Add various language bindings for streaming T-one Russian ASR models (#2576)

This PR adds support for streaming T-one Russian ASR models across various language bindings in the sherpa-onnx library. The changes enable T-one CTC (Connectionist Temporal Classification) model integration by adding new configuration structures and example implementations.

- Introduces OnlineToneCtcModelConfig structures across all language bindings (C, C++, Swift, Java, Kotlin, Go, etc.)
- Adds T-one CTC model support to WASM implementations for both ASR and keyword spotting
- Provides comprehensive example implementations demonstrating T-one model usage in multiple programming languages

java-api-examples/StreamingDecodeFileToneCtc.java

@@ -0,0 +1,53 @@
+// Copyright 2024 Xiaomi Corporation
+
+// This file shows how to use an online T-one CTC model, i.e.,
+// streaming T-one CTC model, to decode files.
+import com.k2fsa.sherpa.onnx.*;
+
+public class StreamingDecodeFileToneCtc {
+  public static void main(String[] args) {
+    String model = "./sherpa-onnx-streaming-t-one-russian-2025-09-08/model.onnx";
+    String tokens = "./sherpa-onnx-streaming-t-one-russian-2025-09-08/tokens.txt";
+    String waveFilename = "./sherpa-onnx-streaming-t-one-russian-2025-09-08/0.wav";
+
+    WaveReader reader = new WaveReader(waveFilename);
+
+    OnlineToneCtcModelConfig ctc = OnlineToneCtcModelConfig.builder().setModel(model).build();
+
+    OnlineModelConfig modelConfig =
+        OnlineModelConfig.builder()
+            .setToneCtc(ctc)
+            .setTokens(tokens)
+            .setNumThreads(1)
+            .setDebug(true)
+            .build();
+
+    OnlineRecognizerConfig config =
+        OnlineRecognizerConfig.builder()
+            .setOnlineModelConfig(modelConfig)
+            .setDecodingMethod("greedy_search")
+            .build();
+
+    OnlineRecognizer recognizer = new OnlineRecognizer(config);
+    OnlineStream stream = recognizer.createStream();
+
+    float[] leftPaddings = new float[(int) (0.3 * reader.getSampleRate())];
+    stream.acceptWaveform(leftPaddings, reader.getSampleRate());
+
+    stream.acceptWaveform(reader.getSamples(), reader.getSampleRate());
+
+    float[] tailPaddings = new float[(int) (0.6 * reader.getSampleRate())];
+    stream.acceptWaveform(tailPaddings, reader.getSampleRate());
+
+    while (recognizer.isReady(stream)) {
+      recognizer.decode(stream);
+    }
+
+    String text = recognizer.getResult(stream).getText();
+
+    System.out.printf("filename:%s\nresult:%s\n", waveFilename, text);
+
+    stream.release();
+    recognizer.release();
+  }
+}