Create a Lite Version of Ultimate-RVC for Lower Resource Environments

[BF667]

Overview

Based on issue #209, comment, there is a strong user need for a lightweight ("lite") version of Ultimate-RVC. Many users are interested in running RVC on less powerful hardware (e.g., laptops, devices with limited RAM/CPU, or cloud instances with restricted specs).

Proposed Features for the Lite Version

• Reduced Resource Footprint: Significantly lower memory and CPU usage compared to the full version.
• Optional/Minimal Dependencies: Remove or make optional any non-essential libraries and features (e.g., advanced GUIs, heavy model checkpoints, or plugins).
• Streamlined UI/UX: Consider a minimal UI (or CLI-only) mode for ultra-lightweight operation.
• Smaller Download Size: Optimize package, models, and dependencies for rapid installation and portability.
• Pre-configured for Typical Use Cases: Ship with a default configuration targeted at typical lightweight use—voice conversion with a single model, no training, and basic inference.
• Documentation: Create a clear guide for installing and running the lite version, including system requirements and feature differences compared to the full version.
• Extensibility: Allow easy upgrade from lite to full version if needed.

Potential Implementation Steps

1. Profile Current Resource Usage: Benchmark RAM/CPU on typical tasks to identify biggest resource drains.
2. Modularize the Codebase: Ensure features can be toggled on/off or removed entirely for the lite version.
3. Create a Lite Build/Install Option: E.g., pip install ultimate-rvc[lite] or a separate branch/fork.
4. Test on Low-End Hardware: Validate with minimum spec targets (e.g., 2GB RAM, dual-core CPU).
5. Document Feature Matrix: Publish a table comparing full and lite versions.

Value to the Community

• Expands usability to a wider audience, especially those on limited hardware.
• Makes cloud/edge deployment more feasible and affordable.
• Responds directly to community feedback and demand.

@JackismyShephard

[BF667]

Good?

[JackismyShephard]

This is still quite vague. Issues should be specific rather than generic AI output. Maybe you can start a thread on the discussion page and we can hash things out further there. I am by no means opposed to implementing a "lite" version of the app, but its important to first define which exact features we want and why. Part of that reasoning will probably involve doing some benchmarking as you suggest. I don't have a lot of time on my hands right now though, so its probably not something I will work on much.