The Piper TTS - French (Siwis) model, developed by Rhasspy, is a compact text-to-speech solution designed to generate natural-sounding French speech. With just 0.02 billion parameters, this model is incredibly lightweight, making it highly efficient for local deployment on devices with limited resources. Despite its small size, the model delivers surprisingly high-quality audio, thanks to its well-optimized architecture. The Siwis voice, known for its clarity and naturalness, makes this model particularly suitable for applications requiring a human-like French voice, such as virtual assistants, audiobook narration, and interactive voice responses.
In its size class, the Piper TTS - French (Siwis) model stands out for its efficiency and performance. It punches well above its weight, offering a balance between computational requirements and output quality that is hard to match with larger models. The model's low VRAM requirement of 0.5 GB makes it accessible on a wide range of hardware, from older laptops to modern smartphones. This versatility means that developers and enthusiasts who are constrained by hardware limitations can still achieve professional-grade text-to-speech capabilities. Ideal users include those working on embedded systems, mobile applications, or any project where resource optimization is crucial.
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| ONNX | 16 | 0.026 GB | 0.53 GB | 1.03 GB | 80% |
How to run Piper TTS - French (Siwis)
Pick a runtime — copy & paste. Commands are pre-filled with this model’s repo.
Fast on-device neural TTS. Single binary, ONNX runtime.
Piper home →- 1
Install
brew install piper # macOS — or grab the binary from GitHub releases - 2
Synthesize
echo "Hello from Piper" | piper --model piper-tts-fr-siwis.onnx --output_file out.wav
Community benchmarks
Real tokens/sec reports from people running Piper TTS - French (Siwis) on actual hardware.
No community runs yet for this model. Be the first to submit your numbers.
how much VRAM do I need to run Piper TTS - French (Siwis)?
Piper TTS - French (Siwis) requires 0.53 GB VRAM minimum with ONNX quantization. For full precision you need 0.53 GB.
which quant should I pick?
Q4_K_M is the best quality/VRAM balance — ~92% of FP16 quality at ~25% the footprint. Q8_0 is near-lossless if you have the headroom.
What GPU do I need to run Piper TTS - French (Siwis)?
Piper TTS - French (Siwis) requires at least 0.5 GB of VRAM. Any modern GPU with this amount of VRAM should suffice.
Is Piper TTS - French (Siwis) good for coding?
Piper TTS - French (Siwis) is designed for text-to-speech tasks, not coding. It can be used to generate spoken text but is not suitable for programming tasks.
Piper TTS - French (Siwis) vs Llama 3.1 8B?
Piper TTS - French (Siwis) is a small, specialized TTS model with 0.02B parameters, while Llama 3.1 8B is a large, general-purpose language model. They serve different purposes and are not directly comparable.
Can I run Piper TTS - French (Siwis) on a Mac?
Yes, you can run Piper TTS - French (Siwis) on a Mac as long as your system meets the minimum VRAM requirement of 0.5 GB.
How much VRAM does Piper TTS - French (Siwis) need?
Piper TTS - French (Siwis) requires 0.5 GB of VRAM, which is consistent across all quantization options.
Is Piper TTS - French (Siwis) censored?
Piper TTS - French (Siwis) is not censored. It generates speech based on the input text without any content filtering.
Is Piper TTS - French (Siwis) commercial-use allowed?
Yes, Piper TTS - French (Siwis) is licensed under the MIT license, which allows for both personal and commercial use.
Piper TTS - French (Siwis) context length?
The context length for Piper TTS - French (Siwis) is unknown. However, it is designed to handle typical sentence lengths for TTS applications.
Does Piper TTS - French (Siwis) support function calling?
Piper TTS - French (Siwis) is a text-to-speech model and does not support function calling or other interactive features.
Piper TTS - French (Siwis) quantization options?
Piper TTS - French (Siwis) supports quantization, but the specific options are not detailed. Check the documentation for more information.
Can Piper TTS - French (Siwis) run on CPU?
Yes, Piper TTS - French (Siwis) can run on CPU, although it may be slower compared to running on a GPU with 0.5 GB VRAM.
Piper TTS - French (Siwis) fine-tuning?
Piper TTS - French (Siwis) can be fine-tuned to improve performance on specific tasks or datasets, but this requires additional data and training resources.
Piper TTS - French (Siwis) system requirements?
Piper TTS - French (Siwis) requires at least 0.5 GB of VRAM, a modern CPU, and sufficient RAM to load the model. It is compatible with various operating systems.
Piper TTS - French (Siwis) performance benchmark?
Performance benchmarks for Piper TTS - French (Siwis) vary based on hardware. On a GPU with 0.5 GB VRAM, it typically processes text at a rate of several tokens per second.
Piper TTS - French (Siwis) for RAG?
Piper TTS - French (Siwis) is not designed for Retrieval-Augmented Generation (RAG). It is a specialized TTS model and does not have the capabilities for retrieval-based tasks.
Piper TTS - French (Siwis) for agents?
Piper TTS - French (Siwis) can be integrated into virtual agents or chatbots to provide natural-sounding French speech output.
Piper TTS - French (Siwis) for coding vs general?
Piper TTS - French (Siwis) is not intended for coding tasks. It is a general-purpose TTS model optimized for generating spoken French text.
Piper TTS - French (Siwis) vs ChatGPT?
Piper TTS - French (Siwis) is a TTS model focused on generating speech, while ChatGPT is a conversational AI model. They serve different purposes and are not directly comparable.
Piper TTS - French (Siwis) download size?
The download size for Piper TTS - French (Siwis) is relatively small due to its 0.02B parameters, typically around a few hundred megabytes.
Best quant for Piper TTS - French (Siwis)?
The best quantization option for Piper TTS - French (Siwis) depends on your specific needs. Generally, a balance between model size and performance is recommended. Check the documentation for available options.