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piper-onnx-durations
rinaldow/piper-onnx-durations
piper-onnx-durations is a machine learning model from rinaldow. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Modified Piper TTS models with exposed duration tensors for temporal alignment.
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Public
Updated Mar 27, 2026
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At a glance
- Access
- Public
- Created
- Mar 22, 2026
- Updated
- Mar 27, 2026
- SHA
- 4f184364
Model card
From the Hugging Face model README
Piper ONNX Timing Models
Modified Piper TTS models with exposed duration tensors for temporal alignment.
Model Outputs
| Output | Type | Description |
|---|---|---|
audio | Float32Array | Synthesized speech |
durations | Float32Array | Frame counts per token |
Duration Format
Durations are expressed in audio frames. To convert to milliseconds:
duration_ms = duration_frames × (hop_size / sample_rate) × 1000
Standard values:
hop_size: 256sample_rate: 22050 (medium models)
Token Structure
The durations array corresponds to a token sequence that includes structural markers:
| ID | Type | Description |
|---|---|---|
| 0 | padding | Inter-token transitions |
| 1 | BOS | Beginning of sentence |
| 2 | EOS | End of sentence |
| 3 | space | Word boundary |
| 4+ | phoneme | Speech sounds |
The token sequence is typically longer than the input phoneme string due to inserted structural tokens.
Configuration
Each model includes a .onnx.json file containing:
phoneme_id_map: Symbol-to-ID mappingsaudio.sample_rate: Sample rate for duration conversion
Source
Based on rhasspy/piper-voices.
Important Notes on Model Selection
- Every model that was based on Lessac was excluded because they're not commercially usable.
- Every MLS (Multilingual LibriSpeech) model is skipped because they failed to produce short audio reliably in any language (just gibberish comes out if the sentence isn't long enough).
- Created
- Mar 22, 2026
- Updated
- Mar 27, 2026