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Piper for local speech synthesis on small devices
Run neural text-to-speech locally with downloadable voice models.
Key Takeaways
- Piper uses voice artifacts and associated configuration to synthesize speech from text.
- Pair the voice model file with its configuration and use a compatible local runtime.
- Synthesize three short messages on the intended hardware.
- Voice models have their own terms, which can differ from the engine's.
How it works
Piper uses voice artifacts and associated configuration to synthesize speech from text. It is useful for apps that need predictable on-device execution and short spoken responses.
What you need
Pair the voice model file with its configuration and use a compatible local runtime. For embedded use, test whether the speaker can start playback while subsequent audio is generated.
What to try first
Synthesize three short messages on the intended hardware. Measure time before playback and test punctuation and number normalization.
Run the voice through the actual speaker or playback chain. Low-powered devices may render quickly but have audible pauses when synthesis and playback share resources.
Where to look for failures
Voice models have their own terms, which can differ from the engine's. Check distribution obligations and test unusual names before shipping a voice.
Related Franklin pages
Compare on-device synthesis with several short phrases that contain names and numbers. Log initialization time separately from steady-state speech generation.
- Embed supported speech models in applications through ONNX-based runtimes. sherpa-onnx for local speech applications
- Generate speech locally using published Kokoro voices and inference tooling. Kokoro for compact text-to-speech pipelines
For an application where the quality of recording and handling audio matters, see Franklin’s Open Mic AI profile. Open Mic AI
Project documentation
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Explore the project’s documentation for setup and usage, or save this page to a collection for later.
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