def __init__(self, save_dir, model_name, step=10, window=20, max_freq=8000, desc_file=None):
"""
Params:
step (int): Step size in milliseconds between windows
window (int): FFT window size in milliseconds
max_freq (int): Only FFT bins corresponding to frequencies between
[0, max_freq] are returned
desc_file (str, optional): Path to a JSON-line file that contains
labels and paths to the audio files. If this is None, then
load metadata right away
"""
#calc_feat_dim returns int(0.001*window*max_freq)+1
super(DataGenerator, self).__init__()
# feat_dim=0.001*20*8000+1=161
self.feat_dim = calc_feat_dim(window, max_freq)
# 1d 161 length of array filled with zeros
self.feats_mean = np.zeros((self.feat_dim,))
# 1d 161 length of array filled with 1s
self.feats_std = np.ones((self.feat_dim,))
self.max_input_length = 0
self.max_length_list_in_batch =[]
# 1d 161 length of array filled with random value
#[0.0, 1.0)
self.rng = random.Random()
if desc_file is not None:
self.load_metadata_from_desc_file(desc_file)
self.step = step
self.window = window
self.max_freq = max_freq
self.save_dir = save_dir
self.model_name = model_name
def __init__(self, save_dir, model_name, step=10, window=20, max_freq=8000, desc_file=None):
"""
Params:
step (int): Step size in milliseconds between windows
window (int): FFT window size in milliseconds
max_freq (int): Only FFT bins corresponding to frequencies between
[0, max_freq] are returned
desc_file (str, optional): Path to a JSON-line file that contains
labels and paths to the audio files. If this is None, then
load metadata right away
"""
#calc_feat_dim returns int(0.001*window*max_freq)+1
super(DataGenerator, self).__init__()
# feat_dim=0.001*20*8000+1=161
self.feat_dim = calc_feat_dim(window, max_freq)
# 1d 161 length of array filled with zeros
self.feats_mean = np.zeros((self.feat_dim,))
# 1d 161 length of array filled with 1s
self.feats_std = np.ones((self.feat_dim,))
self.max_input_length = 0
self.max_length_list_in_batch = []
# 1d 161 length of array filled with random value
#[0.0, 1.0)
self.rng = random.Random()
if desc_file is not None:
self.load_metadata_from_desc_file(desc_file)
self.step = step
self.window = window
self.max_freq = max_freq
self.save_dir = save_dir
self.model_name = model_name