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
Describe passage-based long-form synthesis in the model card
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- SHA256SUMS.json +2 -2
README.md
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| Voice conditioning | Fixed per-voice/per-locale identity and prosody references embedded in the graphs |
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| Text frontend | Trained Scylla's Band G2P with its matched tokenizer and vocabulary |
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Long text is spoken
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## Voices and language coverage
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| Voice conditioning | Fixed per-voice/per-locale identity and prosody references embedded in the graphs |
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| Text frontend | Trained Scylla's Band G2P with its matched tokenizer and vocabulary |
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Long text is spoken as passages within the trained target lengths (64 to 420 latent frames, about 1.4 to 9 seconds): a short first passage so audio starts quickly, then passages as long as the text allows, cut at a sentence end, else a clause, else between words, and measured with the requested delivery. Each passage sees the surrounding text as context and continues from the acoustics of the one before it, and consecutive passages are decoded as one continuous waveform. Durations are predicted deterministically; different controls can alter them.
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## Voices and language coverage
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SHA256SUMS.json
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"bytes": 11357
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