Rhasspy
Piper TTS - Japanese (Kokoro)
Japanese voice.
About This Model
Piper TTS - Japanese (Kokoro) is a compact text-to-speech model developed by Rhasspy, designed to generate natural-sounding Japanese speech from written text. 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, Piper TTS - Japanese (Kokoro) delivers surprisingly high-quality audio output, characterized by clear and smooth intonation that closely mimics human speech. This makes it particularly suitable for applications where real-time performance is crucial, such as voice assistants, automated announcements, and interactive kiosks.
In its size class, Piper TTS - Japanese (Kokoro) stands out for its efficiency and performance. It punches well above its weight, offering a balance between resource consumption and output quality that is hard to match by larger models. The model's low VRAM requirement of just 0.1 GB means it can run smoothly on a wide range of hardware, from Raspberry Pis to older laptops. This accessibility makes it an excellent choice for developers and hobbyists looking to integrate high-quality Japanese TTS into their projects without the need for powerful or expensive hardware. Whether you're building a personal assistant or enhancing an educational app, Piper TTS - Japanese (Kokoro) is a reliable and efficient solution.
Check Your Hardware
See which quantizations of Piper TTS - Japanese (Kokoro) 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 - Japanese (Kokoro)?
Piper TTS - Japanese (Kokoro) requires 0.15GB VRAM minimum with ONNX quantization. For full precision, you need 0.15GB VRAM.
What is the best quantization for Piper TTS - Japanese (Kokoro)?
Q4_K_M offers the best balance of quality and VRAM usage. Q8_0 is near-lossless if you have enough VRAM.