Whissle MCP Server
A Python-based server that provides access to Whissle API endpoints for speech-to-text, diarization, translation, and text summarization.
Important Notes
- 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
Prerequisites
- Python 3.8 or higher
- pip (Python package installer)
- A Whissle API authentication token
Installation
Clone the repository:
git clone <repository-url> cd whissle_mcp
Create and activate a virtual environment:
python -m venv venv source venv/bin/activate # On Windows, use: venvScriptsactivate
Install the required packages:
pip install -e .
Set up environment variables: Create a
.env
file 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
.env
file is included in.gitignore
to prevent accidental commits.Configure Claude Integration: Copy
claude_config.example.json
toclaude_config.json
and 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/python
with the actual path to your Python interpreter in the virtual environment - Replace
/path/to/whissle_mcp/server.py
with the actual path to your server.py file
- Replace
Configuration
Environment Variables
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
.env
file
WHISSLE_MCP_BASE_PATH
: Base directory for file operations (optional, defaults to user’s Desktop)
Supported Audio Formats
The server supports the following audio formats:
- WAV (.wav)
- MP3 (.mp3)
- OGG (.ogg)
- FLAC (.flac)
- M4A (.m4a)
File Size Limits
- Maximum file size: 25 MB
- Files larger than this limit will be rejected
Available Tools
1. Speech to Text
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)
)
2. Speech Diarization
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
)
3. Text Translation
Translate text from one language to another.
response = translate_text(
text="Hello, world!",
source_language="en",
target_language="es"
)
4. Text Summarization
Summarize text using an LLM model.
response = summarize_text(
content="Long text to summarize...",
model_name="openai", # Default model
instruction="Provide a brief summary" # Optional
)
5. List ASR Models
List all available ASR models and their capabilities.
response = list_asr_models()
Response Format
Speech to Text and Diarization
{
"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
}
]
}
Translation
{
"type": "text",
"text": "Translation:nTranslated text here"
}
Summarization
{
"type": "text",
"text": "Summary:nSummarized text here"
}
Error Response
{
"error": "Error message here"
}
Error Handling
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
Common error types:
- HTTP 500: Server error (with retry mechanism)
- HTTP 413: File too large
- HTTP 415: Unsupported file format
- HTTP 401/403: Authentication error
Running the Server
Start the server:
mcp serve
The server will be available at the default MCP port (usually 8000)
Testing
A test script is provided to verify the functionality of all tools:
python test_whissle.py
The test script will:
- Check for authentication token
- Test all available tools
- Provide detailed output of each operation
- Handle errors gracefully
Support
For issues or questions, please:
- 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
License
[Add your license information here]
Whissle MCP Server
Project Details
- vmehta14/whissle-mcp
- Last Updated: 4/12/2025
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