# encode audio feature
logit = get_logit(x, voca_size=voca_size)
# ctc decoding
decoded, _ = tf.nn.ctc_beam_search_decoder(logit.sg_transpose(perm=[1, 0, 2]),
seq_len,
merge_repeated=False)
# to dense tensor
y = tf.sparse_to_dense(decoded[0].indices, decoded[0].dense_shape,
decoded[0].values) + 1
# regcognize audio file
# perintah untuk menginput path file audio
tf.sg_arg_def(file=('', 'speech wave file to recognize.'))
# load audio file
file = sys.argv[1]
wav, sr = librosa.load(file, mono=True, sr=16000)
# mendapatkan mfcc feature
mfcc = np.transpose(np.expand_dims(librosa.feature.mfcc(wav, 16000), axis=0),
[0, 2, 1])
# run network
with tf.Session() as sess:
# init variables
tf.sg_init(sess)
from data import SpeechCorpus, voca_size
from model import *
import numpy as np
from tqdm import tqdm
import pandas as pd
import os
from shutil import copyfile
train_path = './asset/train/'
best_model = './best_model/'
# set log level to debug
tf.sg_verbosity(10)
# command line argument for set_name
tf.sg_arg_def(set=('test', "'train', 'valid', or 'test'. The default is 'valid'"))
tf.sg_arg_def(frac=(1.0, "test fraction ratio to whole data set. The default is 1.0(=whole set)"))
#
# hyper parameters
#
# batch size
batch_size = 16
#
# inputs
#
# corpus input tensor ( with QueueRunner )
import sugartensor as tf
from data import SpeechCorpus, voca_size
from model import *
import numpy as np
from tqdm import tqdm
__author__ = '*****@*****.**'
# set log level to debug
tf.sg_verbosity(10)
# command line argument for set_name
tf.sg_arg_def(set=('valid', "'train', 'valid', or 'test'. The default is 'valid'"))
tf.sg_arg_def(frac=(1.0, "test fraction ratio to whole data set. The default is 1.0(=whole set)"))
#
# hyper parameters
#
# batch size
batch_size = 16
#
# inputs
#
# corpus input tensor ( with QueueRunner )
data = SpeechCorpus(batch_size=batch_size, set_name=tf.sg_arg().set)
