Whissle MCP Server

Access Whissle API for speech-to-text, diarization, translation, and text summarization.

A Python-based server that provides access to Whissle API endpoints for speech-to-text, diarization, translation, and text summarization.

- This server provides access to Whissle API endpoints which may incur costs
- Each tool that makes an API call is marked with a cost warning
- Please follow these guidelines:
- Only use tools when explicitly requested by the user
- For tools that process audio, consider the length of the audio as it affects costs
- Some operations like translation or summarization may have higher costs
- Tools without cost warnings in their description are free to use as they only read existing data

- Python 3.8 or higher
- pip (Python package installer)
- A Whissle API authentication token

git clone <repository-url> cd whissle_mcp

Create and activate a virtual environment:

python -m venv venv source venv/bin/activate # On Windows, use: venv\Scripts\activate

Set up environment variables: Create a.envfile in the project root with the following content:

WHISSLE_AUTH_TOKEN=insert_auth_token_here # Replace with your actual Whissle API token WHISSLE_MCP_BASE_PATH=/path/to/your/base/directory

⚠️Important: Never commit your actual token to the repository. The.envfile is included in.gitignoreto prevent accidental commits.

Configure Claude Integration: Copyclaude_config.example.jsontoclaude_config.jsonand update the paths:

{ "mcpServers": { "Whissle": { "command": "/path/to/your/venv/bin/python", "args": [ "/path/to/whissle_mcp/server.py" ], "env": { "WHISSLE_AUTH_TOKEN": "insert_auth_token_here" } } } }

- Replace/path/to/your/venv/bin/pythonwith the actual path to your Python interpreter in the virtual environment
- Replace/path/to/whissle_mcp/server.pywith the actual path to your server.py file

- WHISSLE_AUTH_TOKEN: Your Whissle API authentication token (required)

- This is a sensitive credential that should never be shared or committed to version control
- Contact your administrator to obtain a valid token
- Store it securely in your local.envfile

The server supports the following audio formats:

- WAV (.wav)
- MP3 (.mp3)
- OGG (.ogg)
- FLAC (.flac)
- M4A (.m4a)

- Maximum file size: 25 MB
- Files larger than this limit will be rejected

Convert speech to text using the Whissle API.

response = speech_to_text( audio_file_path="path/to/audio.wav", model_name="en-NER", # Default model timestamps=True, # Include word timestamps boosted_lm_words=["specific", "terms"], # Words to boost in recognition boosted_lm_score=80 # Score for boosted words (0-100) )

Convert speech to text with speaker identification.

response = diarize_speech( audio_file_path="path/to/audio.wav", model_name="en-NER", # Default model max_speakers=2, # Maximum number of speakers to identify boosted_lm_words=["specific", "terms"], boosted_lm_score=80 )

Translate text from one language to another.

response = translate_text( text="Hello, world!", source_language="en", target_language="es" )

response = summarize_text( content="Long text to summarize...", model_name="openai", # Default model instruction="Provide a brief summary" # Optional )

List all available ASR models and their capabilities.

{ "transcript": "The transcribed text", "duration_seconds": 10.5, "language_code": "en", "timestamps": [ { "word": "The", "startTime": 0, "endTime": 100, "confidence": 0.95 } ], "diarize_output": [ { "text": "The transcribed text", "speaker_id": 1, "start_timestamp": 0, "end_timestamp": 10.5 } ] }

{ "type": "text", "text": "Translation:\nTranslated text here" }

{ "type": "text", "text": "Summary:\nSummarized text here" }

{ "error": "Error message here" }

The server includes robust error handling with:

- Automatic retries for HTTP 500 errors
- Detailed error messages for different failure scenarios
- File validation (existence, size, format)
- Authentication checks

- HTTP 500: Server error (with retry mechanism)
- HTTP 413: File too large
- HTTP 415: Unsupported file format
- HTTP 401/403: Authentication error

The server will be available at the default MCP port (usually 8000)

A test script is provided to verify the functionality of all tools:
- Check for authentication token
- Test all available tools
- Provide detailed output of each operation
- Handle errors gracefully
- Check the error messages for specific details
- Verify your authentication token
- Ensure your audio files meet the requirements
- Contact Whissle support for API-related issues

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