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76 lines
2.6 KiB
Python
76 lines
2.6 KiB
Python
#!/usr/bin/env python3
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"""
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This file shows how to use a non-streaming FireRedAsr AED model from
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https://github.com/FireRedTeam/FireRedASR
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to decode files.
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Please download model files from
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https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models
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For instance,
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wget https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-fire-red-asr-large-zh_en-2025-02-16.tar.bz2
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tar xvf sherpa-onnx-fire-red-asr-large-zh_en-2025-02-16.tar.bz2
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rm sherpa-onnx-fire-red-asr-large-zh_en-2025-02-16.tar.bz2
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"""
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from pathlib import Path
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import sherpa_onnx
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import soundfile as sf
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def create_recognizer():
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encoder = "./sherpa-onnx-fire-red-asr-large-zh_en-2025-02-16/encoder.int8.onnx"
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decoder = "./sherpa-onnx-fire-red-asr-large-zh_en-2025-02-16/decoder.int8.onnx"
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tokens = "./sherpa-onnx-fire-red-asr-large-zh_en-2025-02-16/tokens.txt"
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test_wav = "./sherpa-onnx-fire-red-asr-large-zh_en-2025-02-16/test_wavs/0.wav"
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# test_wav = "./sherpa-onnx-fire-red-asr-large-zh_en-2025-02-16/test_wavs/1.wav"
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# test_wav = "./sherpa-onnx-fire-red-asr-large-zh_en-2025-02-16/test_wavs/2.wav"
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# test_wav = "./sherpa-onnx-fire-red-asr-large-zh_en-2025-02-16/test_wavs/3.wav"
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# test_wav = "./sherpa-onnx-fire-red-asr-large-zh_en-2025-02-16/test_wavs/8k.wav"
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# test_wav = "./sherpa-onnx-fire-red-asr-large-zh_en-2025-02-16/test_wavs/3-sichuan.wav"
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# test_wav = "./sherpa-onnx-fire-red-asr-large-zh_en-2025-02-16/test_wavs/4-tianjin.wav"
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# test_wav = "./sherpa-onnx-fire-red-asr-large-zh_en-2025-02-16/test_wavs/5-henan.wav"
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if (
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not Path(encoder).is_file()
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or not Path(decoder).is_file()
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or not Path(test_wav).is_file()
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):
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raise ValueError(
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"""Please download model files from
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https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models
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"""
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)
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return (
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sherpa_onnx.OfflineRecognizer.from_fire_red_asr(
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encoder=encoder,
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decoder=decoder,
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tokens=tokens,
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debug=True,
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),
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test_wav,
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)
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def main():
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recognizer, wave_filename = create_recognizer()
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audio, sample_rate = sf.read(wave_filename, dtype="float32", always_2d=True)
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audio = audio[:, 0] # only use the first channel
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# audio is a 1-D float32 numpy array normalized to the range [-1, 1]
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# sample_rate does not need to be 16000 Hz
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stream = recognizer.create_stream()
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stream.accept_waveform(sample_rate, audio)
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recognizer.decode_stream(stream)
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print(wave_filename)
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print(stream.result)
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if __name__ == "__main__":
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main()
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