Contributing to Inflect
July 24, 2026 ยท View on GitHub
Contributions are welcome. For a speech model, the bar is reproducible evidence rather than a single favorable clip.
Good Contributions
Useful contributions usually fall into one of these categories:
- reproducible benchmark improvements
- clearer docs
- safer data preparation
- better evaluation prompts
- runtime stability fixes
- small, well-scoped runtime experiments
- bug reports with audio examples and exact commands
Before Opening a PR
Please make sure:
- the change is small enough to review
- generated artifacts are not committed
- private reference voices are not committed
- checkpoints are not committed
- local absolute paths are not added to public docs
- the README does not claim unreleased model quality
Runtime and evaluation reports
For model or inference changes, include:
- base variant
- changed variant
- exact command
- prompt set
- model package and commit tested
- checkpoint used
- listening notes
- objective metrics if available
Minimum listening notes:
- voice consistency
- pacing
- skipped words
- glitches
- long-prompt behavior
Commit Scope
Keep PRs focused.
Good:
- "Add ASR pseudo-label filter"
- "Improve README and media kit"
- "Add duration-ratio check to benchmark"
Bad:
- one PR containing docs, checkpoints, generated audio, unrelated training changes, and local state files
Local artifacts
Do not commit:
outputs/.blind_ab_state*/reference_voices/- local third-party checkouts
- checkpoints
- full generated datasets
- private audio
Public release boundary
The release is open-weight. A contribution may improve public inference, evaluation, examples, documentation, or integration without requiring the private corpus-construction pipeline. Do not open issues requesting private reference material, generated corpora, credentials, or undisclosed training infrastructure.
Project tone
Inflect should be ambitious without exaggerating. Distinguish measured results from estimates, human preference from predicted quality, and complete text-to-waveform parameters from training-only modules.