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

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