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from dataclasses import dataclass, field
from typing import List, Literal, Tuple, Union
PreprocessLangType = Literal["english_only", "multilingual"]
@dataclass
class STFTConfig:
filter_length: int
hop_length: int
win_length: int
n_mel_channels: int
mel_fmin: int
mel_fmax: int
# Base class used with the Univnet vocoder
@dataclass
class PreprocessingConfig:
language: PreprocessLangType
stft: STFTConfig
sampling_rate: int = 22050
min_seconds: float = 0.5
max_seconds: float = 6.0
use_audio_normalization: bool = True
workers: int = 8
@dataclass
class PreprocessingConfigUnivNet(PreprocessingConfig):
stft: STFTConfig = field(
default_factory=lambda: STFTConfig(
filter_length=1024,
hop_length=256,
win_length=1024,
n_mel_channels=100, # univnet
mel_fmin=20,
mel_fmax=11025,
),
)
@dataclass
class PreprocessingConfigHifiGAN(PreprocessingConfig):
stft: STFTConfig = field(
default_factory=lambda: STFTConfig(
filter_length=1024,
hop_length=256,
win_length=1024,
n_mel_channels=80, # For univnet 100
mel_fmin=20,
mel_fmax=11025,
),
)
def __post_init__(self):
r"""It modifies the 'stft' attribute based on the 'sampling_rate' attribute.
If 'sampling_rate' is 44100, 'stft' is set with specific values for this rate.
If 'sampling_rate' is not 22050 or 44100, a ValueError is raised.
Raises:
ValueError: If 'sampling_rate' is not 22050 or 44100.
"""
if self.sampling_rate == 44100:
self.stft = STFTConfig(
filter_length=2048,
hop_length=512, # NOTE: 441 ?? https://github.com/jik876/hifi-gan/issues/116#issuecomment-1436999858
win_length=2048,
n_mel_channels=80, # Based on https://github.com/jik876/hifi-gan/issues/116
mel_fmin=20,
mel_fmax=11025,
)
if self.sampling_rate not in [22050, 44100]:
raise ValueError("Sampling rate must be 22050 or 44100")
@dataclass
class AcousticTrainingOptimizerConfig:
learning_rate: float
weight_decay: float
lr_decay: float
betas: Tuple[float, float] = (0.9, 0.98)
eps: float = 0.000000001
grad_clip_thresh: float = 1.0
warm_up_step: float = 4000
anneal_steps: List[int] = field(default_factory=list)
anneal_rate: float = 0.3
@dataclass
class AcousticFinetuningConfig:
batch_size = 5
grad_acc_step = 3
train_steps = 30000
log_step = 100
synth_step = 250
val_step = 4000
save_step = 250
freeze_bert_until = 0
mcd_gen_max_samples = 400
only_train_speaker_until = 5000
optimizer_config: AcousticTrainingOptimizerConfig = field(
default_factory=lambda: AcousticTrainingOptimizerConfig(
learning_rate=0.0002,
weight_decay=0.001,
lr_decay=0.99999,
),
)
@dataclass
class AcousticPretrainingConfig:
batch_size = 5
grad_acc_step = 5
train_steps = 500000
log_step = 20
synth_step = 250
val_step = 4000
save_step = 1000
freeze_bert_until = 4000
mcd_gen_max_samples = 400
only_train_speaker_until = 0
optimizer_config: AcousticTrainingOptimizerConfig = field(
default_factory=lambda: AcousticTrainingOptimizerConfig(
learning_rate=0.0002,
weight_decay=0.01,
lr_decay=1.0,
),
)
AcousticTrainingConfig = Union[AcousticFinetuningConfig, AcousticPretrainingConfig]
@dataclass
class ConformerConfig:
n_layers: int
n_heads: int
n_hidden: int
p_dropout: float
kernel_size_conv_mod: int
kernel_size_depthwise: int
with_ff: bool
@dataclass
class ReferenceEncoderConfig:
bottleneck_size_p: int
bottleneck_size_u: int
ref_enc_filters: List[int]
ref_enc_size: int
ref_enc_strides: List[int]
ref_enc_pad: List[int]
ref_enc_gru_size: int
ref_attention_dropout: float
token_num: int
predictor_kernel_size: int
@dataclass
class VarianceAdaptorConfig:
n_hidden: int
kernel_size: int
emb_kernel_size: int
p_dropout: float
n_bins: int
@dataclass
class AcousticLossConfig:
ssim_loss_alpha: float
mel_loss_alpha: float
aligner_loss_alpha: float
pitch_loss_alpha: float
energy_loss_alpha: float
u_prosody_loss_alpha: float
p_prosody_loss_alpha: float
dur_loss_alpha: float
binary_align_loss_alpha: float
binary_loss_warmup_epochs: int
@dataclass
class AcousticENModelConfig:
speaker_embed_dim: int = 1024
lang_embed_dim: int = 1
encoder: ConformerConfig = field(
default_factory=lambda: ConformerConfig(
n_layers=6,
n_heads=8,
n_hidden=512,
p_dropout=0.1,
kernel_size_conv_mod=7,
kernel_size_depthwise=7,
with_ff=True,
),
)
decoder: ConformerConfig = field(
default_factory=lambda: ConformerConfig(
n_layers=6,
n_heads=8,
n_hidden=512,
p_dropout=0.1,
kernel_size_conv_mod=11,
kernel_size_depthwise=11,
with_ff=True,
),
)
reference_encoder: ReferenceEncoderConfig = field(
default_factory=lambda: ReferenceEncoderConfig(
bottleneck_size_p=4,
bottleneck_size_u=256,
ref_enc_filters=[32, 32, 64, 64, 128, 128],
ref_enc_size=3,
ref_enc_strides=[1, 2, 1, 2, 1],
ref_enc_pad=[1, 1],
ref_enc_gru_size=32,
ref_attention_dropout=0.2,
token_num=32,
predictor_kernel_size=5,
),
)
variance_adaptor: VarianceAdaptorConfig = field(
default_factory=lambda: VarianceAdaptorConfig(
n_hidden=512,
kernel_size=5,
emb_kernel_size=3,
p_dropout=0.5,
n_bins=256,
),
)
loss: AcousticLossConfig = field(
default_factory=lambda: AcousticLossConfig(
ssim_loss_alpha=1.0,
mel_loss_alpha=1.0,
aligner_loss_alpha=1.0,
pitch_loss_alpha=1.0,
energy_loss_alpha=1.0,
u_prosody_loss_alpha=0.25,
p_prosody_loss_alpha=0.25,
dur_loss_alpha=1.0,
binary_align_loss_alpha=0.1,
binary_loss_warmup_epochs=10,
),
)
@dataclass
class AcousticMultilingualModelConfig:
speaker_embed_dim: int = 1024
lang_embed_dim: int = 256
encoder: ConformerConfig = field(
default_factory=lambda: ConformerConfig(
n_layers=6,
n_heads=8,
n_hidden=512,
p_dropout=0.1,
kernel_size_conv_mod=7,
kernel_size_depthwise=7,
with_ff=True,
),
)
decoder: ConformerConfig = field(
default_factory=lambda: ConformerConfig(
n_layers=6,
n_heads=8,
n_hidden=512,
p_dropout=0.1,
kernel_size_conv_mod=11,
kernel_size_depthwise=11,
with_ff=True,
),
)
reference_encoder: ReferenceEncoderConfig = field(
default_factory=lambda: ReferenceEncoderConfig(
bottleneck_size_p=4,
bottleneck_size_u=256,
ref_enc_filters=[32, 32, 64, 64, 128, 128],
ref_enc_size=3,
ref_enc_strides=[1, 2, 1, 2, 1],
ref_enc_pad=[1, 1],
ref_enc_gru_size=32,
ref_attention_dropout=0.2,
token_num=32,
predictor_kernel_size=5,
),
)
variance_adaptor: VarianceAdaptorConfig = field(
default_factory=lambda: VarianceAdaptorConfig(
n_hidden=512,
kernel_size=5,
emb_kernel_size=3,
p_dropout=0.5,
n_bins=256,
),
)
loss: AcousticLossConfig = field(
default_factory=lambda: AcousticLossConfig(
ssim_loss_alpha=1.0,
mel_loss_alpha=1.0,
aligner_loss_alpha=1.0,
pitch_loss_alpha=1.0,
energy_loss_alpha=1.0,
u_prosody_loss_alpha=0.25,
p_prosody_loss_alpha=0.25,
dur_loss_alpha=1.0,
binary_align_loss_alpha=0.1,
binary_loss_warmup_epochs=10,
),
)
AcousticModelConfigType = Union[AcousticENModelConfig, AcousticMultilingualModelConfig]
@dataclass
class VocoderBasicConfig:
segment_size: int = 16384
learning_rate: float = 0.0001
adam_b1: float = 0.5
adam_b2: float = 0.9
lr_decay: float = 0.995
synth_interval: int = 250
checkpoint_interval: int = 250
stft_lamb: float = 2.5
@dataclass
class VocoderPretrainingConfig(VocoderBasicConfig):
batch_size: int = 14
grad_accum_steps: int = 1
train_steps: int = 1000000
stdout_interval: int = 25
validation_interval: int = 2000
@dataclass
class VocoderFinetuningConfig(VocoderBasicConfig):
batch_size: int = 5
grad_accum_steps: int = 3
train_steps: int = 10000
stdout_interval: int = 100
validation_interval: int = 4000
VoicoderTrainingConfig = Union[VocoderPretrainingConfig, VocoderFinetuningConfig]
@dataclass
class VocoderGeneratorConfig:
noise_dim: int
channel_size: int
dilations: List[int]
strides: List[int]
lReLU_slope: float
kpnet_conv_size: int
@dataclass
class VocoderMPDConfig:
periods: List[int]
kernel_size: int
stride: int
use_spectral_norm: bool
lReLU_slope: float
@dataclass
class VocoderMRDConfig:
resolutions: List[Tuple[int, int, int]]
use_spectral_norm: bool
lReLU_slope: float
@dataclass
class VocoderModelConfig:
gen: VocoderGeneratorConfig = field(
default_factory=lambda: VocoderGeneratorConfig(
noise_dim=64,
channel_size=32,
dilations=[1, 3, 9, 27],
strides=[8, 8, 4],
lReLU_slope=0.2,
kpnet_conv_size=3,
),
)
mpd: VocoderMPDConfig = field(
default_factory=lambda: VocoderMPDConfig(
periods=[2, 3, 5, 7, 11],
kernel_size=5,
stride=3,
use_spectral_norm=False,
lReLU_slope=0.2,
),
)
mrd: VocoderMRDConfig = field(
default_factory=lambda: VocoderMRDConfig(
resolutions=[(1024, 120, 600), (2048, 240, 1200), (512, 50, 240)],
use_spectral_norm=False,
lReLU_slope=0.2,
),
)
#####################
# HI-FI GAN CONFIGS #
#####################
@dataclass
class HifiGanPretrainingConfig(VocoderBasicConfig):
segment_size: int = 16384
learning_rate: float = 0.0002
adam_b1: float = 0.8
adam_b2: float = 0.99
lr_decay: float = 0.9995
lReLU_slope: float = 0.1
l1_factor: int = 45
sampling_rate_acoustic: int = 22050
sampling_rate_vocoder: int = 44100
@dataclass
class HifiGanConfig:
resblock: str = "1"
upsample_rates: List[int] = field(
default_factory=lambda: [8, 8, 4, 2],
)
upsample_kernel_sizes: List[int] = field(
default_factory=lambda: [16, 16, 4, 4],
)
upsample_initial_channel: int = 512
resblock_kernel_sizes: List[int] = field(
default_factory=lambda: [3, 7, 11],
)
resblock_dilation_sizes: List[List[int]] = field(
default_factory=lambda: [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
)
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