Show HN: Run 25 Audio Models (TTS/STT/STS/Muisc) Locally in C++/GGML. No Python (github.com)

The newly launched C++ framework, audio.cpp, revolutionizes audio model deployment by enabling local execution of 25 diverse audio models, including text-to-speech (TTS), speech-to-text (STT), and music generation, without relying on Python. Built on the ggml framework, it promises enhanced portability and performance by offering a shared native runtime that addresses common issues developers face, such as managing multiple Python environments and package conflicts. Notably, models running on this framework can achieve speeds 1.8x to 5.0x faster than their Python counterparts while significantly reducing end-to-end latency. This framework's significance lies in its ability to support a wide range of audio processing tasks through a unified API, enhancing the ease of model integration and improvement. Key features include strong performance optimizations for CUDA, experimental JSON pipeline support for complex workflows, and built-in utilities for tasks like denoising and resampling. With the recent inclusion of four new Automatic Speech Recognition (ASR) families and continuous updates enhancing model performance and capabilities, audio.cpp sets a robust foundation for future advancements in local audio processing, aiming to streamline the development of high-performance audio applications.