File size: 4,639 Bytes
a7c2243 | 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 | name: predictive_conformer
model:
type: predictive
sample_rate: 16000
skip_nan_grad: false
num_outputs: 1
# non-streaming config, use input normalization
normalize_input: true # normalize the input signal to 0dBFS
train_ds:
shar_path: ???
use_lhotse: true
truncate_duration: 4.09 # Number of STFT time frames = 1 + audio_duration // encoder.hop_length = 512
truncate_offset_type: random
batch_size: 64 # batch size may be increased based on the available memory
shuffle: true
num_workers: 8
pin_memory: true
validation_ds:
manifest_filepath: ???
input_key: noisy_filepath
target_key: clean_filepath
batch_size: 8
shuffle: false
num_workers: 4
pin_memory: true
encoder:
_target_: nemo.collections.audio.modules.transforms.AudioToSpectrogram
fft_length: 510 # Number of subbands in the STFT = fft_length // 2 + 1 = 256
hop_length: 128
magnitude_power: 0.5
scale: 0.33
decoder:
_target_: nemo.collections.audio.modules.transforms.SpectrogramToAudio
fft_length: ${model.encoder.fft_length}
hop_length: ${model.encoder.hop_length}
magnitude_power: ${model.encoder.magnitude_power}
scale: ${model.encoder.scale}
estimator:
_target_: nemo.collections.audio.parts.submodules.conformer.SpectrogramConformer
in_channels: 1 # single-channel noisy input
out_channels: 1 # single-channel estimate
feat_in: 256 # input feature dimension = number of subbands
n_layers: 8 # number of layers in the model
d_model: 512 # the hidden size of the model
subsampling_factor: 1 # subsampling factor for the model
self_attention_model: 'rel_pos'
n_heads: 8 # number of heads for the model
# streaming-related arguments
# - this is a non-streaming config
conv_context_size: null
conv_norm_type: 'layer_norm'
causal_downsampling: False
att_context_size: [-1, -1]
att_context_style: 'regular'
loss:
_target_: nemo.collections.audio.losses.audio.MSELoss # computed in the time domain
metrics:
val:
sisdr: # output SI-SDR
_target_: torchmetrics.audio.ScaleInvariantSignalDistortionRatio
optim:
name: adamw
lr: 1e-3
# optimizer arguments
betas: [0.9, 0.98]
weight_decay: 1e-3
# scheduler setup
sched:
name: CosineAnnealing
# scheduler config override
warmup_steps: null
warmup_ratio: 0.1
min_lr: 1e-5
trainer:
devices: -1 # number of GPUs, -1 would use all available GPUs
num_nodes: 1
max_epochs: -1
max_steps: ??? # needs to be set for shar datasets
limit_train_batches: ??? # number of batches to train on in each pseudo-epoch
val_check_interval: ??? # run validation after this many training steps
accelerator: auto
strategy: ddp
use_distributed_sampler: false # required for lhotse
accumulate_grad_batches: 1
gradient_clip_val: null
precision: 32 # Should be set to 16 for O1 and O2 to enable the AMP.
log_every_n_steps: 100 # Interval of logging.
enable_progress_bar: true
num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it
check_val_every_n_epoch: null # number of evaluations on validation every n epochs
sync_batchnorm: true
enable_checkpointing: false # Provided by exp_manager
logger: false # Provided by exp_manager
exp_manager:
exp_dir: null
name: ${name}
# logging
create_tensorboard_logger: true
# checkpointing
create_checkpoint_callback: true
checkpoint_callback_params:
# in case of multiple validation sets, first one is used
monitor: val_sisdr
mode: max
save_top_k: 5
always_save_nemo: true # saves the checkpoints as nemo files instead of PTL checkpoints
# early stopping
create_early_stopping_callback: true
early_stopping_callback_params:
monitor: val_sisdr
mode: max
min_delta: 0.0
patience: 20 # patience in terms of check_val_every_n_epoch
verbose: true
strict: false # Should be False to avoid a runtime error where EarlyStopping says monitor is unavailable, which sometimes happens with resumed training.
resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc.
# you need to set these two to true to continue the training
resume_if_exists: false
resume_ignore_no_checkpoint: false
# You may use this section to create a W&B logger
create_wandb_logger: false
wandb_logger_kwargs:
name: null
project: null
|