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.