has_test_audio: true   # set this to False if you don't have access to the test audio (i.e. training and validation only)
has_test_labels: true   # set this to False if you don't have access to the test labels

wav_crop_len: 5     # Length of cropped files in seconds
data_path_base: ../data/production_data
n_classes: 66       # Number of classes
pretrained: true    # Use pretrained model
backbone: tf_efficientnetv2_s.in21k     # image classification model (from list_models)
in_chans: 1
num_workers: 4      # Number of parallelized CPUs
include_val: true   # Validation-set included / excluded
max_amp: false      # Experimental feature


# Training Hyperparameters
n_epochs: 18                 # Number of epochs
lr: 0.0017                   # Learning rate
weight_decay: 1.0e-05        # Weight decay
label_smoothing: 0.1         # Label smoothing
batch_size: 32               # Batch size
sample_rate: 44100           # Sample rate

# Mel Spectrogram Hyperparameters
# see Torchaudio Documentation to understand these
n_mels: 128
n_fft: 2048
fmin: 400
fmax: 22000
power: 2
top_db: 80.0

win_length: 2048
hop_length: 1024

# Normalization
mel_normalized: true         # Mel normalization as documented in Torchaudio (normalized=True)
minmax_norm: false           # Apply minmax normalization on spectrograms

# Augmentation Parameters
impulse_prob: 0.15           # Impulse probability
noise_prob: 0.15             # Noise probability

max_noise: 0.04              # Noiseinjection amplitude
min_snr: 5                   # signal-noise ratio (Gaussian & Pink Noise)
max_snr: 20

mixup: false                 # Apply mixup augmentation
specaug: false               # Apply OneOf(MaskFrequency, MaskTime)
specaug_prob: 0.25           # Probability to apply spectrogram augmentation
mixup_prob: 1                # Parameter of a symmetric Beta distribution, 1=uniform distribution