70 lines
2.1 KiB
Python
70 lines
2.1 KiB
Python
#!/usr/bin/env python3
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# Real-time speech recognition from a microphone with sherpa-onnx Python API
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#
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# Please refer to
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# https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html
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# to download pre-trained models
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import sys
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try:
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import sounddevice as sd
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except ImportError as e:
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print("Please install sounddevice first. You can use")
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print()
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print(" pip install sounddevice")
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print()
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print("to install it")
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sys.exit(-1)
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import sherpa_onnx
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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/onnx/pretrained_models/index.html
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# for download links.
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recognizer = sherpa_onnx.OnlineRecognizer(
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tokens="./sherpa-onnx-lstm-en-2023-02-17/tokens.txt",
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encoder="./sherpa-onnx-lstm-en-2023-02-17/encoder-epoch-99-avg-1.onnx",
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decoder="./sherpa-onnx-lstm-en-2023-02-17/decoder-epoch-99-avg-1.onnx",
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joiner="./sherpa-onnx-lstm-en-2023-02-17/joiner-epoch-99-avg-1.onnx",
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num_threads=4,
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sample_rate=16000,
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feature_dim=80,
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)
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return recognizer
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def main():
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print("Started! Please speak")
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recognizer = create_recognizer()
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sample_rate = 16000
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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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stream = recognizer.create_stream()
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with sd.InputStream(channels=1, dtype="float32", samplerate=sample_rate) as s:
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while True:
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samples, _ = s.read(samples_per_read) # a blocking read
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samples = samples.reshape(-1)
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stream.accept_waveform(sample_rate, samples)
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while recognizer.is_ready(stream):
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recognizer.decode_stream(stream)
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result = recognizer.get_result(stream)
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if last_result != result:
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last_result = result
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print(result)
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if __name__ == "__main__":
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devices = sd.query_devices()
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print(devices)
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default_input_device_idx = sd.default.device[0]
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print(f'Use default device: {devices[default_input_device_idx]["name"]}')
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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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