Rhasspy
Piper TTS - Chinese (Huayan)
Chinese Mandarin voice.
About This Model
Piper TTS - Chinese (Huayan) is a compact text-to-speech model developed by Rhasspy, designed to convert written Chinese text into natural-sounding speech. With only 0.02 billion parameters, this model is exceptionally lightweight, making it highly efficient for devices with limited computational resources. Despite its small size, the model delivers surprisingly clear and coherent speech, which is particularly useful for applications requiring real-time audio output without significant delays. The model is available in ONNX format, which supports efficient inference on a variety of hardware platforms, including low-end GPUs and even some CPUs.
In its size class, Piper TTS - Chinese (Huayan) punches well above its weight. It achieves a balance between performance and resource consumption that makes it an excellent choice for developers and hobbyists looking to integrate high-quality TTS capabilities into their projects without the need for powerful hardware. The model’s efficiency is evident in its minimal VRAM requirement of just 0.1 GB, allowing it to run smoothly on budget-friendly devices such as Raspberry Pi or older laptops. This makes it ideal for educational tools, personal assistants, and IoT devices where computational power is limited but the need for accurate and natural-sounding speech synthesis is crucial.
Check Your Hardware
See which quantizations of Piper TTS - Chinese (Huayan) your hardware can run.
Quantization Options
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| ONNX | 16 | 0.063 GB | 0.15 GB | 0.3 GB | 80% |
Frequently Asked Questions
How much VRAM do I need to run Piper TTS - Chinese (Huayan)?
Piper TTS - Chinese (Huayan) requires 0.15GB VRAM minimum with ONNX quantization. For full precision, you need 0.15GB VRAM.
What is the best quantization for Piper TTS - Chinese (Huayan)?
Q4_K_M offers the best balance of quality and VRAM usage. Q8_0 is near-lossless if you have enough VRAM.