ONNX export of Qwen/Qwen3-TTS-12Hz-1.7B-Base, a multilingual text-to-speech model from Alibaba Cloud's Qwen team with an ECAPA-TDNN speaker encoder for voice cloning from short reference audio. It is not a newly trained model. This is an unofficial community conversion.

The 1.7B Base variant is larger than the 0.6B Base and targets higher-quality synthesis where compute budget permits. The smaller variant is in tonythethompson/Qwen3-TTS-12Hz-0.6B-Base-ONNX.

Source

Field	Value
Upstream model	Qwen/Qwen3-TTS-12Hz-1.7B-Base
Upstream source revision	`fd4b254389122332181a7c3db7f27e918eec64e3`
Packaging source revision	`ab09194b7e07e645f2165fda2e95ac97c2446b38`
Export tool/script	ONNX export from upstream Qwen3-TTS PyTorch weights (community packaging)
Quantization recipe	See `onnx/` filenames for FP32/FP16/quant variants shipped in this repo

Files

File	Size	Description
`speaker_encoder.onnx` + `.data`	~46 MB	ECAPA-TDNN speaker encoder
`talker_prefill.onnx` + `.data`	~5 GB	Talker LM prefill
`talker_decode.onnx` + `.data`	~5 GB	Talker LM single-step decode
`code_predictor.onnx` + `.data`	~420 MB	Code Predictor
`tokenizer12hz_encode.onnx` + `.data`	~182 MB	12 Hz codec tokenizer (encode)
`vocoder.onnx` + `.data`	~437 MB	Vocoder decoder (24 kHz output)
`embeddings/`	~1.4 GB	Text/codec embedding tables (.npy) + config
`tokenizer/`	~5 MB	BPE tokenizer (vocab.json, merges.txt)

Architecture

Speaker Encoder: ECAPA-TDNN, encodes ~3 seconds of reference audio into a speaker embedding
Talker LM: Autoregressive transformer (larger than 0.6B), generates 12Hz codec tokens
Code Predictor: Predicts remaining RVQ codebook groups from the Talker output
Vocoder: RVQ dequantize -> decoder -> BigVGAN, 12Hz -> 24kHz audio output

Intended Use

Multilingual text-to-speech for local inference via ONNX Runtime. Synthesizes speech using a ~3-second voice reference clip, enabling voice-consistent output. The 1.7B Base is the higher-quality option; use the 0.6B Base for lower-latency or lower-VRAM deployments. The CustomVoice variant is in tonythethompson/Qwen3-TTS-12Hz-1.7B-CustomVoice-ONNX.

Runtime Notes

Designed for ONNX Runtime compatible runtimes.
Output sample rate: 24 kHz.
Voice cloning reference: ~3 seconds of reference audio recommended.
Memory: proportionally higher than the 0.6B variant; exact VRAM requirement not documented in this repo.
Validate on the target execution provider before production use.

Precision and Packaging

Export tooling, precision, and quantization are recorded in the Source table above. This packaging mirror does not publish independent parity benchmarks; validate on your target execution provider before production use.

Limitations

No repository-specific audio quality evaluation is documented here.
Voice cloning from very short or noisy reference clips may degrade results.
Multilingual capability inherited from the upstream model; consult the upstream Qwen3-TTS model card for language coverage.
ONNX export fidelity relative to the upstream PyTorch model is not documented here.

Safety and Responsible Use

Qwen3-TTS is a voice synthesis and voice cloning model capable of producing realistic speech closely matching a target speaker from a short audio reference.

Do not use to impersonate real individuals without their explicit consent.
Do not generate synthetic speech intended to deceive listeners about a speaker's identity.
Disclose AI-generated audio where listeners would reasonably expect a human voice.
Users are responsible for compliance with applicable laws governing synthetic media and voice cloning in their jurisdiction.

License

Apache 2.0 — inherited from Qwen/Qwen3-TTS-12Hz-1.7B-Base. This packaging repo adds no new license terms.

tonythethompson/Qwen3-TTS-12Hz-1.7B-Base-ONNX

Qwen3-TTS 12Hz 1.7B Base — ONNX