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from dataclasses import dataclass, field
from typing import List
from TTS.tts.configs.shared_configs import BaseTTSConfig
from TTS.tts.models.delightful_tts import DelightfulTtsArgs, DelightfulTtsAudioConfig, VocoderConfig
@dataclass
class DelightfulTTSConfig(BaseTTSConfig):
"""
Configuration class for the DelightfulTTS model.
Attributes:
model (str): Name of the model ("delightful_tts").
audio (DelightfulTtsAudioConfig): Configuration for audio settings.
model_args (DelightfulTtsArgs): Configuration for model arguments.
use_attn_priors (bool): Whether to use attention priors.
vocoder (VocoderConfig): Configuration for the vocoder.
init_discriminator (bool): Whether to initialize the discriminator.
steps_to_start_discriminator (int): Number of steps to start the discriminator.
grad_clip (List[float]): Gradient clipping values.
lr_gen (float): Learning rate for the gan generator.
lr_disc (float): Learning rate for the gan discriminator.
lr_scheduler_gen (str): Name of the learning rate scheduler for the generator.
lr_scheduler_gen_params (dict): Parameters for the learning rate scheduler for the generator.
lr_scheduler_disc (str): Name of the learning rate scheduler for the discriminator.
lr_scheduler_disc_params (dict): Parameters for the learning rate scheduler for the discriminator.
scheduler_after_epoch (bool): Whether to schedule after each epoch.
optimizer (str): Name of the optimizer.
optimizer_params (dict): Parameters for the optimizer.
ssim_loss_alpha (float): Alpha value for the SSIM loss.
mel_loss_alpha (float): Alpha value for the mel loss.
aligner_loss_alpha (float): Alpha value for the aligner loss.
pitch_loss_alpha (float): Alpha value for the pitch loss.
energy_loss_alpha (float): Alpha value for the energy loss.
u_prosody_loss_alpha (float): Alpha value for the utterance prosody loss.
p_prosody_loss_alpha (float): Alpha value for the phoneme prosody loss.
dur_loss_alpha (float): Alpha value for the duration loss.
char_dur_loss_alpha (float): Alpha value for the character duration loss.
binary_align_loss_alpha (float): Alpha value for the binary alignment loss.
binary_loss_warmup_epochs (int): Number of warm-up epochs for the binary loss.
disc_loss_alpha (float): Alpha value for the discriminator loss.
gen_loss_alpha (float): Alpha value for the generator loss.
feat_loss_alpha (float): Alpha value for the feature loss.
vocoder_mel_loss_alpha (float): Alpha value for the vocoder mel loss.
multi_scale_stft_loss_alpha (float): Alpha value for the multi-scale STFT loss.
multi_scale_stft_loss_params (dict): Parameters for the multi-scale STFT loss.
return_wav (bool): Whether to return audio waveforms.
use_weighted_sampler (bool): Whether to use a weighted sampler.
weighted_sampler_attrs (dict): Attributes for the weighted sampler.
weighted_sampler_multipliers (dict): Multipliers for the weighted sampler.
r (int): Value for the `r` override.
compute_f0 (bool): Whether to compute F0 values.
f0_cache_path (str): Path to the F0 cache.
attn_prior_cache_path (str): Path to the attention prior cache.
num_speakers (int): Number of speakers.
use_speaker_embedding (bool): Whether to use speaker embedding.
speakers_file (str): Path to the speaker file.
speaker_embedding_channels (int): Number of channels for the speaker embedding.
language_ids_file (str): Path to the language IDs file.
"""
model: str = "delightful_tts"
# model specific params
audio: DelightfulTtsAudioConfig = field(default_factory=DelightfulTtsAudioConfig)
model_args: DelightfulTtsArgs = field(default_factory=DelightfulTtsArgs)
use_attn_priors: bool = True
# vocoder
vocoder: VocoderConfig = field(default_factory=VocoderConfig)
init_discriminator: bool = True
# optimizer
steps_to_start_discriminator: int = 200000
grad_clip: List[float] = field(default_factory=lambda: [1000, 1000])
lr_gen: float = 0.0002
lr_disc: float = 0.0002
lr_scheduler_gen: str = "ExponentialLR"
lr_scheduler_gen_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1})
lr_scheduler_disc: str = "ExponentialLR"
lr_scheduler_disc_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1})
scheduler_after_epoch: bool = True
optimizer: str = "AdamW"
optimizer_params: dict = field(default_factory=lambda: {"betas": [0.8, 0.99], "eps": 1e-9, "weight_decay": 0.01})
# acoustic model loss params
ssim_loss_alpha: float = 1.0
mel_loss_alpha: float = 1.0
aligner_loss_alpha: float = 1.0
pitch_loss_alpha: float = 1.0
energy_loss_alpha: float = 1.0
u_prosody_loss_alpha: float = 0.5
p_prosody_loss_alpha: float = 0.5
dur_loss_alpha: float = 1.0
char_dur_loss_alpha: float = 0.01
binary_align_loss_alpha: float = 0.1
binary_loss_warmup_epochs: int = 10
# vocoder loss params
disc_loss_alpha: float = 1.0
gen_loss_alpha: float = 1.0
feat_loss_alpha: float = 1.0
vocoder_mel_loss_alpha: float = 10.0
multi_scale_stft_loss_alpha: float = 2.5
multi_scale_stft_loss_params: dict = field(
default_factory=lambda: {
"n_ffts": [1024, 2048, 512],
"hop_lengths": [120, 240, 50],
"win_lengths": [600, 1200, 240],
}
)
# data loader params
return_wav: bool = True
use_weighted_sampler: bool = False
weighted_sampler_attrs: dict = field(default_factory=lambda: {})
weighted_sampler_multipliers: dict = field(default_factory=lambda: {})
# overrides
r: int = 1
# dataset configs
compute_f0: bool = True
f0_cache_path: str = None
attn_prior_cache_path: str = None
# multi-speaker settings
# use speaker embedding layer
num_speakers: int = 0
use_speaker_embedding: bool = False
speakers_file: str = None
speaker_embedding_channels: int = 256
language_ids_file: str = None
use_language_embedding: bool = False
# use d-vectors
use_d_vector_file: bool = False
d_vector_file: str = None
d_vector_dim: int = None
# testing
test_sentences: List[List[str]] = field(
default_factory=lambda: [
["It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent."],
["Be a voice, not an echo."],
["I'm sorry Dave. I'm afraid I can't do that."],
["This cake is great. It's so delicious and moist."],
["Prior to November 22, 1963."],
]
)
def __post_init__(self):
# Pass multi-speaker parameters to the model args as `model.init_multispeaker()` looks for it there.
if self.num_speakers > 0:
self.model_args.num_speakers = self.num_speakers
# speaker embedding settings
if self.use_speaker_embedding:
self.model_args.use_speaker_embedding = True
if self.speakers_file:
self.model_args.speakers_file = self.speakers_file
# d-vector settings
if self.use_d_vector_file:
self.model_args.use_d_vector_file = True
if self.d_vector_dim is not None and self.d_vector_dim > 0:
self.model_args.d_vector_dim = self.d_vector_dim
if self.d_vector_file:
self.model_args.d_vector_file = self.d_vector_file
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