wehub-resource-sync
153 lines
4.3 KiB
Markdown
153 lines
4.3 KiB
Markdown
# FunASR MCP Server
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[Model Context Protocol](https://modelcontextprotocol.io/) server that gives AI assistants the ability to transcribe audio.
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## Setup
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### 1. Install dependencies
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```bash
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pip install funasr
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```
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### 2. Optional: run with Docker
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The Dockerfile starts the MCP server over stdio and is suitable for MCP directory
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checks that initialize the server and call `tools/list`.
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```bash
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docker build -t funasr-mcp examples/mcp_server
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docker run --rm -i \
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-e FUNASR_DEVICE=cpu \
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-v /path/to/audio:/audio:ro \
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funasr-mcp
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```
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When submitting this server to MCP directories such as Glama, use this folder as
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the Docker build context so the container entrypoint runs `funasr_mcp.py`.
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The repository root `glama.json` declares GitHub maintainer ownership for Glama,
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while the `glama.json` file in this directory declares the container command and
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metadata for directory scanners.
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### Official MCP Registry checklist
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The Dockerfile includes the OCI ownership label expected by the official MCP
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Registry:
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```dockerfile
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LABEL io.modelcontextprotocol.server.name="io.github.modelscope/funasr-mcp"
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```
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Before publishing, push a public OCI image (for example to GHCR) and create a
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matching `server.json` whose `name` is `io.github.modelscope/funasr-mcp` and
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whose package identifier points at that image tag. The Registry verifies that
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the Docker/OCI label and `server.json` name match.
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### Glama submission checklist
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Use these values when adding the server at <https://glama.ai/mcp/servers>:
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| Field | Value |
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|------|-------|
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| Repository URL | <https://github.com/modelscope/FunASR> |
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| Docker build context | `examples/mcp_server` |
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| Dockerfile path | `examples/mcp_server/Dockerfile` |
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| Server command | `python /app/funasr_mcp.py` |
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| Expected MCP tool | `transcribe_audio` |
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After Glama finishes evaluation, verify that the score badge endpoint returns
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success before adding it to directory PRs:
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```markdown
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[](https://glama.ai/mcp/servers/modelscope/FunASR)
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```
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If the badge endpoint still returns 404, keep the badge out of external
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directory submissions until the Glama listing is live.
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### Directory listings
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The FunASR MCP server is listed on mcp.so:
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- <https://mcp.so/server/mcp-server-funasr/radial-hks>
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### 3. Configure your AI tool
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**Claude Code** (`~/.claude.json`):
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```json
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{
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"mcpServers": {
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"funasr": {
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"command": "python",
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"args": ["/path/to/examples/mcp_server/funasr_mcp.py"],
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"env": {"FUNASR_DEVICE": "cuda"}
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}
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}
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}
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```
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**Claude Desktop** (`claude_desktop_config.json`):
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```json
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{
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"mcpServers": {
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"funasr": {
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"command": "python",
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"args": ["/path/to/funasr_mcp.py"],
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"env": {"FUNASR_DEVICE": "cpu"}
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}
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}
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}
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```
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**Cursor** (Settings → MCP Servers → Add):
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- Command: `python /path/to/funasr_mcp.py`
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- Environment: `FUNASR_DEVICE=cuda`
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## Tools
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### `transcribe_audio`
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Transcribe a speech audio file to text.
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**Parameters:**
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| Name | Type | Required | Description |
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|------|------|----------|-------------|
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| `audio_path` | string | Yes | Path to audio file (wav, mp3, flac, m4a, ogg) |
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| `language` | string | No | Language hint (auto-detected by default) |
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**Returns:** Transcribed text with timestamps and speaker labels (when available).
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## Example Usage
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Once configured, ask your AI assistant:
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- "Transcribe the meeting recording at ~/Downloads/meeting.wav"
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- "What was said in this audio file? /path/to/interview.mp3"
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- "Convert this voice memo to text: ~/voice_note.m4a"
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## Environment Variables
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| Variable | Default | Description |
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|----------|---------|-------------|
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| `FUNASR_DEVICE` | `cpu` | Device: `cuda`, `cpu`, or `mps` |
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| `FUNASR_MODEL` | `iic/SenseVoiceSmall` | ASR model to use |
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## Features
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- **50+ languages** with automatic detection
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- **Speaker diarization** — identifies who said what
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- **Timestamps** — per-segment timing
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- **170x realtime on GPU**, 17x on CPU
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- **No API key needed** — fully local inference
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- MIT licensed, privacy-friendly (audio never leaves your machine)
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## Verified Compatibility
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| Tool | Status |
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|------|--------|
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| Claude Code | ✅ Tested |
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| Claude Desktop | ✅ Compatible |
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| Cursor | ✅ Compatible |
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| Windsurf | ✅ Compatible |
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| Any MCP client | ✅ Standard protocol |
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