107 lines
3.1 KiB
Python
Executable File
107 lines
3.1 KiB
Python
Executable File
#!/usr/bin/env python3
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# Real-time speech recognition from a microphone with sherpa-onnx Python API
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# with endpoint detection.
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# This script uses a streaming paraformer
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#
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# Please refer to
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# https://k2-fsa.github.io/sherpa/onnx/pretrained_models/online-paraformer/paraformer-models.html#
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# to download pre-trained models
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import sys
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from pathlib import Path
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try:
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import sounddevice as sd
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except ImportError:
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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 assert_file_exists(filename: str):
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assert Path(filename).is_file(), (
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f"{filename} does not exist!\n"
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"Please refer to "
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"https://k2-fsa.github.io/sherpa/onnx/pretrained_models/online-paraformer/paraformer-models.html to download it"
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)
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def create_recognizer():
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encoder = "./sherpa-onnx-streaming-paraformer-bilingual-zh-en/encoder.int8.onnx"
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decoder = "./sherpa-onnx-streaming-paraformer-bilingual-zh-en/decoder.int8.onnx"
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tokens = "./sherpa-onnx-streaming-paraformer-bilingual-zh-en/tokens.txt"
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assert_file_exists(encoder)
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assert_file_exists(decoder)
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assert_file_exists(tokens)
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recognizer = sherpa_onnx.OnlineRecognizer.from_paraformer(
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tokens=tokens,
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encoder=encoder,
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decoder=decoder,
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num_threads=1,
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sample_rate=16000,
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feature_dim=80,
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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, # it essentially disables this rule
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)
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return recognizer
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def main():
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devices = sd.query_devices()
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if len(devices) == 0:
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print("No microphone devices found")
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sys.exit(0)
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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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recognizer = create_recognizer()
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print("Started! Please speak")
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# The model is using 16 kHz, we use 48 kHz here to demonstrate that
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# sherpa-onnx will do resampling inside.
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sample_rate = 48000
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samples_per_read = int(0.1 * sample_rate) # 0.1 second = 100 ms
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stream = recognizer.create_stream()
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display = sherpa_onnx.Display()
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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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is_endpoint = recognizer.is_endpoint(stream)
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result = recognizer.get_result(stream)
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display.update_text(result)
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display.display()
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if is_endpoint:
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if result:
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display.finalize_current_sentence()
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display.display()
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recognizer.reset(stream)
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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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