-
-
Notifications
You must be signed in to change notification settings - Fork 343
/
parallel_wavegan.v1.long.yaml
123 lines (114 loc) · 7.03 KB
/
parallel_wavegan.v1.long.yaml
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
# This is the hyperparameter configuration file for Parallel WaveGAN.
# Please make sure this is adjusted for the LJSpeech dataset. If you want to
# apply to the other dataset, you might need to carefully change some parameters.
# This configuration trains more steps up to 1000k compared to v1 config.
# It requires 12 GB GPU memory and takes ~7 days on TITAN V.
###########################################################
# FEATURE EXTRACTION SETTING #
###########################################################
sampling_rate: 22050 # Sampling rate.
fft_size: 1024 # FFT size.
hop_size: 256 # Hop size.
win_length: null # Window length.
# If set to null, it will be the same as fft_size.
window: "hann" # Window function.
num_mels: 80 # Number of mel basis.
fmin: 80 # Minimum freq in mel basis calculation.
fmax: 7600 # Maximum frequency in mel basis calculation.
global_gain_scale: 1.0 # Will be multiplied to all of waveform.
trim_silence: true # Whether to trim the start and end of silence.
trim_threshold_in_db: 60 # Need to tune carefully if the recording is not good.
trim_frame_size: 2048 # Frame size in trimming.
trim_hop_size: 512 # Hop size in trimming.
format: "hdf5" # Feature file format. "npy" or "hdf5" is supported.
###########################################################
# GENERATOR NETWORK ARCHITECTURE SETTING #
###########################################################
generator_params:
in_channels: 1 # Number of input channels.
out_channels: 1 # Number of output channels.
kernel_size: 3 # Kernel size of dilated convolution.
layers: 30 # Number of residual block layers.
stacks: 3 # Number of stacks i.e., dilation cycles.
residual_channels: 64 # Number of channels in residual conv.
gate_channels: 128 # Number of channels in gated conv.
skip_channels: 64 # Number of channels in skip conv.
aux_channels: 80 # Number of channels for auxiliary feature conv.
# Must be the same as num_mels.
aux_context_window: 2 # Context window size for auxiliary feature.
# If set to 2, previous 2 and future 2 frames will be considered.
dropout: 0.0 # Dropout rate. 0.0 means no dropout applied.
use_weight_norm: true # Whether to use weight norm.
# If set to true, it will be applied to all of the conv layers.
upsample_net: "ConvInUpsampleNetwork" # Upsampling network architecture.
upsample_params: # Upsampling network parameters.
upsample_scales: [4, 4, 4, 4] # Upsampling scales. Prodcut of these must be the same as hop size.
###########################################################
# DISCRIMINATOR NETWORK ARCHITECTURE SETTING #
###########################################################
discriminator_params:
in_channels: 1 # Number of input channels.
out_channels: 1 # Number of output channels.
kernel_size: 3 # Number of output channels.
layers: 10 # Number of conv layers.
conv_channels: 64 # Number of chnn layers.
bias: true # Whether to use bias parameter in conv.
use_weight_norm: true # Whether to use weight norm.
# If set to true, it will be applied to all of the conv layers.
nonlinear_activation: "LeakyReLU" # Nonlinear function after each conv.
nonlinear_activation_params: # Nonlinear function parameters
negative_slope: 0.2 # Alpha in LeakyReLU.
###########################################################
# STFT LOSS SETTING #
###########################################################
stft_loss_params:
fft_sizes: [1024, 2048, 512] # List of FFT size for STFT-based loss.
hop_sizes: [120, 240, 50] # List of hop size for STFT-based loss
win_lengths: [600, 1200, 240] # List of window length for STFT-based loss.
window: "hann_window" # Window function for STFT-based loss
###########################################################
# ADVERSARIAL LOSS SETTING #
###########################################################
lambda_adv: 4.0 # Loss balancing coefficient.
###########################################################
# DATA LOADER SETTING #
###########################################################
batch_size: 6 # Batch size.
batch_max_steps: 25600 # Length of each audio in batch. Make sure dividable by hop_size.
pin_memory: true # Whether to pin memory in Pytorch DataLoader.
num_workers: 2 # Number of workers in Pytorch DataLoader.
remove_short_samples: true # Whether to remove samples the length of which are less than batch_max_steps.
allow_cache: true # Whether to allow cache in dataset. If true, it requires cpu memory.
###########################################################
# OPTIMIZER & SCHEDULER SETTING #
###########################################################
generator_optimizer_params:
lr: 0.0001 # Generator's learning rate.
eps: 1.0e-6 # Generator's epsilon.
weight_decay: 0.0 # Generator's weight decay coefficient.
generator_scheduler_params:
step_size: 200000 # Generator's scheduler step size.
gamma: 0.5 # Generator's scheduler gamma.
# At each step size, lr will be multiplied by this parameter.
generator_grad_norm: 10 # Generator's gradient norm.
discriminator_optimizer_params:
lr: 0.00005 # Discriminator's learning rate.
eps: 1.0e-6 # Discriminator's epsilon.
weight_decay: 0.0 # Discriminator's weight decay coefficient.
discriminator_scheduler_params:
step_size: 200000 # Discriminator's scheduler step size.
gamma: 0.5 # Discriminator's scheduler gamma.
# At each step size, lr will be multiplied by this parameter.
discriminator_grad_norm: 1 # Discriminator's gradient norm.
###########################################################
# INTERVAL SETTING #
###########################################################
discriminator_train_start_steps: 100000 # Number of steps to start to train discriminator.
train_max_steps: 1000000 # Number of training steps.
save_interval_steps: 5000 # Interval steps to save checkpoint.
eval_interval_steps: 1000 # Interval steps to evaluate the network.
log_interval_steps: 100 # Interval steps to record the training log.
###########################################################
# OTHER SETTING #
###########################################################
num_save_intermediate_results: 4 # Number of results to be saved as intermediate results.