To get this model running locally in no time, utilize the built-in WSL tools.
Just follow the guidelines provided below.
The script takes care of fetching the multi-gigabyte model weights.
The smart installation system will instantly find the perfect configuration.
Unlocking the Potential of Advanced Voice Technology
Our cutting-edge text-to-speech model, Qwen3-TTS-12Hz-1.7B-CustomVoice, represents a significant breakthrough in voice synthesis. With its 12 Hz frame rate, it delivers high-fidelity voice synthesis that is unmatched in the industry. By supporting custom voice cloning, users can create personalized speech that retains the speaker’s unique characteristics, resulting in a more authentic and engaging listening experience.• The model’s 1.7 B parameter architecture strikes a perfect balance between performance and memory usage, 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.• With its optimization for multiple languages and prosodic styles, the model produces natural-sounding output across a wide range of domains.
| Key Features | Description |
|---|---|
| Parameter Count | 1.7 B |
| Sample Rate | 12 Hz (frame) |
| Training Data | 200 h multi-speaker speech |
| Latency | 50 ms |
| Supported Languages | 20+ |
Technical Specifications at a Glance
| Specification | Value || — | — || Parameter Count | 1.7 B || Sample Rate | 12 Hz (frame) || Training Data | 200 h multi-speaker speech || Latency | 50 ms |What is the primary benefit of using Qwen3-TTS-12Hz-1.7B-CustomVoice in real-time applications?
The primary benefit of using Qwen3-TTS-12Hz-1.7B-CustomVoice in real-time applications is its ability to produce high-quality, natural-sounding voice synthesis with low latency, making it ideal for interactive assistants and live dubbing.
How does the model’s custom voice cloning feature work?
The model’s custom voice cloning feature allows users to train on just a few samples and generate personalized speech that retains the speaker’s unique characteristics. This results in a more authentic and engaging listening experience.
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