3bf986d08d
This PR adds support for non-streaming Zipformer CTC ASR models across multiple language bindings, WebAssembly, examples, and CI workflows. - Introduces a new OfflineZipformerCtcModelConfig in C/C++, Python, Swift, Java, Kotlin, Go, Dart, Pascal, and C# APIs - Updates initialization, freeing, and recognition logic to include Zipformer CTC in WASM and Node.js - Adds example scripts and CI steps for downloading, building, and running Zipformer CTC models Model doc is available at https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-ctc/icefall/zipformer.html
Introduction
sherpa-onnx is an open-source real-time speech recognition toolkit developed by the Next-gen Kaldi team.
It supports streaming recognition on a variety of platforms such as Android, iOS, Raspberry, Linux, Windows, macOS, etc.
It does not require Internet connection during recognition.
See the documentation https://k2-fsa.github.io/sherpa/onnx/index.html for details.
Please see https://github.com/k2-fsa/sherpa-onnx/tree/dot-net/dotnet-examples for how to use C# APIs of this package.