74 lines
2.2 KiB
Python
74 lines
2.2 KiB
Python
#!/usr/bin/env python3
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"""
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This file demonstrates how to use sherpa-onnx Python API to recognize
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a single file.
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Please refer to
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https://k2-fsa.github.io/sherpa/onnx/index.html
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to install sherpa-onnx and to download the pre-trained models
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used in this file.
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"""
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import wave
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import time
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import numpy as np
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import sherpa_onnx
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def main():
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sample_rate = 16000
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num_threads = 4
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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=num_threads,
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sample_rate=sample_rate,
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feature_dim=80,
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)
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filename = "./sherpa-onnx-lstm-en-2023-02-17/test_wavs/1089-134686-0001.wav"
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with wave.open(filename) as f:
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assert f.getframerate() == sample_rate, f.getframerate()
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assert f.getnchannels() == 1, f.getnchannels()
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assert f.getsampwidth() == 2, f.getsampwidth() # it is in bytes
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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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duration = len(samples_float32) / sample_rate
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start_time = time.time()
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print("Started!")
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stream = recognizer.create_stream()
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stream.accept_waveform(sample_rate, samples_float32)
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tail_paddings = np.zeros(int(0.2 * sample_rate), dtype=np.float32)
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stream.accept_waveform(sample_rate, tail_paddings)
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stream.input_finished()
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while recognizer.is_ready(stream):
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recognizer.decode_stream(stream)
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print(recognizer.get_result(stream))
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print("Done!")
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end_time = time.time()
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elapsed_seconds = end_time - start_time
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rtf = elapsed_seconds / duration
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print(f"num_threads: {num_threads}")
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print(f"Wave duration: {duration:.3f} s")
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print(f"Elapsed time: {elapsed_seconds:.3f} s")
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print(f"Real time factor (RTF): {elapsed_seconds:.3f}/{duration:.3f} = {rtf:.3f}")
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if __name__ == "__main__":
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main()
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