File size: 6,115 Bytes
f2e7e64 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 |
name: &name "QuartzNet15x5"
model:
sample_rate: &sample_rate 16000
repeat: &repeat 5
dropout: &dropout 0.0
separable: &separable true
labels: &labels [" ", "'", "-", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "à", "á", "è", "é", "ì", "í", "ò", "ó", "ù", "ú", "ċ", "ġ", "ħ", "ż"]
train_ds:
manifest_filepath: ???
sample_rate: 16000
labels: *labels
batch_size: 16
trim_silence: True
max_duration: 16.7
shuffle: True
num_workers: 8
pin_memory: true
# tarred datasets
is_tarred: false
tarred_audio_filepaths: null
shuffle_n: 2048
# bucketing params
bucketing_strategy: "synced_randomized"
bucketing_batch_size: null
validation_ds:
manifest_filepath: ???
sample_rate: 16000
labels: *labels
batch_size: 16
shuffle: False
num_workers: 8
pin_memory: true
preprocessor:
_target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
normalize: "per_feature"
window_size: 0.02
sample_rate: *sample_rate
window_stride: 0.01
window: "hann"
features: &n_mels 64
n_fft: 512
frame_splicing: 1
dither: 1.0e-05
spec_augment:
_target_: nemo.collections.asr.modules.SpectrogramAugmentation
rect_freq: 50
rect_masks: 5
rect_time: 120
encoder:
_target_: nemo.collections.asr.modules.ConvASREncoder
feat_in: *n_mels
activation: relu
conv_mask: true
jasper:
#1
- dilation: [1]
dropout: *dropout
filters: 256
kernel: [33]
repeat: 1
residual: false
separable: *separable
stride: [2]
#2
- dilation: [1]
dropout: *dropout
filters: 256
kernel: [33]
repeat: *repeat
residual: true
separable: *separable
stride: [1]
#3
- dilation: [1]
dropout: *dropout
filters: 256
kernel: [33]
repeat: *repeat
residual: true
separable: *separable
stride: [1]
#4
- dilation: [1]
dropout: *dropout
filters: 256
kernel: [33]
repeat: *repeat
residual: true
separable: *separable
stride: [1]
#5
- dilation: [1]
dropout: *dropout
filters: 256
kernel: [39]
repeat: *repeat
residual: true
separable: *separable
stride: [1]
#6
- dilation: [1]
dropout: *dropout
filters: 256
kernel: [39]
repeat: *repeat
residual: true
separable: *separable
stride: [1]
#7
- dilation: [1]
dropout: *dropout
filters: 256
kernel: [39]
repeat: *repeat
residual: true
separable: *separable
stride: [1]
#8
- dilation: [1]
dropout: *dropout
filters: 512
kernel: [51]
repeat: *repeat
residual: true
separable: *separable
stride: [1]
#9
- dilation: [1]
dropout: *dropout
filters: 512
kernel: [51]
repeat: *repeat
residual: true
separable: *separable
stride: [1]
#10
- dilation: [1]
dropout: *dropout
filters: 512
kernel: [51]
repeat: *repeat
residual: true
separable: *separable
stride: [1]
#11
- dilation: [1]
dropout: *dropout
filters: 512
kernel: [63]
repeat: *repeat
residual: true
separable: *separable
stride: [1]
#12
- dilation: [1]
dropout: *dropout
filters: 512
kernel: [63]
repeat: *repeat
residual: true
separable: *separable
stride: [1]
#13
- dilation: [1]
dropout: *dropout
filters: 512
kernel: [63]
repeat: *repeat
residual: true
separable: *separable
stride: [1]
#14
- dilation: [1]
dropout: *dropout
filters: 512
kernel: [75]
repeat: *repeat
residual: true
separable: *separable
stride: [1]
#15
- dilation: [1]
dropout: *dropout
filters: 512
kernel: [75]
repeat: *repeat
residual: true
separable: *separable
stride: [1]
#16
- dilation: [1]
dropout: *dropout
filters: 512
kernel: [75]
repeat: *repeat
residual: true
separable: *separable
stride: [1]
#17
- dilation: [2]
dropout: *dropout
filters: 512
kernel: [87]
repeat: 1
residual: false
separable: *separable
stride: [1]
#18
- dilation: [1]
dropout: *dropout
filters: &enc_filters 1024
kernel: [1]
repeat: 1
residual: false
stride: [1]
decoder:
_target_: nemo.collections.asr.modules.ConvASRDecoder
feat_in: *enc_filters
num_classes: 43
vocabulary: *labels
optim:
name: novograd
# _target_: nemo.core.optim.optimizers.Novograd
lr: 0.0012
# optimizer arguments
betas: [0.95, 0.25]
weight_decay: 0.001
# scheduler setup
sched:
name: CosineAnnealing
# pytorch lightning args
# monitor: val_loss
# reduce_on_plateau: false
# Scheduler params
warmup_steps: null
warmup_ratio: null
min_lr: 0.0
last_epoch: -1
trainer:
devices: 1 # number of gpus
max_epochs: 5
max_steps: -1 # computed at runtime if not set
num_nodes: 1
accelerator: gpu
strategy: ddp
accumulate_grad_batches: 1
enable_checkpointing: False # Provided by exp_manager
logger: False # Provided by exp_manager
log_every_n_steps: 1 # Interval of logging.
val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations
benchmark: false # needs to be false for models with variable-length speech input as it slows down training
exp_manager:
exp_dir: null
name: *name
create_tensorboard_logger: True
create_checkpoint_callback: True
checkpoint_callback_params:
monitor: "val_wer"
mode: "min"
create_wandb_logger: False
wandb_logger_kwargs:
name: null
project: null
|