Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@whisper-mcp Transcribe samples/meeting.mp4 and generate an SRT file."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Local Whisper transcription, exposed as
an MCP server — so any MCP client (Claude
Desktop, Claude Code, etc.) can transcribe audio/video files, generate .srt
subtitles, and burn captions into a video, directly as tool calls. No cloud
API, no manual "run a script and go read the output file" step.
Built on faster-whisper for
inference and ffmpeg for audio extraction / caption burn-in.
MCP client (Claude, etc.) --stdio--> whisper-mcp server --> faster-whisper (Whisper model)
\--> ffmpeg (audio extract / burn-in)The server keeps loaded Whisper models cached in memory for the life of the process, so repeated tool calls in one session don't re-pay model load time.
Related MCP server: m4x
Tool | Description |
| Returns detected language + timestamped segments as structured data. |
| Transcribes and writes a |
| Burns an |
Python 3.10+
ffmpeg on PATH
Optional: a CUDA-capable GPU (falls back to CPU automatically)
pip install -e ".[dev]"
whisper-mcpOr, zero-install:
uvx --from git+https://github.com/Chain-P/whisper-mcp whisper-mcpAdd to your client's MCP config (e.g. Claude Code's .mcp.json or Claude
Desktop's claude_desktop_config.json):
{
"mcpServers": {
"whisper": {
"command": "whisper-mcp"
}
}
}Then ask the client to transcribe a file, e.g. "transcribe
samples/podcast_clip.mp4 and give me the SRT."
pip install -e ".[dev]"
ruff check .
pytest # unit tests only
pytest -m integration # + real transcription against a sample fileSpeaker diarization (pyannote.audio / whisperx) for multi-speaker
labeling in the SRT output.
MCP progress notifications for long transcriptions.
A resource exposing recent transcript history.
MIT
Burn an existing .srt subtitle file into a video, producing a new video file.
Args: video_path: Path to the source video. srt_path: Path to the .srt subtitle file to burn in. output_path: Where to write the captioned video. Defaults to ".captioned".
Inputs: srt_path (required), video_path (required), output_path. Returns: result
Transcribe a file and write an .srt subtitle file next to it (or at output_path).
Args: path: Path to the input audio/video file. output_path: Where to write the .srt file. Defaults to the input path with a .srt suffix. model_size: Whisper model size (tiny/base/small/medium/large-v3). device: Inference device: "auto", "cpu", or "cuda".
Inputs: path (required), device, model_size, output_path.
Transcribe an audio or video file and return language info + timestamped segments.
Args: path: Path to the input audio/video file. model_size: Whisper model size (tiny/base/small/medium/large-v3). device: Inference device: "auto", "cpu", or "cuda".
Inputs: path (required), device, model_size.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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Scored across 3 tools
transcribe and generate_srt both involve transcription, but their output types are clearly different: one returns structured segments while the other writes an SRT file. burn_captions is completely distinct, focusing on video rendering rather than audio processing.
generate_srt and burn_captions follow a clear verb_noun pattern. transcribe is a bare verb, which is a minor deviation, but it is still short, predictable, and fits the domain.
Three tools is well-scoped for a whisper-focused server. Each tool covers a meaningful step in the transcription/subtitling workflow without unnecessary redundancy.
The core workflow of transcribing audio, generating subtitles, and burning them into video is fully covered. Minor gaps like explicit plain-text transcript export or translation are absent, but they can be worked around from the timestamped segments.
OCR, transcription, file extraction, and image generation for AI agents via MCP.
Transform video, audio and images, and generate media from prompts. FFmpeg, captions, models.
MCP server for Clipkit — gives AI agents a video toolbox via the Clipkit schema.
Transcribe, subtitle, dub and lip-sync media; create speech, voices, images and thumbnails.
