Add Java and Kotlin API for NeMo Canary models (#2359)

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

kotlin-api-examples/run.sh

@@ -455,8 +455,31 @@ function testOfflineSenseVoiceWithHr() {
   ls -lh $out_filename
   java -Djava.library.path=../build/lib -jar $out_filename
 }
-testVersion
 
+function testOfflineNeMoCanary() {
+  if [ ! -f sherpa-onnx-nemo-canary-180m-flash-en-es-de-fr-int8/encoder.int8.onnx ]; then
+    curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-nemo-canary-180m-flash-en-es-de-fr-int8.tar.bz2
+    tar xvf sherpa-onnx-nemo-canary-180m-flash-en-es-de-fr-int8.tar.bz2
+    rm sherpa-onnx-nemo-canary-180m-flash-en-es-de-fr-int8.tar.bz2
+  fi
+
+  out_filename=test_offline_nemo_canary.jar
+  kotlinc-jvm -include-runtime -d $out_filename \
+    test_offline_nemo_canary.kt \
+    FeatureConfig.kt \
+    HomophoneReplacerConfig.kt \
+    OfflineRecognizer.kt \
+    OfflineStream.kt \
+    WaveReader.kt \
+    faked-asset-manager.kt
+
+  ls -lh $out_filename
+  java -Djava.library.path=../build/lib -jar $out_filename
+}
+
+# testVersion
+
+testOfflineNeMoCanary
 testOfflineSenseVoiceWithHr
 testOfflineSpeechDenoiser
 testOfflineSpeakerDiarization

kotlin-api-examples/test_offline_nemo_canary.kt

@@ -0,0 +1,48 @@
+package com.k2fsa.sherpa.onnx
+
+fun main() {
+  val recognizer = createOfflineRecognizer()
+  val waveFilename = "./sherpa-onnx-nemo-canary-180m-flash-en-es-de-fr-int8/test_wavs/en.wav"
+
+  val objArray = WaveReader.readWaveFromFile(
+      filename = waveFilename,
+  )
+  val samples: FloatArray = objArray[0] as FloatArray
+  val sampleRate: Int = objArray[1] as Int
+
+  var stream = recognizer.createStream()
+  stream.acceptWaveform(samples, sampleRate=sampleRate)
+  recognizer.decode(stream)
+
+  var result = recognizer.getResult(stream)
+  println("English: $result")
+
+  stream.release()
+
+  // now output text in German
+  val config = recognizer.config.copy(modelConfig=recognizer.config.modelConfig.copy(
+    canary=recognizer.config.modelConfig.canary.copy(
+      tgtLang="de"
+    )
+  ))
+  recognizer.setConfig(config)
+
+  stream = recognizer.createStream()
+  stream.acceptWaveform(samples, sampleRate=sampleRate)
+  recognizer.decode(stream)
+
+  result = recognizer.getResult(stream)
+  println("German: $result")
+
+  stream.release()
+  recognizer.release()
+}
+
+
+fun createOfflineRecognizer(): OfflineRecognizer {
+  val config = OfflineRecognizerConfig(
+      modelConfig = getOfflineModelConfig(type = 32)!!,
+  )
+
+  return OfflineRecognizer(config = config)
+}