Add Python APIs for WeNet CTC models (#428)

sherpa-onnx/python/tests/test_online_recognizer.py

@@ -143,6 +143,64 @@ class TestOnlineRecognizer(unittest.TestCase):
                     print(f"{wave_filename}\n{result}")
                     print("-" * 10)
 
+    def test_wenet_ctc(self):
+        models = [
+            "sherpa-onnx-zh-wenet-aishell",
+            "sherpa-onnx-zh-wenet-aishell2",
+            "sherpa-onnx-zh-wenet-wenetspeech",
+            "sherpa-onnx-zh-wenet-multi-cn",
+            "sherpa-onnx-en-wenet-librispeech",
+            "sherpa-onnx-en-wenet-gigaspeech",
+        ]
+        for m in models:
+            for use_int8 in [True, False]:
+                name = (
+                    "model-streaming.int8.onnx" if use_int8 else "model-streaming.onnx"
+                )
+                model = f"{d}/{m}/{name}"
+                tokens = f"{d}/{m}/tokens.txt"
+
+                wave0 = f"{d}/{m}/test_wavs/0.wav"
+                wave1 = f"{d}/{m}/test_wavs/1.wav"
+                wave2 = f"{d}/{m}/test_wavs/8k.wav"
+
+                if not Path(model).is_file():
+                    print("skipping test_wenet_ctc()")
+                    return
+
+                recognizer = sherpa_onnx.OnlineRecognizer.from_wenet_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)
+
 
 if __name__ == "__main__":
     unittest.main()