if self.mode == "valid":
for i in record['species_id']:
label[i] = 1
return {
"image" : y,
"target" : label,
"id" : record['recording_id']
}
###############################
#Augmentations
###############################
import audiomentations as AA
train_audio_transform = AA.Compose([
AA.AddGaussianNoise(p=0.5),
AA.AddGaussianSNR(p=0.5),
#AA.AddBackgroundNoise("../input/train_audio/", p=1)
#AA.AddImpulseResponse(p=0.1),
#AA.AddShortNoises("../input/train_audio/", p=1)
#AA.FrequencyMask(min_frequency_band=0.0, max_frequency_band=0.2, p=0.1),
#AA.TimeMask(min_band_part=0.0, max_band_part=0.2, p=0.1),
#AA.PitchShift(min_semitones=-0.5, max_semitones=0.5, p=0.1),
#AA.Shift(p=0.1),
#AA.Normalize(p=0.1),
#AA.ClippingDistortion(min_percentile_threshold=0, max_percentile_threshold=1, p=0.05),
#AA.PolarityInversion(p=0.05),
#AA.Gain(p=0.2)
])
import audiomentations
import cv2
from model.mixers import UseMixerWithProb, RandomMixer, SigmoidConcatMixer, AddMixer, SigmoidVerticalConcatMixer
from model.random_resized_crop import RandomResizedCrop, RandomResizedCrop2
from model.transforms import Compose, UseWithProb, SpecAugment, SpectreScale, PreprocessMelImage, GaussNoise, OneOf, \
PadToSize, RandomCrop, PreprocessSingleChannelMelImage
wave_augmentations = {
0: None,
1: audiomentations.Compose([
audiomentations.AddGaussianNoise(min_amplitude=0.001, max_amplitude=0.015, p=0.5),
audiomentations.TimeStretch(min_rate=0.8, max_rate=1.25, p=0.5),
audiomentations.PitchShift(min_semitones=-4, max_semitones=4, p=0.5),
audiomentations.Shift(min_fraction=-0.5, max_fraction=0.5, p=0.5),
], p=0.96), # (1-(1-0.5)^4)*0.96==0.9 - In total there will be 90% augmented samples
2: audiomentations.Compose([
audiomentations.AddGaussianNoise(min_amplitude=0.001, max_amplitude=0.010, p=0.95),
audiomentations.Shift(min_fraction=-0.1, max_fraction=0.1, p=0.3),
], p=1),
}
size_4_sec_750_hop = 256
_base_mel_post_process = {
'none': [],
'3ch_1': [
# Use librosa.feature.delta with order 1 and 2 for creating 2 additional channels then divide by 100
PreprocessMelImage(),
],
'1ch_1': [PreprocessSingleChannelMelImage(), ],
kfold = StratifiedKFold(n_splits=CFG.n_fold)
for fold, (t_idx, v_idx) in enumerate(kfold.split(X, y)):
train_gby.loc[v_idx, "kfold"] = fold
train_df = train_df.merge(train_gby[["recording_id", "kfold"]],
on="recording_id",
how="left")
print(train_df.kfold.value_counts())
train_df.to_csv(OUTPUT_DIR / "folds.csv", index=False)
species_fmin_fmax.to_csv(OUTPUT_DIR / "species_fmin_fmax.csv", index=False)
################################################
# audiomentations #
################################################
augmenter = A.Compose([
A.AddGaussianNoise(min_amplitude=0.01, max_amplitude=0.03, p=0.2),
A.PitchShift(min_semitones=-3, max_semitones=3, p=0.2),
A.Gain(p=0.2)
])
################################################
# Dataset #
################################################
def cut_spect(spect: torch.Tensor, fmin_mel: int, fmax_mel: int):
return spect[fmin_mel:fmax_mel]
def do_normalize(img: torch.Tensor):
bs, ch, w, h = img.shape
import audiomentations as A
augmenter = A.Compose([
A.AddGaussianNoise(p=0.4),
A.AddGaussianSNR(p=0.4),
#A.AddBackgroundNoise("../input/train_audio/", p=1)
#A.AddImpulseResponse(p=0.1),
#A.AddShortNoises("../input/train_audio/", p=1)
#A.FrequencyMask(min_frequency_band=0.0, max_frequency_band=0.2, p=0.05),
#A.TimeMask(min_band_part=0.0, max_band_part=0.2, p=0.05),
#A.PitchShift(min_semitones=-0.5, max_semitones=0.5, p=0.05),
#A.Shift(p=0.1),
#A.Normalize(p=0.1),
#A.ClippingDistortion(min_percentile_threshold=0, max_percentile_threshold=1, p=0.05),
#A.PolarityInversion(p=0.05),
A.Gain(p=0.2)
])