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Loader.py
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Loader.py
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import os
from tqdm import tqdm
import numpy as np
import librosa
from Preprocessor import *
class Loader():
def __init__(self, sample_rate=32000):
self.class_id_mapping = {'Hi-hat': 0, 'Saxophone': 1, 'Trumpet': 2, 'Glockenspiel': 3, 'Cello': 4, 'Knock': 5, 'Gunshot_or_gunfire': 6, 'Clarinet': 7, 'Computer_keyboard': 8, 'Keys_jangling': 9, 'Snare_drum': 10, 'Writing': 11, 'Laughter': 12, 'Tearing': 13, 'Fart': 14, 'Oboe': 15, 'Flute': 16, 'Cough': 17, 'Telephone': 18, 'Bark': 19, 'Chime': 20, 'Bass_drum': 21,
'Bus': 22, 'Squeak': 23, 'Scissors': 24, 'Harmonica': 25, 'Gong': 26, 'Microwave_oven': 27, 'Burping_or_eructation': 28, 'Double_bass': 29, 'Shatter': 30, 'Fireworks': 31, 'Tambourine': 32, 'Cowbell': 33, 'Electric_piano': 34, 'Meow': 35, 'Drawer_open_or_close': 36, 'Applause': 37, 'Acoustic_guitar': 38, 'Violin_or_fiddle': 39, 'Finger_snapping': 40}
self.classes_frequency = {}
self.spec_statistic = {"max": None,
"min": None, "average": 0, "variance": 0, "len_hist": {}}
self.audio_statistics = {"max": None,
"min": None, "average": 0, "variance": 0, "len_hist": {}}
self.classes_verified = {}
self.files = []
self.labels = []
self.verified = []
self.spectrograms = []
self.audio_signals = []
self.train_csv = False
self.sample_rate = sample_rate
def load_files_labels(self, file_csv):
with open(file_csv, 'r') as fp:
file_list = fp.read()
print("load labels and files name from csv...")
file_list = file_list.split("\n")
if(file_list[0].split(",")[2].strip() == "manually_verified"):
self.train_csv = True
for line in file_list[1:]:
split_line = line.split(",")
if(split_line[0] == ''):
continue
file_name = split_line[0].strip()
file_label = split_line[1].strip()
if(file_label == "None"):
continue
if(self.train_csv):
file_verified = np.bool(split_line[2].strip() == '1')
else:
file_verified = np.bool(True)
self.verified.append(file_verified)
self.files.append(file_name)
self.labels.append(self.class_id_mapping[file_label])
self.classes_frequency[
file_label] = self.classes_frequency.setdefault(file_label, 0) + 1
if(file_verified):
self.classes_verified[
file_label] = self.classes_verified.setdefault(file_label, 0) + 1
print("finish loading csv")
return self.files, self.labels
def load_spectrogram(self, version):
if(len(self.files) == 0):
raise NameError("load the file name list before from csv file")
if(version == 1):
data_path = "./dataset/spec/ver1/"
elif(version == 2):
data_path = "./dataset/spec/ver2/"
preprocessor = Preprocessor(
spectrogram_path=data_path, version=version, test=False, dump=True)
if(self.train_csv):
audio_path = "./dataset/audio_train/"
else:
audio_path = "./dataset/audio_test/"
print("loading spectrograms...")
for file_name in tqdm(self.files):
spec_file_name = file_name.replace(".wav", ".npy")
spec_file_name = os.path.join(data_path, spec_file_name)
try:
spec = np.load(spec_file_name)
except FileNotFoundError:
print(
file_name, " spectrogram not exist, compute spectrogram from the original file")
audio_file_name = os.path.join(audio_path, file_name)
signal, sample_rate = librosa.load(
audio_file_name, sr=self.sample_rate, mono=True)
spec = preprocessor.compute_spectrogram(
signal, os.path.basename(spec_file_name))
self.spectrograms.append(spec)
# compute statistics
Xk = spec.shape[1]
if(self.spec_statistic["max"] == None or Xk > self.spec_statistic["max"]):
self.spec_statistic["max"] = Xk
if(self.spec_statistic["min"] == None or Xk < self.spec_statistic["min"]):
self.spec_statistic["min"] = Xk
k = len(self.spectrograms)
delta = (Xk - self.spec_statistic["average"]) / k
self.spec_statistic["average"] = self.spec_statistic[
"average"] + delta
self.spec_statistic["variance"] = ((
(k - 1) * self.spec_statistic["variance"]) / k) + (delta * (Xk - self.spec_statistic["average"]))
self.spec_statistic["len_hist"][Xk] = self.spec_statistic[
"len_hist"].setdefault(Xk, 0) + 1
return self.spectrograms, self.labels
def load_audio_signal(self):
if(len(self.files) == 0):
raise NameError("load the file name list before from csv file")
preprocessor = Preprocessor()
if(self.train_csv):
audio_path = "./dataset/audio_train/"
else:
audio_path = "./dataset/audio_test/"
print("loading audio signals...")
for file_name in tqdm(self.files):
audio_file_name = os.path.join(audio_path, file_name)
signal, sample_rate = librosa.load(
audio_file_name, sr=self.sample_rate, mono=True)
signal = preprocessor.normalize_and_trim_silence(signal)
self.audio_signals.append(signal)
# compute statistics
Xk = len(signal)
if(self.audio_statistics["max"] == None or Xk > self.audio_statistics["max"]):
self.audio_statistics["max"] = Xk
if(self.audio_statistics["min"] == None or Xk < self.audio_statistics["min"]):
self.audio_statistics["min"] = Xk
k = len(self.audio_signals)
delta = (Xk - self.audio_statistics["average"]) / k
self.audio_statistics["average"] = self.audio_statistics[
"average"] + delta
self.audio_statistics["variance"] = ((
(k - 1) * self.audio_statistics["variance"]) / k) + (delta * (Xk - self.audio_statistics["average"]))
self.audio_statistics["len_hist"][Xk] = self.audio_statistics[
"len_hist"].setdefault(Xk, 0) + 1
return self.audio_signals, self.labels
# with default parameters select all
def select_spectrogram(self, verified=False, classes=[]):
if(len(self.spectrograms) == 0):
raise NameError("load spectrograms list before")
subset_spec = []
subset_label = []
for index in range(len(self.spectrograms)):
if(verified == self.verified[index]):
if(classes == [] or self.labels[index] in classes):
subset_spec.append(self.spectrograms[index])
subset_label.appen(self.labels[index])
return subset_spec, subset_label
def select_audio_signal(self, verified=False, classes=[]):
if(len(self.audio_signals) == 0):
raise NameError("load audio signals list before")
subset_audio = []
subset_label = []
for index in range(len(self.audio_signals)):
if(verified == self.verified[index]):
if(classes == [] or self.labels[index] in classes):
subset_audio.append(self.audio_signals[index])
subset_label.append(self.labels[index])
return subset_audio, subset_label
def get_label_id_mapping(self):
return self.class_id_mapping
def get_label(self):
return self.class_id_mapping.keys()
def get_general_statistics(self):
tot = len(self.labels)
classes_percent = {}
classes_percent_verified = {}
for key in self.classes_frequency.keys():
classes_percent[key] = self.classes_frequency[key] / tot
classes_percent_verified[key] = self.classes_verified[
key] / self.classes_frequency[key]
verif_num = self.verified.count(True)
return self.classes_frequency, classes_percent, verif_num, verif_num / tot, self.classes_verified, classes_percent_verified
def get_audio_statistics(self):
return self.audio_statistics
def get_spectrogram_statistics(self):
return self.spec_statistic
# c as number
def get_audio_statistics_for_class(self, c):
class_dict = {"max": None, "min": None,
"average": 0, "variance": 0, "len_hist": {}}
for index in range(len(self.audio_signals)):
if(self.labels[index] == c):
Xk = len(self.audio_signals[index])
if(class_dict["max"] == None or Xk > class_dict["max"]):
class_dict["max"] = Xk
if(class_dict["min"] == None or Xk < class_dict["min"]):
class_dict["min"] = Xk
k = index
delta = (Xk - class_dict["average"]) / k
class_dict["average"] = class_dict[
"average"] + delta
class_dict["variance"] = ((
(k - 1) * class_dict["variance"]) / k) + (delta * (Xk - class_dict["average"]))
class_dict["len_hist"][Xk] = class_dict[
"len_hist"].setdefault(Xk, 0) + 1
return class_dict
def get_spec_statistics_for_class(self, c):
class_dict = {"max": None, "min": None,
"average": 0, "variance": 0, "len_hist": {}}
for index in range(len(self.spectrograms)):
if(self.labels[index] == c):
Xk = self.spectrograms[index].shape[1]
if(class_dict["max"] == None or Xk > class_dict["max"]):
class_dict["max"] = Xk
if(class_dict["min"] == None or Xk < class_dict["min"]):
class_dict["min"] = Xk
k = index
delta = (Xk - class_dict["average"]) / k
class_dict["average"] = class_dict[
"average"] + delta
class_dict["variance"] = ((
(k - 1) * class_dict["variance"]) / k) + (delta * (Xk - class_dict["average"]))
class_dict["len_hist"][Xk] = class_dict[
"len_hist"].setdefault(Xk, 0) + 1
return class_dict