All Voice Lab MCP Server

import logging from mcp.types import TextContent from client.all_voice_lab import VoiceCloneNoPermissionError from .base import get_client from .utils import validate_audio_file, validate_output_directory, create_error_response, create_success_response def text_to_speech( text: str, voice_id: str, model_id: str, speed: int = 1, output_dir: str = None ) -> TextContent: """ Convert text to speech Args: text: Target text for speech synthesis. Maximum 5,000 characters. voice_id: Voice ID to use for synthesis. Required. Must be a valid voice ID from the available voices (use get_voices tool to retrieve). model_id: Model ID to use for synthesis. Required. Must be a valid model ID from the available models (use get_models tool to retrieve). speed: Speech rate adjustment, range [0.5, 1.5], where 0.5 is slowest and 1.5 is fastest. Default value is 1. output_dir: Output directory for the generated audio file. Default is user's desktop. Returns: TextContent: Contains the file path to the generated audio file. """ all_voice_lab = get_client() output_dir = all_voice_lab.get_output_path(output_dir) logging.info(f"Tool called: text_to_speech, voice_id: {voice_id}, model_id: {model_id}, speed: {speed}") logging.info(f"Output directory: {output_dir}") # Validate parameters if not text: logging.warning("Text parameter is empty") return TextContent( type="text", text="text parameter cannot be empty" ) if len(text) > 5000: logging.warning(f"Text parameter exceeds maximum length: {len(text)} characters") return TextContent( type="text", text="text parameter cannot exceed 5,000 characters" ) if not voice_id: logging.warning("voice_id parameter is empty") return TextContent( type="text", text="voice_id parameter cannot be empty" ) # Validate voice_id is numeric if not voice_id.isdigit(): logging.warning(f"Invalid voice_id format: {voice_id}, not a numeric value") return TextContent( type="text", text="voice_id parameter must be a numeric value" ) if not model_id: logging.warning("model_id parameter is empty") return TextContent( type="text", text="model_id parameter cannot be empty" ) # Validate model_id against available models try: logging.info(f"Validating model_id: {model_id}") model_resp = all_voice_lab.get_supported_voice_model() available_models = model_resp.models valid_model_ids = [model.model_id for model in available_models] if model_id not in valid_model_ids: logging.warning(f"Invalid model_id: {model_id}, available models: {valid_model_ids}") return TextContent( type="text", text=f"Invalid model_id: {model_id}. Please use a valid model ID." ) logging.info(f"Model ID validation successful: {model_id}") except Exception as e: logging.error(f"Failed to validate model_id: {str(e)}") # Continue with the process even if validation fails # to maintain backward compatibility try: logging.info(f"Starting text-to-speech processing, text length: {len(text)} characters") file_path = all_voice_lab.text_to_speech(text, voice_id, model_id, output_dir, speed) logging.info(f"Text-to-speech successful, file saved at: {file_path}") return TextContent( type="text", text=f"Speech generation completed, file saved at: {file_path}\n" ) except Exception as e: logging.error(f"Text-to-speech failed: {str(e)}") return TextContent( type="text", text=f"Synthesis failed, tool temporarily unavailable" ) def speech_to_speech( audio_file_path: str, voice_id: str, similarity: float = 1, remove_background_noise: bool = False, output_dir: str = None ) -> TextContent: """ Convert audio to another voice while preserving speech content Args: audio_file_path: Path to the source audio file. Only MP3 and WAV formats are supported. Maximum file size: 50MB. voice_id: Voice ID to use for the conversion. Required. Must be a valid voice ID from the available voices (use get_voices tool to retrieve). similarity: Voice similarity factor, range [0, 1], where 0 is least similar and 1 is most similar to the original voice characteristics. Default value is 1. remove_background_noise: Whether to remove background noise from the source audio before conversion. Default is False. output_dir: Output directory for the generated audio file. Default is user's desktop. Returns: TextContent: Contains the file path to the generated audio file with the new voice. """ all_voice_lab = get_client() output_dir = all_voice_lab.get_output_path(output_dir) logging.info(f"Tool called: speech_to_speech, voice_id: {voice_id}, similarity: {similarity}") logging.info(f"Audio file path: {audio_file_path}, remove background noise: {remove_background_noise}") logging.info(f"Output directory: {output_dir}") # Validate audio file is_valid, error_message = validate_audio_file(audio_file_path) if not is_valid: return create_error_response(error_message) # Validate voice_id parameter if not voice_id: logging.warning("voice_id parameter is empty") return create_error_response("voice_id parameter cannot be empty") # Validate voice_id format (basic check) if not isinstance(voice_id, str) or len(voice_id.strip()) == 0: logging.warning(f"Invalid voice_id format: {voice_id}") return create_error_response("Invalid voice_id format") # Validate similarity range if similarity < 0 or similarity > 1: logging.warning(f"Similarity parameter {similarity} is out of range [0, 1]") return create_error_response("similarity parameter must be between 0 and 1") # Validate and create output directory is_valid, error_message = validate_output_directory(output_dir) if not is_valid: return create_error_response(error_message) try: logging.info("Starting speech conversion processing") file_path = all_voice_lab.speech_to_speech(audio_file_path, voice_id, output_dir, similarity, remove_background_noise) logging.info(f"Speech conversion successful, file saved at: {file_path}") return create_success_response(f"Audio conversion completed, file saved at: {file_path}\n") except FileNotFoundError as e: logging.error(f"Audio file does not exist: {audio_file_path}, error: {str(e)}") return create_error_response(f"Audio file does not exist: {audio_file_path}") except Exception as e: logging.error(f"Speech conversion failed: {str(e)}") return create_error_response("Conversion failed, tool temporarily unavailable") def isolate_human_voice( audio_file_path: str, output_dir: str = None ) -> TextContent: """ Extract clean human voice by removing background noise and non-speech sounds Args: audio_file_path: Path to the audio file to process. Only MP3 and WAV formats are supported. Maximum file size: 50MB. output_dir: Output directory for the processed audio file. Default is user's desktop. Returns: TextContent: Contains the file path to the generated audio file with isolated human voice. """ all_voice_lab = get_client() output_dir = all_voice_lab.get_output_path(output_dir) logging.info(f"Tool called: isolate_human_voice") logging.info(f"Audio file path: {audio_file_path}") logging.info(f"Output directory: {output_dir}") # Validate audio file is_valid, error_message = validate_audio_file(audio_file_path) if not is_valid: return create_error_response(error_message) # Validate and create output directory is_valid, error_message = validate_output_directory(output_dir) if not is_valid: return create_error_response(error_message) try: logging.info("Starting human voice isolation processing") file_path = all_voice_lab.audio_isolation(audio_file_path, output_dir) logging.info(f"Human voice isolation successful, file saved at: {file_path}") return create_success_response(f"Voice isolation completed, file saved at: {file_path}\n") except FileNotFoundError as e: logging.error(f"Audio file does not exist: {audio_file_path}, error: {str(e)}") return create_error_response(f"Audio file does not exist: {audio_file_path}") except Exception as e: logging.error(f"Human voice isolation failed: {str(e)}") return create_error_response("Voice isolation failed, tool temporarily unavailable") def clone_voice( audio_file_path: str, name: str, description: str = None ) -> TextContent: """ Create a custom voice profile by cloning from an audio sample Args: audio_file_path: Path to the audio file containing the voice sample to clone. Only MP3 and WAV formats are supported. Maximum file size: 10MB. name: Name to assign to the cloned voice profile. Required. description: Optional description for the cloned voice profile. Returns: TextContent: Contains the voice ID of the newly created voice profile. """ all_voice_lab = get_client() logging.info(f"Tool called: clone_voice") logging.info(f"Audio file path: {audio_file_path}") logging.info(f"Voice name: {name}") if description: logging.info(f"Voice description: {description}") # Validate audio file, using 10MB size limit is_valid, error_message = validate_audio_file(audio_file_path, max_size_mb=10) if not is_valid: return create_error_response(error_message) # Validate name parameter if not name: logging.warning("Name parameter is empty") return create_error_response("name parameter cannot be empty") try: logging.info("Starting voice cloning process") voice_id = all_voice_lab.add_voice(name, audio_file_path, description) logging.info(f"Voice cloning successful, voice ID: {voice_id}") return TextContent( type="text", text=f"Voice cloning completed. Your new voice ID is: {voice_id}\n" ) except VoiceCloneNoPermissionError as e: logging.error(f"Voice cloning failed: {str(e)}") return TextContent( type="text", text=f"Voice cloning failed, you don't have permission to clone voice. Please contact AllVoiceLab com." ) except FileNotFoundError as e: logging.error(f"Audio file does not exist: {audio_file_path}, error: {str(e)}") return TextContent( type="text", text=f"Audio file does not exist: {audio_file_path}" ) except Exception as e: logging.error(f"Voice cloning failed: {str(e)}") return TextContent( type="text", text=f"Voice cloning failed, tool temporarily unavailable" )