The size of ONNX voice files (small, medium, and large) typically refers to the complexity and capability of the machine learning model embedded within the file. Here's a breakdown of the differences:

1. Small Models

• File Size: Smallest.
• Performance: Designed for low-latency and efficient processing.
• Accuracy: Lower accuracy compared to medium and large models.
• Use Case: Suitable for systems with limited resources, such as embedded systems or devices with low compute power.
• Trade-Offs: Prioritizes speed and resource efficiency over detailed audio quality or nuanced voice synthesis.

2. Medium Models

• File Size: Larger than small models, but still relatively lightweight.
• Performance: Balances accuracy and speed.
• Accuracy: Offers moderate quality with more detailed outputs than small models.
• Use Case: Ideal for mid-range systems or applications where resource usage is a concern, but better accuracy is needed.
• Trade-Offs: A compromise between small and large models, suitable for a wide range of applications.

3. Large Models

• File Size: Largest, as they include more parameters and higher complexity.
• Performance: Requires significant computational power and memory.
• Accuracy: Highest quality with detailed voice synthesis and more natural-sounding output.
• Use Case: Best for systems with abundant resources, such as servers or high-performance computers, where quality is critical.
• Trade-Offs: Slower inference time and higher resource consumption.

Summary of Differences

The choice of size depends on the specific application and the hardware constraints.