Introduction to Sherpa
The Sherpa project is an open-source framework designed to handle speech-to-text tasks, leveraging the power of PyTorch. Sherpa is noteworthy for its exclusive focus on end-to-end (E2E) models, specifically transducer and Connectionist Temporal Classification (CTC) models. This framework offers APIs in both C++ and Python, catering to developers who work in different programming environments.
Purpose and Focus
Sherpa's primary focus is on deployment. Its main use is for transcribing speech using pre-trained models, meaning it deals with the actual application of models rather than the training phase. If one seeks to train or fine-tune their own speech models, they are directed to explore a related project known as Icefall.
Related Projects
Sherpa also introduces two sibling projects for users who prefer not utilizing PyTorch:
- Sherpa-ONNX: This version uses the ONNX format, often preferred for its interoperability.
- Sherpa-NCNN: Utilizes the NCNN framework, which is particularly popular in mobile and embedded system applications.
Both sherpa-onnx and sherpa-ncnn extend their support to iOS, Android, and embedded systems, making them versatile options for deployment in various environments.
Official Resources and Documentation
For those interested in installing and making use of Sherpa, comprehensive documentation is available. It offers detailed guidance on setting up and running models: Sherpa Documentation.
Interactive Experience
Furthermore, Sherpa can be experienced directly from your browser without requiring any installation. This is made possible through a browser-based demonstration hosted on Hugging Face, allowing users to try automatic speech recognition first-hand: Try Sherpa Online.
In conclusion, Sherpa is a streamlined, efficient solution for anyone focused on deploying speech-to-text systems with pre-trained models, while its related projects provide additional flexibility for developers working in non-PyTorch environments.