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