Iliass Lasri
add examples cong
7af5906
training:
run_name: example_config
epochs: 150
learning_rate: 0.0001
log_interval: 100
checkpoint_dir: null
resume_from: null
n_iterative_pseudolabeling: 3
lr_scheduler:
_target_: torch.optim.lr_scheduler.CosineAnnealingLR
T_max: ${training.epochs}
eta_min: 1.0e-06
lr_scheduler_start_epoch: -1
dataset:
root: data/LibriSpeech
train_split: train-clean-100
test_split: test-clean
batch_size: 32
num_workers: 1
noise_dir: noise_fullband
max_audio_length: 160000
augmentations:
max_augs: 4 # in all our experiments we used 4
time_stretch: true
pitch_shift: true
reverberation: true
noise: true
rir_dir: data/rirs
activate_extra_augs: true
echo:
enabled: true
volume_range:
- 0.1
- 0.5
duration_range:
- 0.1
- 0.5
random_noise:
enabled: true
noise_std: 0.001
pink_noise:
enabled: true
noise_std: 0.01
lowpass_filter:
enabled: true
cutoff_freq: 5000
highpass_filter:
enabled: true
cutoff_freq: 500
bandpass_filter:
enabled: true
cutoff_freq_low: 300
cutoff_freq_high: 8000
smooth:
enabled: true
window_size_range:
- 2
- 10
boost_audio:
enabled: true
amount: 20
duck_audio:
enabled: true
amount: 20
updownresample:
enabled: true
intermediate_freq: 32000
model:
name: hubert-base-ls960
layer: 6
vocab_size: 500
kind_kmeans: kmeans
quantizer:
hidden_dim: 256