Text-to-Speech
LiteRT
LiteRT
ONNX
Core ML
PyTorch
tts
singing
singing-voice-synthesis
multilingual
expressive-tts
duration-flow
Instructions to use spybyscript/scyllasband with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use spybyscript/scyllasband with LiteRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Add a delivery primer to the model card
Browse files- README.md +22 -0
- SHA256SUMS.json +2 -2
README.md
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@@ -32,6 +32,28 @@ The default download is the LiteRT bundle, installed under `scyllasband/models/l
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| Energy, tension, valence, assertiveness | 0–4 or unspecified | 2 each |
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| Whisper | on/off or unspecified | off |
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`--delivery auto` omits all coordinates. A per-axis `auto` omits just that coordinate; other unspecified fields in a partial request use neutral defaults. Omission is represented by a separate mask, not by the value 2. It is not an automatic text-to-performance planner.
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The graph receives `(rating - 2) / 4` for the four continuous axes and 0/1 for whisper, plus five presence bits. The public interface accepts the original 0–4 values. Expressiveness is an auxiliary training target, not an inference axis.
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| Energy, tension, valence, assertiveness | 0–4 or unspecified | 2 each |
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| Whisper | on/off or unspecified | off |
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With every control at 2, the default, voices speak in a calm, conversational delivery with a neutral tone. Moving a control away from 2 changes the delivery:
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| Control | Toward 0 | Toward 4 |
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| --- | --- | --- |
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| Energy | Lower, darker voice with more pauses between phrases | Higher, brighter, more projected voice |
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| Tension | Slightly looser and steadier | Tighter, with wider pitch swings |
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| Valence | Flatter pitch, a little slower, longer pauses | Livelier pitch, a little quicker, shorter pauses |
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| Assertiveness | Hesitant, with longer pauses between phrases | Faster and clipped with few pauses; the strongest control over pace |
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Whisper on gives quieter, breathy speech. Combining the controls gives familiar deliveries:
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| Delivery | Settings |
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| --- | --- |
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| Calm and soft | `energy=1.2,tension=0.8,valence=2.6` |
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| Assertive and rapid-fire | `energy=2.6,tension=2.2,assertiveness=3.6` |
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| Joyful | `energy=3.2,valence=3.6` |
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| Angry | `energy=3.4,tension=3.4,valence=0.8,assertiveness=3.4` |
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| Sad | `energy=1,tension=1.6,valence=0.8,assertiveness=1.6` |
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| Whispered | `whisper=on` |
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The [voice gallery](https://lowkeytea.github.io/scyllasband/#delivery) plays each of these for all ten voices with the exact settings used. How far a control moves the delivery varies with voice, language and text, so start near 2 and listen as you move toward the extremes.
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`--delivery auto` omits all coordinates. A per-axis `auto` omits just that coordinate; other unspecified fields in a partial request use neutral defaults. Omission is represented by a separate mask, not by the value 2. It is not an automatic text-to-performance planner.
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The graph receives `(rating - 2) / 4` for the four continuous axes and 0/1 for whisper, plus five presence bits. The public interface accepts the original 0–4 values. Expressiveness is an auxiliary training target, not an inference axis.
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SHA256SUMS.json
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"bytes": 11357
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"README.md": {
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"sha256": "
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"bytes":
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"litert/assets/fixed_references.json": {
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"sha256": "d3c26e7e90d90fa719254ff1ff05dc4b391cd47231b7556c3c2ed5f63547925b",
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"bytes": 11357
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"litert/assets/fixed_references.json": {
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