checkpoints/voice_multi_b2048_r48000/config.gin

from __gin__ import dynamic_registration
import cached_conv as cc
from cached_conv import convs
import rave
from rave import blocks
from rave import core
from rave import dataset
from rave import descript_discriminator
from rave import discriminator
from rave import model
from rave import pqmf
import torch
import torch.nn as nn

# Macros:
# ==============================================================================
ACTIVATION = @blocks.Snake
CAPACITY = 96
DILATIONS = [[1, 3, 9], [1, 3, 9], [1, 3, 9], [1, 3]]
KERNEL_SIZE = 3
LATENT_SIZE = 128
N_BAND = 16
NOISE_AUGMENTATION = 0
PHASE_1_DURATION = 50000
RATIOS = [4, 4, 4, 2]
SAMPLING_RATE = 48000

# Parameters for blocks.AdaptiveInstanceNormalization:
# ==============================================================================
# None.

# Parameters for variational/blocks.AdaptiveInstanceNormalization:
# ==============================================================================
# None.

# Parameters for core.AudioDistanceV1:
# ==============================================================================
core.AudioDistanceV1.log_epsilon = 1e-07
core.AudioDistanceV1.multiscale_stft = @core.MultiScaleSTFT

# Parameters for model.BetaWarmupCallback:
# ==============================================================================
model.BetaWarmupCallback.initial_value = 1e-06
model.BetaWarmupCallback.log = True
model.BetaWarmupCallback.target_value = 0.002
model.BetaWarmupCallback.warmup_len = 20000

# Parameters for pqmf.CachedPQMF:
# ==============================================================================
pqmf.CachedPQMF.attenuation = 100
pqmf.CachedPQMF.n_band = %N_BAND

# Parameters for cc.Conv1d:
# ==============================================================================
cc.Conv1d.bias = False

# Parameters for variational/cc.Conv1d:
# ==============================================================================
variational/cc.Conv1d.bias = False

# Parameters for cc.ConvTranspose1d:
# ==============================================================================
cc.ConvTranspose1d.bias = False

# Parameters for descript_discriminator.DescriptDiscriminator:
# ==============================================================================
descript_discriminator.DescriptDiscriminator.bands = \
    [(0.0, 0.1), (0.1, 0.25), (0.25, 0.5), (0.5, 0.75), (0.75, 1.0)]
descript_discriminator.DescriptDiscriminator.fft_sizes = [2048, 1024, 512]
descript_discriminator.DescriptDiscriminator.periods = [2, 3, 5, 7, 11]
descript_discriminator.DescriptDiscriminator.rates = []
descript_discriminator.DescriptDiscriminator.sample_rate = 44100

# Parameters for variational/blocks.EncoderV2:
# ==============================================================================
variational/blocks.EncoderV2.activation = %ACTIVATION
variational/blocks.EncoderV2.adain = @blocks.AdaptiveInstanceNormalization
variational/blocks.EncoderV2.capacity = %CAPACITY
variational/blocks.EncoderV2.data_size = %N_BAND
variational/blocks.EncoderV2.dilations = %DILATIONS
variational/blocks.EncoderV2.keep_dim = False
variational/blocks.EncoderV2.kernel_size = %KERNEL_SIZE
variational/blocks.EncoderV2.latent_size = %LATENT_SIZE
variational/blocks.EncoderV2.n_out = 2
variational/blocks.EncoderV2.ratios = %RATIOS
variational/blocks.EncoderV2.recurrent_layer = None
variational/blocks.EncoderV2.spectrogram = None

# Parameters for blocks.GeneratorV2:
# ==============================================================================
blocks.GeneratorV2.activation = %ACTIVATION
blocks.GeneratorV2.adain = @blocks.AdaptiveInstanceNormalization
blocks.GeneratorV2.amplitude_modulation = True
blocks.GeneratorV2.capacity = %CAPACITY
blocks.GeneratorV2.causal_convtranspose = True
blocks.GeneratorV2.data_size = %N_BAND
blocks.GeneratorV2.dilations = %DILATIONS
blocks.GeneratorV2.keep_dim = False
blocks.GeneratorV2.kernel_size = %KERNEL_SIZE
blocks.GeneratorV2.latent_size = @core.get_augmented_latent_size()
blocks.GeneratorV2.noise_module = @blocks.NoiseGeneratorV2
blocks.GeneratorV2.ratios = %RATIOS
blocks.GeneratorV2.recurrent_layer = None

# Parameters for core.get_augmented_latent_size:
# ==============================================================================
core.get_augmented_latent_size.latent_size = %LATENT_SIZE
core.get_augmented_latent_size.noise_augmentation = %NOISE_AUGMENTATION

# Parameters for dataset.get_dataset:
# ==============================================================================
dataset.get_dataset.augmentations = []

# Parameters for convs.get_padding:
# ==============================================================================
convs.get_padding.dilation = 1
convs.get_padding.mode = 'causal'
convs.get_padding.stride = 1

# Parameters for variational/convs.get_padding:
# ==============================================================================
variational/convs.get_padding.dilation = 1
variational/convs.get_padding.mode = 'causal'
variational/convs.get_padding.stride = 1

# Parameters for core.MultiScaleSTFT:
# ==============================================================================
core.MultiScaleSTFT.magnitude = True
core.MultiScaleSTFT.normalized = False
core.MultiScaleSTFT.num_mels = None
core.MultiScaleSTFT.random_crop = False
core.MultiScaleSTFT.sample_rate = %SAMPLING_RATE
core.MultiScaleSTFT.scales = [2048, 1024, 512, 256, 128]

# Parameters for blocks.NoiseGeneratorV2:
# ==============================================================================
blocks.NoiseGeneratorV2.activation = %ACTIVATION
blocks.NoiseGeneratorV2.data_size = %N_BAND
blocks.NoiseGeneratorV2.hidden_size = 128
blocks.NoiseGeneratorV2.noise_bands = 5
blocks.NoiseGeneratorV2.ratios = [2, 2, 2]

# Parameters for blocks.normalization:
# ==============================================================================
blocks.normalization.mode = 'weight_norm'

# Parameters for variational/blocks.normalization:
# ==============================================================================
variational/blocks.normalization.mode = 'weight_norm'

# Parameters for model.RAVE:
# ==============================================================================
model.RAVE.audio_distance = @core.AudioDistanceV1
model.RAVE.audio_monitor_epochs = 1
model.RAVE.balancer = None
model.RAVE.decoder = @blocks.GeneratorV2
model.RAVE.discriminator = @descript_discriminator.DescriptDiscriminator
model.RAVE.enable_pqmf_decode = None
model.RAVE.enable_pqmf_encode = None
model.RAVE.encoder = @blocks.VariationalEncoder
model.RAVE.feature_matching_fun = @feature_matching/core.mean_difference
model.RAVE.freeze_encoder = True
model.RAVE.gan_loss = @core.hinge_gan
model.RAVE.input_mode = 'pqmf'
model.RAVE.is_mel_input = None
model.RAVE.latent_size = %LATENT_SIZE
model.RAVE.loss_weights = None
model.RAVE.multiband_audio_distance = @core.AudioDistanceV1
model.RAVE.n_bands = 16
model.RAVE.n_channels = 1
model.RAVE.num_skipped_features = 1
model.RAVE.output_mode = 'pqmf'
model.RAVE.phase_1_duration = %PHASE_1_DURATION
model.RAVE.pqmf = @pqmf.CachedPQMF
model.RAVE.sampling_rate = %SAMPLING_RATE
model.RAVE.spectrogram = None
model.RAVE.update_discriminator_every = 4
model.RAVE.valid_signal_crop = True
model.RAVE.warmup_quantize = None
model.RAVE.weights = {'feature_matching': 20}

# Parameters for blocks.Snake:
# ==============================================================================
# None.

# Parameters for variational/blocks.Snake:
# ==============================================================================
# None.

# Parameters for dataset.split_dataset:
# ==============================================================================
dataset.split_dataset.max_residual = 1000

# Parameters for blocks.VariationalEncoder:
# ==============================================================================
blocks.VariationalEncoder.beta = 1.0
blocks.VariationalEncoder.encoder = @variational/blocks.EncoderV2