Sherpa is an open-source speech-to-text inference framework designed for efficient deployment of end-to-end (E2E) models, specifically transducer and CTC-based architectures. It targets developers and researchers needing to integrate pre-trained speech recognition models into applications, offering both C++ and Python APIs for flexibility.

How It Works

Sherpa leverages PyTorch for its core inference engine, focusing on optimized deployment of E2E models. This approach allows for streamlined integration of advanced speech recognition capabilities directly into applications, bypassing the complexities of model training pipelines.

Quick Start & Requirements

• Installation: Refer to the official documentation at https://k2-fsa.github.io/sherpa/
• Demo: Try Sherpa in your browser at https://huggingface.co/spaces/k2-fsa/automatic-speech-recognition

Highlighted Details

• Supports both transducer and CTC-based end-to-end models.
• Provides both C++ and Python APIs for broad integration.
• Focuses exclusively on inference and deployment of pre-trained models.
• Offers alternative implementations (sherpa-onnx, sherpa-ncnn) for mobile and embedded systems.

Maintenance & Community

• Project is hosted by k2-fsa.
• Further community and roadmap details are available via the official documentation.

Licensing & Compatibility

• License details are not explicitly stated in the provided README snippet. Further investigation into the repository is recommended for licensing specifics and commercial use compatibility.

Limitations & Caveats

The project explicitly states it is not for model training or fine-tuning; users interested in those aspects should refer to the icefall project.