k2-fsa/sherpa
Speech-to-text server framework with next-gen Kaldi
What it solves
It provides a streamlined way to deploy pre-trained end-to-end speech-to-text models, removing the need for the training infrastructure when you only need to transcribe speech.
How it works
The framework uses PyTorch to run inference on transducer- and CTC-based models. It offers both C++ and Python APIs to integrate these models into applications.
Who it’s for
Developers who want to use pre-trained speech-to-text models for transcription tasks without needing to train or fine-tune them.
Highlights
- End-to-end (E2E) model focus
- Supports both C++ and Python APIs
- Built on PyTorch
- Specifically designed for deployment and inference