Mimic Recording Studio
Mimic Recording Studio is a project designed to facilitate the creation of customized voices using the Mimic 2 Text-to-Speech (TTS) engine. Developed by Mycroft, this technology converts written text into spoken audio by training on specific voice data, allowing users to create a unique voice model that replicates the speaker's tone and mannerisms. The studio enables individuals to easily record phrases that can then be used to train these personalized voice models.
Software Quick Start
Getting started with Mimic Recording Studio is straightforward, with instructions for both Windows and Linux/Mac users. The setup process includes cloning the GitHub repository and following platform-specific steps to run the software. For Windows, a simple batch file initiates the setup, while Linux/Mac users are encouraged to use Docker for a seamless cross-platform experience. Docker ensures that necessary dependencies are easily managed, requiring minimal manual configuration.
Manual Installation
For those preferring a manual installation, the studio is divided into two main components: the backend and the frontend. The backend handles processing and data management using Python and Flask, while the frontend, built with React, is responsible for the user interface and audio visualization. Both components have their own dependencies and build processes, which are clearly outlined for developers ready to dive into the codebase.
Data Management
Mimic Recording Studio organizes recordings in WAV format stored in a structured directory. Each user’s recordings are associated with a uuid, ensuring data accuracy and management consistency. Metadata files accompany audio outputs, mapping each file to its corresponding spoken phrase, crucial for effective training with Mimic 2.
The studio leverages a corpus to guide recordings. Initially, an English-language corpus is provided, but users have the flexibility to introduce new corpora, including those in other languages. This feature supports customized training by allowing the selection of commonly used phrases and diverse phonetic elements.
Technologies Utilized
The frontend and backend components use modern web technologies. The frontend relies on JavaScript and the React framework to offer dynamic user interfaces and audio tools. Its key functions include recording audio, visualizing sound waves, and displaying metrics.
On the backend, the studio uses Python with Flask to manage recordings and serve data. It interacts with a SQLite database to store session data, ensuring reliable audio processing and easy retrieval for future use. Docker containers are used to streamline deployment, with configurable ports for both components.
Tips for Recording
Achieving a high-quality voice model requires numerous recordings, up to 20,000 phrases. The studio suggests recording in quiet, sound-dampened environments for the best results. Consistent speaking pace and volume, along with a quality microphone, are also recommended. Users are advised to limit recording sessions to avoid vocal strain and to regularly back up their data.
Advanced Features and Contributions
For those interested in further exploration, the project allows database queries to analyze recording metrics. Users can adjust settings, such as changing recorder uuid, to maintain session consistency. The community is encouraged to contribute improvements via pull requests, fostering collaborative growth of the project.
Users can donate their voice recordings to Mycroft under the Creative Commons license for broader use in TTS technologies, highlighting the open-source nature and community-driven focus of Mimic Recording Studio.
Support and Community
For any assistance or to share experiences, users can access the Mycroft community forums and chat for support. These platforms provide a space for collaboration, troubleshooting, and expanding the impact of Mimic Recording Studio.