cjm-transcription-plugin-voxtral-vllm 0.0.18

Mistral Voxtral plugin for the cjm-transcription-plugin-system library - provides local speech-to-text transcription through vLLM with configurable model selection and parameter control.

cjm-transcription-plugin-voxtral-vllm

Install

pip install cjm_transcription_plugin_voxtral_vllm

Project Structure

nbs/
├── meta.ipynb   # Metadata introspection for the Voxtral VLLM plugin used by cjm-ctl to generate the registration manifest.
└── plugin.ipynb # Plugin implementation for Mistral Voxtral transcription through vLLM server

Total: 2 notebooks

Module Dependencies

graph LR
    meta[meta<br/>Metadata]
    plugin[plugin<br/>Voxtral VLLM Plugin]

    plugin --> meta

1 cross-module dependencies detected

CLI Reference

No CLI commands found in this project.

Module Overview

Detailed documentation for each module in the project:

Metadata (meta.ipynb)

Metadata introspection for the Voxtral VLLM plugin used by cjm-ctl to generate the registration manifest.

Import

from cjm_transcription_plugin_voxtral_vllm.meta import (
    get_plugin_metadata
)

Functions

def get_plugin_metadata() -> Dict[str, Any]: # Plugin metadata for manifest generation
    """Return metadata required to register this plugin with the PluginManager."""
    # Fallback base path (current behavior for backward compatibility)
    base_path = os.path.dirname(os.path.dirname(sys.executable))
    
    # Use CJM config if available, else fallback to env-relative paths
    cjm_data_dir = os.environ.get("CJM_DATA_DIR")
    cjm_models_dir = os.environ.get("CJM_MODELS_DIR")
    
    # Plugin data directory
    plugin_name = "cjm-transcription-plugin-voxtral-vllm"
    if cjm_data_dir
    "Return metadata required to register this plugin with the PluginManager."

Voxtral VLLM Plugin (plugin.ipynb)

Plugin implementation for Mistral Voxtral transcription through vLLM server

Import

from cjm_transcription_plugin_voxtral_vllm.plugin import (
    VLLMServer,
    VoxtralVLLMPluginConfig,
    VoxtralVLLMPlugin
)

Functions

@patch
def supports_streaming(
    self: VoxtralVLLMPlugin # The plugin instance
) -> bool: # True if streaming is supported
    "Check if this plugin supports streaming transcription."

@patch
def execute_stream(
    self: VoxtralVLLMPlugin, # The plugin instance
    audio: Union[str, Path], # Audio data or path to audio file
    **kwargs # Additional plugin-specific parameters
) -> Generator[str, None, TranscriptionResult]: # Yields text chunks, returns final result
    "Stream transcription results chunk by chunk."

Classes

class VLLMServer:
    def __init__(
        self,
        model: str = "mistralai/Voxtral-Mini-3B-2507", # Model name to serve
        port: int = 8000, # Port for the server
        host: str = "0.0.0.0", # Host address to bind to
        gpu_memory_utilization: float = 0.85, # Fraction of GPU memory to use
        log_level: str = "INFO", # Logging level (DEBUG, INFO, WARNING, ERROR)
        capture_logs: bool = True, # Whether to capture and display server logs
        **kwargs # Additional vLLM server arguments
    )
    "vLLM server manager for Voxtral models."
    
    def __init__(
            self,
            model: str = "mistralai/Voxtral-Mini-3B-2507", # Model name to serve
            port: int = 8000, # Port for the server
            host: str = "0.0.0.0", # Host address to bind to
            gpu_memory_utilization: float = 0.85, # Fraction of GPU memory to use
            log_level: str = "INFO", # Logging level (DEBUG, INFO, WARNING, ERROR)
            capture_logs: bool = True, # Whether to capture and display server logs
            **kwargs # Additional vLLM server arguments
        )
    
    def add_log_callback(
            self, 
            callback: Callable[[str], None] # Function that receives log line strings
        ) -> None: # Returns nothing
        "Add a callback function to receive each log line."
    
    def start(
            self, 
            wait_for_ready: bool = True, # Wait for server to be ready before returning
            timeout: int = 120, # Maximum seconds to wait for server readiness
            show_progress: bool = True # Show progress indicators during startup
        ) -> None: # Returns nothing
        "Start the vLLM server."
    
    def stop(self) -> None: # Returns nothing
            """Stop the vLLM server."""
            if self.process and self.process.poll() is None
        "Stop the vLLM server."
    
    def restart(self) -> None: # Returns nothing
            """Restart the server."""
            self.stop()
            time.sleep(2)
            self.start()
        
        def is_running(self) -> bool: # True if server is running and responsive
        "Restart the server."
    
    def is_running(self) -> bool: # True if server is running and responsive
        "Check if server is running and responsive."
    
    def get_recent_logs(
            self, 
            n: int = 100 # Number of recent log lines to retrieve
        ) -> List[str]: # List of recent log lines
        "Get the most recent n log lines."
    
    def get_metrics_from_logs(self) -> dict: # Dictionary with performance metrics
            """Parse recent logs to extract performance metrics."""
            metrics = {
                "prompt_throughput": 0.0,
        "Parse recent logs to extract performance metrics."
    
    def tail_logs(
            self, 
            follow: bool = True, # Continue displaying new logs as they arrive
            n: int = 10 # Number of initial lines to display
        ) -> None: # Returns nothing
        "Tail the server logs (similar to tail -f)."

@dataclass
class VoxtralVLLMPluginConfig:
    "Configuration for Voxtral VLLM transcription plugin."
    
    model_id: str = field(...)
    device: str = field(...)
    server_mode: str = field(...)
    server_url: str = field(...)
    server_port: int = field(...)
    gpu_memory_utilization: float = field(...)
    max_model_len: int = field(...)
    language: Optional[str] = field(...)
    temperature: float = field(...)
    streaming: bool = field(...)
    server_startup_timeout: int = field(...)
    auto_start_server: bool = field(...)
    capture_server_logs: bool = field(...)
    dtype: str = field(...)
    tensor_parallel_size: int = field(...)

class VoxtralVLLMPlugin:
    def __init__(self):
        """Initialize the Voxtral VLLM plugin with default configuration."""
        self.logger = logging.getLogger(f"{__name__}.{type(self).__name__}")
        self.config: VoxtralVLLMPluginConfig = None
    "Mistral Voxtral transcription plugin via vLLM server."
    
    def __init__(self):
            """Initialize the Voxtral VLLM plugin with default configuration."""
            self.logger = logging.getLogger(f"{__name__}.{type(self).__name__}")
            self.config: VoxtralVLLMPluginConfig = None
        "Initialize the Voxtral VLLM plugin with default configuration."
    
    def name(self) -> str: # The plugin name identifier
            """Get the plugin name identifier."""
            return "voxtral_vllm"
        
        @property
        def version(self) -> str: # The plugin version string
        "Get the plugin name identifier."
    
    def version(self) -> str: # The plugin version string
            """Get the plugin version string."""
            return "1.0.0"
        
        @property
        def supported_formats(self) -> List[str]: # List of supported audio formats
        "Get the plugin version string."
    
    def supported_formats(self) -> List[str]: # List of supported audio formats
            """Get the list of supported audio file formats."""
            return ["wav", "mp3", "flac", "m4a", "ogg", "webm", "mp4", "avi", "mov"]
        
        def get_current_config(self) -> Dict[str, Any]: # Current configuration as dictionary
        "Get the list of supported audio file formats."
    
    def get_current_config(self) -> Dict[str, Any]: # Current configuration as dictionary
            """Return current configuration state."""
            if not self.config
        "Return current configuration state."
    
    def get_config_schema(self) -> Dict[str, Any]: # JSON Schema for configuration
            """Return JSON Schema for UI generation."""
            return dataclass_to_jsonschema(VoxtralVLLMPluginConfig)
    
        @staticmethod
        def get_config_dataclass() -> VoxtralVLLMPluginConfig: # Configuration dataclass
        "Return JSON Schema for UI generation."
    
    def get_config_dataclass() -> VoxtralVLLMPluginConfig: # Configuration dataclass
            """Return dataclass describing the plugin's configuration options."""
            return VoxtralVLLMPluginConfig
        
        def initialize(
            self,
            config: Optional[Any] = None # Configuration dataclass, dict, or None
        ) -> None
        "Return dataclass describing the plugin's configuration options."
    
    def initialize(
            self,
            config: Optional[Any] = None # Configuration dataclass, dict, or None
        ) -> None
        "Initialize or re-configure the plugin (idempotent)."
    
    def execute(
            self,
            audio: Union[str, Path], # Audio data or path to audio file to transcribe
            **kwargs # Additional arguments to override config
        ) -> TranscriptionResult: # Transcription result with text and metadata
        "Transcribe audio using Voxtral via vLLM."
    
    def is_available(self) -> bool: # True if vLLM and dependencies are available
            """Check if vLLM and required dependencies are available."""
            if not OPENAI_AVAILABLE
        "Check if vLLM and required dependencies are available."
    
    def cleanup(self) -> None:
            """Clean up resources."""
            self.logger.info("Cleaning up Voxtral VLLM plugin")
            
            # Stop managed server if running
            if self.config and self.config.server_mode == "managed" and self.server
        "Clean up resources."