xuning / sherpaonnx

Refactors and extends model export support to include new NeMo Parakeet TDT int8 variants for English and Japanese, updating the Kotlin API, export scripts, test runners, and CI workflows.

- Added support for two new int8 model types in OfflineRecognizer.kt.
- Enhanced Python export scripts to perform dynamic quantization and metadata injection.
- Updated shell scripts and GitHub workflows to package, test, and publish int8 model artifacts.

Add support for the NeMo Canary model in both Java and Kotlin APIs, wiring it through
JNI and updating examples and CI.

- Introduce OfflineCanaryModelConfig in Kotlin and Java with builder patterns
- Extend OfflineRecognizer to accept and apply the new canary config via setConfig
- Update JNI binding (GetOfflineConfig) and getOfflineModelConfig mapping (type 32), 
   plus examples and CI workflows

This PR introduces support for NeMo Canary models across C, C++, and JavaScript APIs 
by adding new Canary configuration structures, updating bindings, extending examples,
and enhancing CI workflows.

- Add OfflineCanaryModelConfig to all language bindings (C, C++, JS, ETS).
- Implement SetConfig methods and NAPI wrappers for updating recognizer config at runtime.
- Update examples and CI scripts to demonstrate and test NeMo Canary model usage.

Unreal Engine has its own memory management, so we cannot return a struct containing a std::vector object.

Adds support for building and packaging Linux AArch64 (arm64) artifacts alongside x64 for Dart/Flutter plugins.

- Detects host architecture in CMake and adjusts library paths
- Extends test workflows to run on an ARM runner and handle linux-aarch64 paths
- Splits release pipeline into separate x64 and aarch64 build/package jobs

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

It was not able to handle short audios., e.g., 2.1 seconds.

It refactors the release scripts to centralize and simplify version updates across 
multiple files. Key changes include:

- Introducing variables (old_version, new_version, replace_str) for version substitution.
- Replacing hard-coded sed expressions with dynamic ones in various files.
- Ensuring backup files generated by sed are cleaned up after execution.

Adds support for Zipformer transducer ASR models that use Whisper-style 
features by introducing a new feature flag, parsing metadata, 
and integrating per-chunk normalization.

- Introduce UseWhisperFeature in the model interface and Zipformer implementation
- Parse "feature" metadata to set the whisper flag and wire it into the recognizer
- Update feature extraction logic to handle Whisper filterbanks with early returns

Replace the deprecated portaudio-go integration with malgo in the Go real-time 
speech recognition example and correct version string typos in the Node.js examples.

- Fixed “verison” typo in Node.js console logs.
- Swapped out portaudio-go for malgo in the Go microphone example, 
   introducing initRecognizer, callback-driven streaming, and sample conversion.
- Removed portaudio-go from go.mod.

See also https://github.com/mtkresearch/BreezeApp

- generate samples for https://k2-fsa.github.io/sherpa/onnx/tts/all/
- provide int8 model for kokoro v0.19 kokoro-int8-en-v0_19.tar.bz2

Handle the case when the input audio contains no speeches.

Fix linking with C++ examples.

Co-authored-by: gxy <gxy@conwi.cn>