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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
This directory contains examples for how to use the VAD (voice activity detection) with non-streaming speech recognition models.
| Directory | Description |
|---|---|
| run-vad-with-dolphin-ctc.sh | It shows how to use the VAD + Dolphin for speech recognition. |
| run-vad-with-whisper.sh | It shows how to use the VAD + Whisper for speech recognition. |
| run-vad-with-sense-voice.sh | It shows how to use the VAD + SenseVoice for speech recognition. |
| run-vad-with-moonshine.sh | It shows how to use the VAD + Moonshine for speech recognition. |
Please refer to non-streaming-asr for more kinds of non-streaming models.