Deploy Qwen3-TTS-12Hz-1.7B-CustomVoice

The fastest way to get this model running locally is via Docker.

Follow the step-by-step instructions below.

Once launched, the setup wizard will detect your specs to configure the model for maximum efficiency.

🧮 Hash-code: c20650afa1c08706751d36bd5ca3aa6f • 📆 2026-06-26



  • Processor: Intel i7 / Ryzen 7 for heavy Quantized models
  • RAM: at least 32 GB in dual-channel mode for bandwidth
  • Disk Space: free: 80 GB on system drive for scratch space
  • Graphics: TensorRT-LLM / vLLM inference engine compatible chip

Qwen3-TTS-12Hz-1.7B-CustomVoice is a cutting‑edge text‑to‑speech model that delivers high‑fidelity voice synthesis at a 12 Hz frame rate. It supports custom voice cloning, allowing users to train on just a few samples and generate personalized speech that retains the speaker’s unique characteristics. Its 1.7 B parameter architecture balances performance with a low memory footprint, making it suitable for deployment on consumer‑grade hardware. Inference latency stays under 50 ms per utterance, enabling real‑time applications such as interactive assistants and live dubbing. The model has been optimized for multiple languages and prosodic styles, producing natural‑sounding output across a wide range of domains.

Spec Value
Parameter Count 1.7 B
Sample Rate 12 Hz (frame)
Training Data 200 h multi‑speaker speech
Latency <50 ms
Supported Languages 20+

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