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import wave |
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from pathlib import Path |
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from typing import Tuple |
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import sys |
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import numpy as np |
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import sherpa_onnx |
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def read_wave(wave_filename: str) -> Tuple[np.ndarray, int]: |
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with wave.open(wave_filename) as f: |
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assert f.getnchannels() == 1, f.getnchannels() |
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assert f.getsampwidth() == 2, f.getsampwidth() |
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num_samples = f.getnframes() |
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samples = f.readframes(num_samples) |
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samples_int16 = np.frombuffer(samples, dtype=np.int16) |
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samples_float32 = samples_int16.astype(np.float32) |
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samples_float32 = samples_float32 / 32768 |
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return samples_float32, f.getframerate() |
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def main(): |
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recognizer = sherpa_onnx.OfflineRecognizer.from_transducer( |
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encoder="am/encoder.onnx", |
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decoder="am/decoder.onnx", |
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joiner="am/joiner.onnx", |
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tokens="lang/tokens.txt", |
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num_threads=4, |
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sample_rate=16000, |
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decoding_method="greedy_search") |
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samples, sample_rate = read_wave("test.wav") |
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s = recognizer.create_stream() |
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s.accept_waveform(sample_rate, samples) |
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recognizer.decode_stream(s) |
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print (s.result.text) |
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if __name__ == "__main__": |
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main() |
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