| from signal import signal | |
| import tensorflow as tf | |
| gpus = tf.config.list_physical_devices('GPU') | |
| tf.config.set_visible_devices(gpus[0:1], 'GPU') | |
| from vocab.vocab import Vocab | |
| import librosa | |
| import numpy as np | |
| import sys | |
| import os | |
| from tqdm import tqdm | |
| from sklearn.metrics import accuracy_score | |
| vocab = Vocab("vocab/vocab.txt") | |
| model = tf.saved_model.load('saved_models/lang14/pb/2/') | |
| def predict_wav(wav_path): | |
| signal, _ = librosa.load(wav_path, sr=16000) | |
| output, prob = model.predict_pb(signal) | |
| language = vocab.token_list[output.numpy()] | |
| print(language, prob.numpy()*100) | |
| return output.numpy(), prob.numpy() | |
| if __name__ == '__main__': | |
| wav_path = sys.argv[1] | |
| predict_wav(wav_path) | |