What Whisper models can I use?
The server supports tiny.en, base.en, small.en, medium.en, and large. Models with a `.en` suffix are optimized for English, offering faster and more accurate transcription.
@DefiBax
Basic information
No overview available yet
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"mcp_servers": {
"command": "npx",
"args": [
"@modelcontextprotocol/inspector",
"voice-recorder-mcp"
]
}
}
}Start recording audio from the default microphone
Stop recording and transcribe the audio to text
Record audio for a specified duration and transcribe it
The Voice Recorder MCP Server records audio from a microphone and transcribes it using OpenAI's Whisper model. It works as a custom extension for the Goose AI agent or as a standalone MCP server.
Install from source with git clone and pip install -e ., then run voice-recorder-mcp with optional --model and --sample-rate arguments. Configure via environment variables like WHISPER_MODEL, SAMPLE_RATE, and MAX_DURATION. Use the tools start_recording, stop_and_transcribe, and record_and_transcribe to manage recordings.
The server supports tiny.en, base.en, small.en, medium.en, and large. Models with a `.en` suffix are optimized for English, offering faster and more accurate transcription.
In Goose, go to Settings > Extensions > Add > Command Line Extension, set the name to `voice-recorder`, and provide the full path to the `voice-recorder-mcp` executable.
Python 3.12 or later and an audio input device (microphone).
Set the sample rate via the `--sample-rate` command-line argument or the `SAMPLE_RATE` environment variable (default 16000). Use higher sample rates for better audio quality, though file sizes increase.
Check your microphone permissions and settings. Also ensure the server has access to the audio device. If model download fails, verify your internet connection.
YC (S26) | Open Computer History | Continuously record your company computer work, map your workflows, help you find work worth automating, and power your agents' context

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