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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 | # This config contains the default values for training 16kHz NeMo Audio Codec model
# If you want to train model on other dataset, you can change config values according to your dataset.
# Most dataset-specific arguments are in the head of the config file, see below.
name: AudioCodec
max_epochs: ???
max_steps: 200000
# Adjust batch size based on GPU memory
batch_size: 32
# When doing weighted sampling with multiple manifests, this defines how many training steps are in an epoch.
# If null, then weighted sampling is disabled.
weighted_sampling_steps_per_epoch: null
# Dataset metadata for each manifest
# https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/tts/data/vocoder_dataset.py#L39-L41
train_ds_meta: ???
val_ds_meta: ???
log_ds_meta: ???
log_dir: ???
# Modify these values based on your sample rate
sample_rate: 16000
train_n_samples: 16000
down_sample_rates: [2, 4, 5, 5]
up_sample_rates: [5, 5, 4, 2]
# The number of samples per encoded audio frame. Should be the product of the down_sample_rates.
# For example 2 * 4 * 5 * 5 = 200. => frame_rate = 16000/200 = 80
samples_per_frame: 200
model:
max_epochs: ${max_epochs}
steps_per_epoch: ${weighted_sampling_steps_per_epoch}
max_steps: ${max_steps}
sample_rate: ${sample_rate}
samples_per_frame: ${samples_per_frame}
mel_loss_l1_scale: 1.0
mel_loss_l2_scale: 1.0
stft_loss_scale: 0.0
time_domain_loss_scale: 0.1
# Probability of updating the discriminator during each training step
# For example, update the discriminator 2/3 times (2 updates for every 3 batches)
disc_updates_per_period: 2
disc_update_period: 3
# All resolutions for reconstruction loss, ordered [num_fft, hop_length, window_length]
loss_resolutions: [
[32, 8, 32], [64, 16, 64], [128, 32, 128], [256, 64, 256], [512, 128, 512], [1024, 256, 1024], [2048, 512, 2048]
]
mel_loss_dims: [64, 64, 64, 64, 64, 64, 64]
mel_loss_log_guard: 1E-5
stft_loss_log_guard: 1.0
train_ds:
dataset:
_target_: nemo.collections.tts.data.vocoder_dataset.VocoderDataset
weighted_sampling_steps_per_epoch: ${weighted_sampling_steps_per_epoch}
sample_rate: ${sample_rate}
n_samples: ${train_n_samples}
min_duration: 1.01
max_duration: null
dataset_meta: ${train_ds_meta}
dataloader_params:
batch_size: ${batch_size}
drop_last: true
num_workers: 4
validation_ds:
dataset:
_target_: nemo.collections.tts.data.vocoder_dataset.VocoderDataset
sample_rate: ${sample_rate}
n_samples: null
min_duration: null
max_duration: null
trunc_duration: 10.0 # Only use the first 10 seconds of audio for computing validation loss
dataset_meta: ${val_ds_meta}
dataloader_params:
batch_size: 8
num_workers: 2
# Configures how audio samples are generated and saved during training.
# Remove this section to disable logging.
log_config:
log_dir: ${log_dir}
log_epochs: [1, 2, 3, 4, 5, 6]
epoch_frequency: 1
log_tensorboard: false
log_wandb: false
generators:
- _target_: nemo.collections.tts.parts.utils.callbacks.AudioCodecArtifactGenerator
log_audio: true
log_encoding: false
log_dequantized: false
dataset:
_target_: nemo.collections.tts.data.vocoder_dataset.VocoderDataset
sample_rate: ${sample_rate}
n_samples: null
min_duration: null
max_duration: null
trunc_duration: 15.0 # Only log the first 15 seconds of generated audio.
dataset_meta: ${log_ds_meta}
dataloader_params:
batch_size: 4
num_workers: 2
audio_encoder:
_target_: nemo.collections.tts.modules.encodec_modules.SEANetEncoder
down_sample_rates: ${down_sample_rates}
audio_decoder:
_target_: nemo.collections.tts.modules.encodec_modules.SEANetDecoder
up_sample_rates: ${up_sample_rates}
vector_quantizer:
_target_: nemo.collections.tts.modules.encodec_modules.ResidualVectorQuantizer
num_codebooks: 8
discriminator:
_target_: nemo.collections.tts.modules.encodec_modules.MultiResolutionDiscriminatorSTFT
resolutions: [[128, 32, 128], [256, 64, 256], [512, 128, 512], [1024, 256, 1024], [2048, 512, 2048]]
generator_loss:
_target_: nemo.collections.tts.losses.audio_codec_loss.GeneratorSquaredLoss
discriminator_loss:
_target_: nemo.collections.tts.losses.audio_codec_loss.DiscriminatorSquaredLoss
optim:
_target_: torch.optim.AdamW
lr: 1e-4
betas: [0.8, 0.9]
sched:
name: StepLR
gamma: 0.999996
step_size: 1
# Parameters above are tuned based on 8 GPUs with bs 32 for librilight dataset, based on number of GPUs, those parameters need to be updated accordingly
trainer:
num_nodes: 1
devices: 1
accelerator: gpu
strategy: ddp_find_unused_parameters_true
precision: 32 # Vector quantization only works with 32-bit precision.
max_steps: ${max_steps}
max_epochs: ${max_epochs}
accumulate_grad_batches: 1
enable_checkpointing: False # Provided by exp_manager
logger: false # Provided by exp_manager
log_every_n_steps: 100
check_val_every_n_epoch: 1
benchmark: false
exp_manager:
exp_dir: null
name: ${name}
create_tensorboard_logger: true
create_checkpoint_callback: true
create_wandb_logger: false
checkpoint_callback_params:
monitor: val_loss
resume_if_exists: false
resume_ignore_no_checkpoint: false
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