Qwen3 TTS 12Hz 1.7B CustomVoice ONNX

Qwen3-TTS 12Hz 1.7B CustomVoice — ONNX

ONNX export of Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice for local inference with ONNX Runtime. Uses predefined built-in speaker voices selected by name at inference time; no reference audio is required. This is an unofficial community mirror of the ONNX export; it is not a newly trained model. The Qwen team (Alibaba Cloud) is the original author. The 0.6B version is in tonythethompson/Qwen3-TTS-12Hz-0.6B-CustomVoice-ONNX.

Source

Field	Value
Upstream model	Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice
Upstream source revision	0c0e3051f131929182e2c023b9537f8b1c68adfe
Packaging source revision	a2b73f56ffec086c75f154024d5fc7f391f228af
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	Description	Size
talker_prefill.onnx + .data	Talker LM prefill (28 layers, hidden=2048)	~5.3 GB
talker_decode.onnx + .data	Talker LM single-step decode	~5.3 GB
code_predictor.onnx + .data	Code Predictor (5 layers, 15 groups)	~428 MB
vocoder.onnx	Vocoder decoder (24kHz output)	~435 MB
embeddings/	Text/codec embeddings as .npy + config	~1.4 GB
tokenizer/	BPE tokenizer (vocab.json, merges.txt)	~4 MB

Architecture

• Talker: 28 transformer layers, 16 attn heads, 8 KV heads, hidden=2048 (larger than 0.6B)
• Code Predictor: 5 layers, hidden=1024, generates codebook groups 1-15
• Vocoder: RVQ dequantize -> transformer -> BigVGAN decoder, 12Hz codec -> 24kHz audio
• KV Cache: Decode step uses stacked format (num_layers, B, num_kv_heads, T, head_dim)

Available Speakers

Built-in speaker identities: serena, vivian, uncle_fu, ryan, aiden, ono_anna, sohee, eric, dylan

Intended Use

Multilingual text-to-speech for local inference via ONNX Runtime using predefined voices. The 1.7B CustomVoice variant targets higher synthesis quality than the 0.6B where VRAM and compute budget permit.

Standalone usage (external project)

The snippet below uses an external C# project by elbruno and references that project's ONNX repo, not this one.

git clone https://github.com/elbruno/ElBruno.QwenTTS.git
cd ElBruno.QwenTTS
python python/download_onnx_models.py --repo-id elbruno/Qwen3-TTS-12Hz-1.7B-CustomVoice-ONNX
dotnet run --project src/QwenTTS -- --model-dir python/onnx_runtime --text "Hello world" --speaker ryan --language english

Runtime Notes

• Designed for ONNX Runtime compatible runtimes.
• Output sample rate: 24 kHz.
• Memory: the talker model pair alone is ~10.6 GB on disk; GPU VRAM requirement is proportionally larger than the 0.6B variant.
• No reference audio required; select a speaker by name.
• 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

• Very large model; requires substantial VRAM for GPU inference.
• Predefined speaker set is fixed; adding new voices requires the Base variant with ECAPA-TDNN voice cloning.
• No repository-specific quality evaluation is documented here.

Safety and Responsible Use

Qwen3-TTS is a voice synthesis model capable of producing realistic speech. CustomVoice uses predefined speaker identities, which limits arbitrary voice cloning, but synthesized output can still be deceptive to listeners.

• Do not create synthetic speech intended to deceive listeners.
• Disclose AI-generated audio where listeners would reasonably expect a human voice.
• Users are responsible for compliance with applicable laws governing synthetic media in their jurisdiction.

License

Apache 2.0 — same as Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice. This packaging repo adds no new license terms.