Instructions to use arraypress/tts-supertonic-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Supertonic
How to use arraypress/tts-supertonic-3 with Supertonic:
from supertonic import TTS tts = TTS(auto_download=True) style = tts.get_voice_style(voice_name="M1") text = "The train delay was announced at 4:45 PM on Wed, Apr 3, 2024 due to track maintenance." wav, duration = tts.synthesize(text, voice_style=style) tts.save_audio(wav, "output.wav")
- Notebooks
- Google Colab
- Kaggle
Download README.md from arraypress/tts-supertonic-3: direct link, hf CLI and curl.
- Browser
- Download file 1.83 kB
-
https://huggingface.co/arraypress/tts-supertonic-3/resolve/main/README.md
- Command line
-
hf download hf://arraypress/tts-supertonic-3/README.md
-
curl -L -o README.md https://huggingface.co/arraypress/tts-supertonic-3/resolve/main/README.md
license: openrail
license_name: bigscience-openrail-m
license_link: LICENSE
pipeline_tag: text-to-speech
tags:
- tts
- onnx
- supertonic
- mirror
base_model: Supertone/supertonic-3
Supertonic 3 (mirror) — for the arraypress speech tools
An unmodified mirror of Supertone/supertonic-3
at commit 3cadd1ee6394adea1bd021217a0e650ede09a323: the six ONNX/JSON files of the
pipeline (duration predictor, text encoder, vector estimator, vocoder, tokenizer config,
unicode indexer) and the ten voice styles (F1–F5, M1–M5), ~410 MB. It exists so the fleet's
swift-tts (and the speak / redub tools on it) download from a repository pinned and
controlled by the same people as the tools, rather than following another repo's main.
Every file matches the hash Supertone publishes (SHA-256 for the large files, git blob SHA-1 for the small ones). For the model itself — what it is, quality, languages — see Supertone's model card.
Licence and use restrictions
BigScience OpenRAIL-M (LICENSE). Using these files means accepting it, including the
use-based restrictions in Attachment A — among them: no use that violates law; no
exploiting or harming minors; no generating or disseminating verifiably false information
to harm others; no personally identifiable information to harm individuals; no
defamation or harassment; no fully automated decisions that adversely affect legal rights;
no discrimination; no medical advice or results interpretation; no use for administration
of justice or law enforcement in ways that adversely affect rights. Read Attachment A for
the full list. Anyone you share the model or a derivative with must be bound by the same
restrictions. Attribution: see NOTICE.