Instructions to use ampixa/sanoTTS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Piper
How to use ampixa/sanoTTS with Piper:
# 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
Commit History
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web: pinyin Chinese voice bundle (zh-xiaoya, 1,554,284 params, f16)
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Voice bundles at float16, and the eight ~511k variants
Verified signatureThe web bundles widened fp16 to f32 at export and shipped double the bytes. They now ship float16 and are widened after download; the browser verifies a sha256 of the widened result. 79.0 MB -> 39.5 MB across the eighteen. float16 is the reference here, not an approximation: every CER and WER number these voices have was measured on their roota.raw-fp16.v1 package. The twelve previously released voices are untouched and stay f32. Also adds the eight ~511k variants.94fda4d -
Add ten new-language voice bundles for the browser demo
Verified signaturede, fr, es, it, pt, ru, cs, ro, tr, ar at ~1.56M each, in the web {meta.json, front_f32.bin, dec_f32.bin} format the demo fetches. Russian and Arabic additionally need assets served same-origin by the site: web/g2p-lazy/ru (sharded espeak lexicon, ~150KB per page of prose) and web/tashkeel (the 1.16M-parameter diacritizer Piper runs before espeak; without it Arabic scores 44.55% of the correct phoneme ids).593dbac
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web: browser-format voice blobs so sanotts-web can fetch weights from here
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