Rhasspy
Piper TTS - Russian (Irina)
Russian female voice.
About This Model
Piper TTS - Russian (Irina) is a lightweight text-to-speech model developed by Rhasspy, designed to generate natural-sounding Russian speech from text inputs. With only 0.02 billion parameters, this model is exceptionally compact, making it highly efficient for local deployment. Despite its small size, Piper TTS - Russian (Irina) delivers surprisingly good audio quality, with clear and articulate speech that closely mimics the voice of Irina, a typical Russian speaker. The model's efficiency is further enhanced by its ONNX quantization, which reduces the VRAM requirement to just 0.1 GB, making it suitable for devices with limited resources.
In its size class, Piper TTS - Russian (Irina) stands out as a model that punches well above its weight. It offers a balance between performance and resource consumption that is hard to match by larger, more resource-intensive models. This makes it an excellent choice for developers and hobbyists who need a reliable text-to-speech solution for projects running on low-power devices such as Raspberry Pis, smartphones, or even older laptops. Users looking for a lightweight, high-quality TTS system for applications like voice assistants, e-learning platforms, or personal projects will find this model particularly useful. Given its minimal hardware requirements, almost any modern device can handle Piper TTS - Russian (Irina) without breaking a sweat.
Check Your Hardware
See which quantizations of Piper TTS - Russian (Irina) 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 - Russian (Irina)?
Piper TTS - Russian (Irina) requires 0.15GB VRAM minimum with ONNX quantization. For full precision, you need 0.15GB VRAM.
What is the best quantization for Piper TTS - Russian (Irina)?
Q4_K_M offers the best balance of quality and VRAM usage. Q8_0 is near-lossless if you have enough VRAM.