Generate speech locally
Piper turns text into speech on your own device, so it can work well for offline narration, assistants, and accessibility tools.
Browse Piper models for generating speech locally from text. Each listing links to the source where the model and its supporting files were published.
Browse Piper voice models →Showing 2 of 2 · Page 1 of 1
Standalone artifact
by bibebobberson
Standalone artifact
by LocomotivePlayz
Continue by voice, framework, language, franchise, category, or creator when that context is represented in these models.
Start with what you want to make: live voice changes, recorded speech, a song cover, or another vocal project. Then check the model's language, version, files, and app requirements.
Different versions of the same framework may need different tools or settings.
Do not assume a model's language from the character name alone.
Look for the required weights, configs, checkpoints, setup notes, demos, and app requirements.
No. Each listing links to the external page where the model was published.
Different creators, datasets, framework versions, and training runs remain separate models.
Piper is a lightweight text-to-speech system designed to run locally. Its voices generate speech from text rather than converting an existing performance, making them useful for narration, assistants, accessibility, and offline applications.
Check the original source for the voice model, matching configuration file, supported language, sample rate, license, and setup instructions.
Start with the language and voice you need, then check the model quality, sample rate, required configuration, and license.
Piper turns text into speech on your own device, so it can work well for offline narration, assistants, and accessibility tools.
A Piper voice normally needs its model file and matching JSON configuration. Download the complete voice package described by the source.
Piper voices are trained for specific languages and may be released at different quality levels. Check the listing and original model card before choosing one.