STATUSPersonal research and development scripts; historical training context retained.
FOCUSSpeech synthesis
DIAGRAM SCOPEWorkflow overview
THE PROBLEM
What it sets out to solve.
Custom speech training requires consistent audio/transcript pairs and a traceable path from preparation to listening evaluation.
THE APPROACH
How it comes together.
Python scripts inspect WAV metadata, filter transcript/audio issues, create train/validation metadata, configure VITS training through Coqui and PyTorch, and generate audio from selected checkpoint directories. PowerShell helpers support local launch and monitoring.
UNDER THE HOOD
Follow the workflow.
Trace the components. Select a node for its role in the system.
Dataset preparation to checkpoint-based speech synthesis7 COMPONENTS
INTERACTIVE 3DTTS Humanize
PythonCoqui TTSVITS
TTS Humanize / workflow
Move to tilt · Drag to rotate · Arrow keysDrag to rotate · Tap a component7 components · 6 connections
2D diagram
Trace connections 6 relationships
WAV files and transcripts→Inspect and clean
Inspect and clean→Prepare dataset splits
Prepare dataset splits→VITS training
VITS training→Run configuration and checkpoints
Run configuration and checkpoints→Checkpoint inference
Run configuration and checkpoints→Listening samples
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Select a component to see how it fits into the system.
CAPABILITIES
What’s inside.
01
Audio duration, RMS, metadata, and missing-file diagnostics
02
Transcript-pattern and audio-quality filtering with cleaning reports
03
Train/validation metadata preparation
04
Configurable VITS training, checkpoint restore/continue options, and CUDA selection
05
Checkpoint-sample generation and WAV inference output