Support streaming zipformer CTC (#496)

* Support streaming zipformer CTC

* test online zipformer2 CTC

* Update doc of sherpa-onnx.cc

* Add Python APIs for streaming zipformer2 ctc

* Add Python API examples for streaming zipformer2 ctc

* Swift API for streaming zipformer2 CTC

* NodeJS API for streaming zipformer2 CTC

* Kotlin API for streaming zipformer2 CTC

* Golang API for streaming zipformer2 CTC

* C# API for streaming zipformer2 CTC

* Release v1.9.6

sherpa-onnx/python/tests/test_online_recognizer.py

@@ -143,6 +143,57 @@ class TestOnlineRecognizer(unittest.TestCase):
                     print(f"{wave_filename}\n{result}")
                     print("-" * 10)
 
+    def test_zipformer2_ctc(self):
+        m = "sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13"
+        for use_int8 in [True, False]:
+            name = (
+                "ctc-epoch-20-avg-1-chunk-16-left-128.int8.onnx"
+                if use_int8
+                else "ctc-epoch-20-avg-1-chunk-16-left-128.onnx"
+            )
+            model = f"{d}/{m}/{name}"
+            tokens = f"{d}/{m}/tokens.txt"
+            wave0 = f"{d}/{m}/test_wavs/DEV_T0000000000.wav"
+            wave1 = f"{d}/{m}/test_wavs/DEV_T0000000001.wav"
+            wave2 = f"{d}/{m}/test_wavs/DEV_T0000000002.wav"
+            if not Path(model).is_file():
+                print("skipping test_zipformer2_ctc()")
+                return
+            print(f"testing {model}")
+
+            recognizer = sherpa_onnx.OnlineRecognizer.from_zipformer2_ctc(
+                model=model,
+                tokens=tokens,
+                num_threads=1,
+                provider="cpu",
+            )
+
+            streams = []
+            waves = [wave0, wave1, wave2]
+            for wave in waves:
+                s = recognizer.create_stream()
+                samples, sample_rate = read_wave(wave)
+                s.accept_waveform(sample_rate, samples)
+
+                tail_paddings = np.zeros(int(0.2 * sample_rate), dtype=np.float32)
+                s.accept_waveform(sample_rate, tail_paddings)
+                s.input_finished()
+                streams.append(s)
+
+            while True:
+                ready_list = []
+                for s in streams:
+                    if recognizer.is_ready(s):
+                        ready_list.append(s)
+                if len(ready_list) == 0:
+                    break
+                recognizer.decode_streams(ready_list)
+
+            results = [recognizer.get_result(s) for s in streams]
+            for wave_filename, result in zip(waves, results):
+                print(f"{wave_filename}\n{result}")
+                print("-" * 10)
+
     def test_wenet_ctc(self):
         models = [
             "sherpa-onnx-zh-wenet-aishell",