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add global init for multi band melgan to avoid large output in the begin

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pull/951/head
TianYuan 4 years ago
parent 9125d71a81
commit d3d9f83594
4 changed files with 19 additions and 148 deletions
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4
examples/csmsc/voc3/conf/default.yaml
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@ -35,7 +35,7 @@ generator_params:
stacks: 4 # Number of stacks in a single residual stack module.
use_weight_norm: True # Whether to use weight normalization.
use_causal_conv: False # Whether to use causal convolution.
use_final_nonlinear_activation: False # If True, spectral_convergence_loss and sub_spectral_convergence_loss will be too large (eg.30)
use_final_nonlinear_activation: True
###########################################################
@ -129,7 +129,7 @@ discriminator_scheduler_params:
###########################################################
discriminator_train_start_steps: 200000 # Number of steps to start to train discriminator.
train_max_steps: 1000000 # Number of training steps.
save_interval_steps: 50000 # Interval steps to save checkpoint.
save_interval_steps: 5000 # Interval steps to save checkpoint.
eval_interval_steps: 1000 # Interval steps to evaluate the network.
###########################################################

139
examples/csmsc/voc3/conf/use_tanh.yaml
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@ -1,139 +0,0 @@
# This is the hyperparameter configuration file for MelGAN.
# Please make sure this is adjusted for the CSMSC dataset. If you want to
# apply to the other dataset, you might need to carefully change some parameters.
# This configuration requires ~ 8GB memory and will finish within 7 days on Titan V.
# This configuration is based on full-band MelGAN but the hop size and sampling
# rate is different from the paper (16kHz vs 24kHz). The number of iteraions
# is not shown in the paper so currently we train 1M iterations (not sure enough
# to converge). The optimizer setting is based on @dathudeptrai advice.
# https://github.com/kan-bayashi/ParallelWaveGAN/issues/143#issuecomment-632539906
###########################################################
# FEATURE EXTRACTION SETTING #
###########################################################
fs: 24000 # Sampling rate.
n_fft: 2048 # FFT size. (in samples)
n_shift: 300 # Hop size. (in samples)
win_length: 1200 # Window length. (in samples)
# If set to null, it will be the same as fft_size.
window: "hann" # Window function.
n_mels: 80 # Number of mel basis.
fmin: 80 # Minimum freq in mel basis calculation. (Hz)
fmax: 7600 # Maximum frequency in mel basis calculation. (Hz)
###########################################################
# GENERATOR NETWORK ARCHITECTURE SETTING #
###########################################################
generator_params:
in_channels: 80 # Number of input channels.
out_channels: 4 # Number of output channels.
kernel_size: 7 # Kernel size of initial and final conv layers.
channels: 384 # Initial number of channels for conv layers.
upsample_scales: [5, 5, 3] # List of Upsampling scales.
stack_kernel_size: 3 # Kernel size of dilated conv layers in residual stack.
stacks: 4 # Number of stacks in a single residual stack module.
use_weight_norm: True # Whether to use weight normalization.
use_causal_conv: False # Whether to use causal convolution.
use_final_nonlinear_activation: True # If True, spectral_convergence_loss and sub_spectral_convergence_loss will be too large (eg.30)
###########################################################
# DISCRIMINATOR NETWORK ARCHITECTURE SETTING #
###########################################################
discriminator_params:
in_channels: 1 # Number of input channels.
out_channels: 1 # Number of output channels.
scales: 3 # Number of multi-scales.
downsample_pooling: "AvgPool1D" # Pooling type for the input downsampling.
downsample_pooling_params: # Parameters of the above pooling function.
kernel_size: 4
stride: 2
padding: 1
exclusive: True
kernel_sizes: [5, 3] # List of kernel size.
channels: 16 # Number of channels of the initial conv layer.
max_downsample_channels: 512 # Maximum number of channels of downsampling layers.
downsample_scales: [4, 4, 4] # List of downsampling scales.
nonlinear_activation: "LeakyReLU" # Nonlinear activation function.
nonlinear_activation_params: # Parameters of nonlinear activation function.
negative_slope: 0.2
use_weight_norm: True # Whether to use weight norm.
###########################################################
# STFT LOSS SETTING #
###########################################################
use_stft_loss: true
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 function for STFT-based loss
use_subband_stft_loss: true
subband_stft_loss_params:
fft_sizes: [384, 683, 171] # List of FFT size for STFT-based loss.
hop_sizes: [30, 60, 10] # List of hop size for STFT-based loss
win_lengths: [150, 300, 60] # List of window length for STFT-based loss.
window: "hann" # Window function for STFT-based loss
###########################################################
# ADVERSARIAL LOSS SETTING #
###########################################################
use_feat_match_loss: false # Whether to use feature matching loss.
lambda_adv: 2.5 # Loss balancing coefficient for adversarial loss.
###########################################################
# DATA LOADER SETTING #
###########################################################
batch_size: 64 # Batch size.
batch_max_steps: 16200 # Length of each audio in batch. Make sure dividable by hop_size.
num_workers: 2 # Number of workers in DataLoader.
###########################################################
# OPTIMIZER & SCHEDULER SETTING #
###########################################################
generator_optimizer_params:
epsilon: 1.0e-7 # Generator's epsilon.
weight_decay: 0.0 # Generator's weight decay coefficient.
generator_grad_norm: -1 # Generator's gradient norm.
generator_scheduler_params:
learning_rate: 1.0e-3 # Generator's learning rate.
gamma: 0.5 # Generator's scheduler gamma.
milestones: # At each milestone, lr will be multiplied by gamma.
- 100000
- 200000
- 300000
- 400000
- 500000
- 600000
discriminator_optimizer_params:
epsilon: 1.0e-7 # Discriminator's epsilon.
weight_decay: 0.0 # Discriminator's weight decay coefficient.
discriminator_grad_norm: -1 # Discriminator's gradient norm.
discriminator_scheduler_params:
learning_rate: 1.0e-3 # Discriminator's learning rate.
gamma: 0.5 # Discriminator's scheduler gamma.
milestones: # At each milestone, lr will be multiplied by gamma.
- 100000
- 200000
- 300000
- 400000
- 500000
- 600000
###########################################################
# INTERVAL SETTING #
###########################################################
discriminator_train_start_steps: 200000 # Number of steps to start to train discriminator.
train_max_steps: 1000000 # Number of training steps.
save_interval_steps: 50000 # Interval steps to save checkpoint.
eval_interval_steps: 1000 # Interval steps to evaluate the network.
###########################################################
# OTHER SETTING #
###########################################################
num_snapshots: 10 # max number of snapshots to keep while training
seed: 42 # random seed for paddle, random, and np.random

19
parakeet/models/melgan/melgan.py
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@ -22,6 +22,7 @@ from paddle import nn
from parakeet.modules.causal_conv import CausalConv1D
from parakeet.modules.causal_conv import CausalConv1DTranspose
from parakeet.modules.nets_utils import initialize
from parakeet.modules.pqmf import PQMF
from parakeet.modules.residual_stack import ResidualStack
@ -45,7 +46,8 @@ class MelGANGenerator(nn.Layer):
pad_params: Dict[str, Any]={"mode": "reflect"},
use_final_nonlinear_activation: bool=True,
use_weight_norm: bool=True,
use_causal_conv: bool=False, ):
use_causal_conv: bool=False,
init_type: str="xavier_uniform", ):
"""Initialize MelGANGenerator module.
Parameters
----------
@ -91,7 +93,10 @@ class MelGANGenerator(nn.Layer):
if not use_causal_conv:
assert (kernel_size - 1
) % 2 == 0, "Not support even number kernel size."
# add initial layer
# initialize parameters
initialize(self, init_type)
layers = []
if not use_causal_conv:
layers += [
@ -178,6 +183,7 @@ class MelGANGenerator(nn.Layer):
# define the model as a single function
self.melgan = nn.Sequential(*layers)
nn.initializer.set_global_initializer(None)
# apply weight norm
if use_weight_norm:
@ -322,6 +328,7 @@ class MelGANDiscriminator(nn.Layer):
assert len(kernel_sizes) == 2
assert kernel_sizes[0] % 2 == 1
assert kernel_sizes[1] % 2 == 1
# add first layer
self.layers.append(
nn.Sequential(
@ -417,7 +424,8 @@ class MelGANMultiScaleDiscriminator(nn.Layer):
nonlinear_activation_params: Dict[str, Any]={"negative_slope": 0.2},
pad: str="Pad1D",
pad_params: Dict[str, Any]={"mode": "reflect"},
use_weight_norm: bool=True, ):
use_weight_norm: bool=True,
init_type: str="xavier_uniform", ):
"""Initilize MelGAN multi-scale discriminator module.
Parameters
----------
@ -454,6 +462,9 @@ class MelGANMultiScaleDiscriminator(nn.Layer):
Whether to use causal convolution.
"""
super().__init__()
# initialize parameters
initialize(self, init_type)
self.discriminators = nn.LayerList()
# add discriminators
@ -474,6 +485,8 @@ class MelGANMultiScaleDiscriminator(nn.Layer):
self.pooling = getattr(nn, downsample_pooling)(
**downsample_pooling_params)
nn.initializer.set_global_initializer(None)
# apply weight norm
if use_weight_norm:
self.apply_weight_norm()

5
parakeet/modules/residual_stack.py
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@ -106,7 +106,4 @@ class ResidualStack(nn.Layer):
Tensor
Output tensor (B, chennels, T).
"""
stack_output = self.stack(c)
skip_layer_output = self.skip_layer(c)
out = stack_output + skip_layer_output
return out
return self.stack(c) + self.skip_layer(c)

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