Voice Generator Workspace

A documentation-first workspace for speech synthesis, divided into two tracks. The runtime track turns input text into generated audio. The training track covers dataset preparation, training and generation of a custom voice model that the runtime can then use.

The workspace keeps the two tracks apart on purpose, because training dependencies, datasets and generated artifacts can be large and environment-specific.

Category :Speech Synthesis / Machine Learning
Stack :Python, Piper-style training assets
Runtime :board-computer-tts
Training :human-like-voice-ml
Outputs :Generated voice and audio artifacts
Status :Documentation-first workspace

Voice lens with a text to audio card and a training loss chart

Core Features

The goal is a voice that sounds natural and can be produced from plain text on a local machine.

Runtime text-to-speech from input text.
A training track for a human-like voice model.
Dataset preparation and fine-tuning.
Custom models that feed back into the runtime.
Separated runtime and training scripts.
README-driven planning where code is incomplete.

“Do not mix runtime scripts and training scripts.”

Design Note - Separation of Concerns

Architecture

Input text goes to the runtime track, which produces audio with ready models. Separately, a voice dataset goes through the training track and produces a custom model, which the runtime can use. Both tracks share a models folder for local artifacts. The workspace is documentation-first: the READMEs are the planning source while the code is being completed.