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+#!/usr/bin/env python3
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+
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+# Real-time speech recognition from a microphone with sherpa-ncnn Python API
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+# with endpoint detection.
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+#
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+# Note: This script uses ALSA and works only on Linux systems, especially
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+# for embedding Linux systems and for running Linux on Windows using WSL.
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+#
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+# Please refer to
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+# https://k2-fsa.github.io/sherpa/ncnn/pretrained_models/index.html
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+# to download pre-trained models
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+
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+import argparse
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+import sys
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+
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+import sherpa_ncnn
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+
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+
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+def get_args():
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+ parser = argparse.ArgumentParser(
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+ formatter_class=argparse.ArgumentDefaultsHelpFormatter
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+ )
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+
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+ parser.add_argument(
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+ "--device-name",
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+ type=str,
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+ required=True,
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+ help="""
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+The device name specifies which microphone to use in case there are several
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+on your system. You can use
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+
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+ arecord -l
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+
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+to find all available microphones on your computer. For instance, if it outputs
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+
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+**** List of CAPTURE Hardware Devices ****
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+card 3: UACDemoV10 [UACDemoV1.0], device 0: USB Audio [USB Audio]
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+ Subdevices: 1/1
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+ Subdevice #0: subdevice #0
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+
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+and if you want to select card 3 and the device 0 on that card, please use:
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+
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+ plughw:3,0
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+
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+as the device_name.
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+ """,
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+ )
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+
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+ return parser.parse_args()
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+
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+
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+def create_recognizer():
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+ # Please replace the model files if needed.
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+ # See https://k2-fsa.github.io/sherpa/ncnn/pretrained_models/index.html
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+ # for download links.
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+ recognizer = sherpa_ncnn.Recognizer(
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+ tokens="./sherpa-ncnn-conv-emformer-transducer-2022-12-06/tokens.txt",
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+ encoder_param="./sherpa-ncnn-conv-emformer-transducer-2022-12-06/encoder_jit_trace-pnnx.ncnn.param",
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+ encoder_bin="./sherpa-ncnn-conv-emformer-transducer-2022-12-06/encoder_jit_trace-pnnx.ncnn.bin",
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+ decoder_param="./sherpa-ncnn-conv-emformer-transducer-2022-12-06/decoder_jit_trace-pnnx.ncnn.param",
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+ decoder_bin="./sherpa-ncnn-conv-emformer-transducer-2022-12-06/decoder_jit_trace-pnnx.ncnn.bin",
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+ joiner_param="./sherpa-ncnn-conv-emformer-transducer-2022-12-06/joiner_jit_trace-pnnx.ncnn.param",
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+ joiner_bin="./sherpa-ncnn-conv-emformer-transducer-2022-12-06/joiner_jit_trace-pnnx.ncnn.bin",
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+ num_threads=4,
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+ decoding_method="modified_beam_search",
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+ enable_endpoint_detection=True,
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+ rule1_min_trailing_silence=2.4,
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+ rule2_min_trailing_silence=1.2,
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+ rule3_min_utterance_length=300,
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+ hotwords_file="",
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+ hotwords_score=1.5,
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+ )
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+ return recognizer
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+
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+
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+def main():
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+ args = get_args()
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+ device_name = args.device_name
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+ print(f"device_name: {device_name}")
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+ alsa = sherpa_ncnn.Alsa(device_name)
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+
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+ recognizer = create_recognizer()
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+ print("Started! Please speak")
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+ sample_rate = recognizer.sample_rate
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+ samples_per_read = int(0.1 * sample_rate) # 0.1 second = 100 ms
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+ last_result = ""
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+ segment_id = 0
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+
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+ while True:
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+ samples = alsa.read(samples_per_read) # a blocking read
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+ recognizer.accept_waveform(sample_rate, samples)
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+
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+ is_endpoint = recognizer.is_endpoint
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+
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+ result = recognizer.text
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+ if result and (last_result != result):
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+ last_result = result
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+ print("\r{}:{}".format(segment_id, result), end="", flush=True)
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+
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+ if is_endpoint:
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+ if result:
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+ print("\r{}:{}".format(segment_id, result), flush=True)
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+ segment_id += 1
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+ recognizer.reset()
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+
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+
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+if __name__ == "__main__":
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+ try:
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+ main()
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+ except KeyboardInterrupt:
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+ print("\nCaught Ctrl + C. Exiting")
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Fangjun Kuang