Support non-streaming zipformer CTC ASR models (#2340)

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

swift-api-examples/zipformer-ctc-asr.swift

@@ -0,0 +1,66 @@
+import AVFoundation
+
+extension AudioBuffer {
+  func array() -> [Float] {
+    return Array(UnsafeBufferPointer(self))
+  }
+}
+
+extension AVAudioPCMBuffer {
+  func array() -> [Float] {
+    return self.audioBufferList.pointee.mBuffers.array()
+  }
+}
+
+func run() {
+  let model = "./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/model.int8.onnx"
+  let tokens = "./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/tokens.txt"
+
+  let zipformerCtc = sherpaOnnxOfflineZipformerCtcModelConfig(
+    model: model
+  )
+
+  let modelConfig = sherpaOnnxOfflineModelConfig(
+    tokens: tokens,
+    debug: 0,
+    zipformerCtc: zipformerCtc
+  )
+
+  let featConfig = sherpaOnnxFeatureConfig(
+    sampleRate: 16000,
+    featureDim: 80
+  )
+  var config = sherpaOnnxOfflineRecognizerConfig(
+    featConfig: featConfig,
+    modelConfig: modelConfig
+  )
+
+  let recognizer = SherpaOnnxOfflineRecognizer(config: &config)
+
+  let filePath = "./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/test_wavs/0.wav"
+  let fileURL: NSURL = NSURL(fileURLWithPath: filePath)
+  let audioFile = try! AVAudioFile(forReading: fileURL as URL)
+
+  let audioFormat = audioFile.processingFormat
+  assert(audioFormat.channelCount == 1)
+  assert(audioFormat.commonFormat == AVAudioCommonFormat.pcmFormatFloat32)
+
+  let audioFrameCount = UInt32(audioFile.length)
+  let audioFileBuffer = AVAudioPCMBuffer(pcmFormat: audioFormat, frameCapacity: audioFrameCount)
+
+  try! audioFile.read(into: audioFileBuffer!)
+  let array: [Float]! = audioFileBuffer?.array()
+  let result = recognizer.decode(samples: array, sampleRate: Int(audioFormat.sampleRate))
+  print("\nresult is:\n\(result.text)")
+  if result.timestamps.count != 0 {
+    print("\ntimestamps is:\n\(result.timestamps)")
+  }
+
+}
+
+@main
+struct App {
+  static func main() {
+    run()
+  }
+}