Model selection is sandboxed. Both `switch_model` and the `transcribe_audio` `model` override only accept `.bin` files within the configured models directory. Paths outside that directory are rejected via normalized path containment.

No PATH shadowing. System binaries the server invokes on your behalf (`tasklist`, `wmic`) are called by absolute `System32` path so they can't be shadowed by a same-named executable earlier on `PATH`.

See SECURITY.md for the full security policy.

Troubleshooting

See TROUBLESHOOTING.md for detailed solutions. See PRIVACY.md for compliance guidance if you handle regulated content.

Quick checklist:

• Paths in config use double backslashes (`C:\\whisper\\...`)
• `whisper-cli.exe` exists at the configured path
• Model `.bin` file exists at the configured path
• FFmpeg is installed and in PATH (`ffmpeg -version` works)
• Claude Desktop was fully restarted after editing config
• Whisper shows running in Settings → Developer

License

Non-commercial use: MIT — free for personal, educational, and non-commercial use. See LICENSE.

Commercial use: A separate commercial license is required for any business, professional, or revenue-generating use. See COMMERCIAL-LICENSE.md for terms and contact information.

Contributing

Pull requests welcome. See ROADMAP.md for planned features.

If you've tested GPU acceleration on hardware not listed above, please open an issue with your results — GPU model, VRAM, model size, and observed throughput.