Converts text and Markdown files into natural-sounding speech using a fast, local neural TTS system.
Piper Text-to-Speech enables the generation of high-quality audio from text files and Markdown documents using a privacy-respecting, local neural synthesis engine. It includes a specialized preprocessing script for Obsidian files that strips out frontmatter and formatting, ensuring clear narration of technical content. Users can fine-tune the audio output through speed, volume, and pause duration controls, and even follow guided workflows to incorporate image descriptions into the audio stream. This skill is perfect for turning documentation into listenable content or building accessible workflows without relying on external cloud APIs.
Key Features
01Granular speech control including speed multipliers, volume, and sentence pauses
02Specialized cleaning script for Obsidian markdown to remove frontmatter and formatting
03Batch processing capabilities for converting multiple files simultaneously
04Integrated workflow for transcribing image content into the audio narration
05Local neural text-to-speech synthesis using high-quality ONNX voice models
060 GitHub stars
Use Cases
01Transforming technical documentation and personal notes into audio narrations for offline listening
02Creating accessible content by narrating visual elements and diagrams through manual transcription integration
03Automating text-to-audio conversion for large datasets of text files or articles
