Solidaxel/Qwen3-TTS-GGUF

Qwen3-TTS GGUF + ONNX

Pre-converted weights for running Qwen3-TTS 12Hz 1.7B Base entirely via llama.cpp + ONNX Runtime. No PyTorch required.

Which model is this: the 1.7B Base variant (voice cloning from a reference sample), not the 0.6B, and not CustomVoice or VoiceDesign. About 1.5 GB total.

Why this repo

The upstream conversion toolchain expects the original PyTorch weights and a three-stage export (codec/speaker/decoder ONNX, talker GGUF, predictor GGUF) spread across a dozen scripts. These files are the finished output of that pipeline: download and run.

Files

File	Size	Format
qwen3_tts_talker.q5_k.gguf	960 MB	GGUF Q5_K
qwen3_tts_predictor.q8_0.gguf	145 MB	GGUF Q8_0
qwen3_tts_decoder.fp16.onnx	220 MB	ONNX FP16
qwen3_tts_speaker_encoder.fp16.onnx	24 MB	ONNX FP16
qwen3_tts_codec_encoder.fp16.onnx	109 MB	ONNX FP16
tokenizer.json	11 MB	JSON

INT8 and FP32 variants are also included for the ONNX components.

Usage

Requires the HaujetZhao/Qwen3-TTS-GGUF runtime package and llama.cpp compiled libraries.

pip install huggingface_hub
hf download Solidaxel/Qwen3-TTS-GGUF --local-dir model-base

Then point the runtime at that directory:

from qwen3_tts_gguf import TTSEngine, TTSConfig

engine = TTSEngine(model_dir="model-base")
stream = engine.create_stream()
stream.set_voice("output/elaborate/sample.json")   # reference voice

result = stream.clone("Hello, world!", config=TTSConfig(temperature=0.8, seed=42))
stream.join()
result.save("output.wav")

The runtime also ships 51-Interactive-Clone.py for an interactive session and 43-Inference-Base.py for the Base variant, which is the one in this repo.

Credits

• Original model: Qwen/Qwen3-TTS-12Hz-1.7B-Base (Apache 2.0)
• GGUF/ONNX conversion: HaujetZhao/Qwen3-TTS-GGUF (MIT)
• Redistribution: SolidAxel Project

License

Apache 2.0, same as the original model.