canto-tts-nano

canto-tts-nano-v1

Open-source Hong Kong Cantonese text-to-speech, fine-tuned from MOSS-TTS-Nano (0.1B params, GPT-2 backbone, Apache-2.0). CPU-first — runs via ONNX Runtime, no GPU required.

This repo ships the ONNX export consumed by the canto-tts Python SDK. It bundles the fine-tuned TTS graphs, the stock (unmodified) OpenMOSS audio-codec ONNX graphs, and one baked default voice.

Why a dedicated Cantonese fine-tune

MOSS-TTS-Nano's base pretraining is Mandarin-centric — feeding it raw Hanzi produces Mandarin-accented output. This fine-tune instead feeds jyutping phoneme tokens (onset/rime/tone) for Chinese characters, while leaving English words as plain orthography (the base model's multilingual English prior is already good) — this is what actually yields Cantonese output with natural English code-switching. Text→jyutping conversion is handled upstream by canto-hk-g2p; this checkpoint only consumes phoneme tokens, it does not do grapheme-to-phoneme itself.

Usage

pip install canto-tts   # see https://github.com/typangaa/canto-tts for current install instructions

from canto_tts import CantoTTS

tts = CantoTTS()  # auto-downloads this repo via huggingface_hub, cached under ~/.cache/huggingface
tts.synthesize("多謝晒，今日天氣幾好。", "hello.wav")

# English code-switching
tts.synthesize("我哋一齊去 IFC food court 食飯。", "codeswitching.wav")

Advanced / no-SDK usage: browser_poc_manifest.json describes the ONNX graph layout (prefill/decode steps for the TTS local+global transformer, plus the codec encode/decode graphs) and carries the baked default-voice audio_codes — see canto_tts.backends.onnx_backend.OnnxBackend in the SDK repo for the reference ONNX Runtime session-wiring implementation.

Gate results (N=5 repeats, Qwen3-ASR-1.7B judge)

Full lineage, methodology, and paired-bootstrap significance notes: see the GitHub repo.

Default voice

The single baked default voice is this checkpoint's own unconditional (mode="continuation") output — i.e. self-generated speech, not a copy of any identifiable real person's recording and not borrowed from another project's demo asset. This sidesteps voice-likeness/rights questions entirely for the shipped default. There is currently no runtime voice selection in the ONNX bundle (one baked voice only) — multi-voice picking is planned for a future release via the underlying checkpoint's native zero-shot voice-cloning path, not this static ONNX export.

Known limitations

• Single default voice — no voice selection or cloning in this ONNX bundle.
• Cantonese + English code-switching only — Mandarin capability was intentionally not preserved (the phoneme fine-tune overrides the base model's Mandarin reading prior for Chinese characters by design).
• No explicit style/emotion/gender control at inference — the training schema's control fields are fixed to a neutral default for this checkpoint.

License

Apache-2.0, inherited from MOSS-TTS-Nano (OpenMOSS). The audio codec graphs (MOSS-Audio-Tokenizer-Nano-ONNX/) are the stock, unmodified OpenMOSS codec, included here only for convenience so the SDK has a single download.

Training data is privately sourced and not released (copyright) — only these weights are public.