Qwen3 TTS 12Hz 0.6B CustomVoice ONNX

Qwen3-TTS 12Hz 0.6B CustomVoice — ONNX

ONNX export of Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice for local inference with ONNX Runtime. Unlike the Base variant, CustomVoice does not include the ECAPA-TDNN speaker encoder; it uses a set of predefined built-in speaker voices selected by name at inference time. 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.

Source

Field	Value
Upstream model	Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice
Upstream source revision	85e237c12c027371202489a0ec509ded67b5e4b5
Packaging source revision	747ac4c3f6c6a317e83f6303148f28585a8bcadf
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)	~1.7 GB
talker_decode.onnx + .data	Talker LM single-step decode	~1.7 GB
code_predictor.onnx	Code Predictor (5 layers, 15 groups)	~420 MB
vocoder.onnx + .data	Vocoder decoder (24kHz output)	~437 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=1024
• Code Predictor: 5 layers, generates codebook groups 1-15 from Talker output
• 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 a predefined voice profile rather than reference-audio voice cloning. Voice cloning from reference audio requires the Base variant tonythethompson/Qwen3-TTS-12Hz-0.6B-Base-ONNX; the 1.7B CustomVoice variant is in tonythethompson/Qwen3-TTS-12Hz-1.7B-CustomVoice-ONNX.

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/qwen-labs-cs.git
cd qwen-labs-cs
python python/download_onnx_models.py --repo-id elbruno/Qwen3-TTS-12Hz-0.6B-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.
• 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

• 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.
• Multilingual coverage: verify the upstream model card for supported languages and quality by language.

Safety and Responsible Use

Qwen3-TTS is a voice synthesis model capable of producing realistic speech. CustomVoice uses predefined speaker identities (not arbitrary voice cloning), which limits some misuse vectors, but the 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-0.6B-CustomVoice. This packaging repo adds no new license terms.