from dataclasses import dataclass, field
from typing import List
from TTS.tts.configs.shared_configs import BaseTTSConfig
from TTS.tts.models.forward_tts import ForwardTTSArgs
@dataclass
class Fastspeech2Config(BaseTTSConfig):
"""Configure `ForwardTTS` as FastPitch model.
Example:
>>> from TTS.tts.configs.fastspeech2_config import FastSpeech2Config
>>> config = FastSpeech2Config()
Args:
model (str):
Model name used for selecting the right model at initialization. Defaults to `fast_pitch`.
base_model (str):
Name of the base model being configured as this model so that 🐸 TTS knows it needs to initiate
the base model rather than searching for the `model` implementation. Defaults to `forward_tts`.
model_args (Coqpit):
Model class arguments. Check `FastPitchArgs` for more details. Defaults to `FastPitchArgs()`.
data_dep_init_steps (int):
Number of steps used for computing normalization parameters at the beginning of the training. GlowTTS uses
Activation Normalization that pre-computes normalization stats at the beginning and use the same values
for the rest. Defaults to 10.
speakers_file (str):
Path to the file containing the list of speakers. Needed at inference for loading matching speaker ids to
speaker names. Defaults to `None`.
use_speaker_embedding (bool):
enable / disable using speaker embeddings for multi-speaker models. If set True, the model is
in the multi-speaker mode. Defaults to False.
use_d_vector_file (bool):
enable /disable using external speaker embeddings in place of the learned embeddings. Defaults to False.
d_vector_file (str):
Path to the file including pre-computed speaker embeddings. Defaults to None.
d_vector_dim (int):
Dimension of the external speaker embeddings. Defaults to 0.
optimizer (str):
Name of the model optimizer. Defaults to `Adam`.
optimizer_params (dict):
Arguments of the model optimizer. Defaults to `{"betas": [0.9, 0.998], "weight_decay": 1e-6}`.
lr_scheduler (str):
Name of the learning rate scheduler. Defaults to `Noam`.
lr_scheduler_params (dict):
Arguments of the learning rate scheduler. Defaults to `{"warmup_steps": 4000}`.
lr (float):
Initial learning rate. Defaults to `1e-3`.
grad_clip (float):
Gradient norm clipping value. Defaults to `5.0`.
spec_loss_type (str):
Type of the spectrogram loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`.
duration_loss_type (str):
Type of the duration loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`.
use_ssim_loss (bool):
Enable/disable the use of SSIM (Structural Similarity) loss. Defaults to True.
wd (float):
Weight decay coefficient. Defaults to `1e-7`.
ssim_loss_alpha (float):
Weight for the SSIM loss. If set 0, disables the SSIM loss. Defaults to 1.0.
dur_loss_alpha (float):
Weight for the duration predictor's loss. If set 0, disables the huber loss. Defaults to 1.0.
spec_loss_alpha (float):
Weight for the L1 spectrogram loss. If set 0, disables the L1 loss. Defaults to 1.0.
pitch_loss_alpha (float):
Weight for the pitch predictor's loss. If set 0, disables the pitch predictor. Defaults to 1.0.
energy_loss_alpha (float):
Weight for the energy predictor's loss. If set 0, disables the energy predictor. Defaults to 1.0.
binary_align_loss_alpha (float):
Weight for the binary loss. If set 0, disables the binary loss. Defaults to 1.0.
binary_loss_warmup_epochs (float):
Number of epochs to gradually increase the binary loss impact. Defaults to 150.
min_seq_len (int):
Minimum input sequence length to be used at training.
max_seq_len (int):
Maximum input sequence length to be used at training. Larger values result in more VRAM usage.
# dataset configs
compute_f0(bool):
Compute pitch. defaults to True
f0_cache_path(str):
pith cache path. defaults to None
# dataset configs
compute_energy(bool):
Compute energy. defaults to True
energy_cache_path(str):
energy cache path. defaults to None
"""
model: str = "fastspeech2"
base_model: str = "forward_tts"
# model specific params
model_args: ForwardTTSArgs = field(default_factory=lambda: ForwardTTSArgs(use_pitch=True, use_energy=True))
# multi-speaker settings
num_speakers: int = 0
speakers_file: str = None
use_speaker_embedding: bool = False
use_d_vector_file: bool = False
d_vector_file: str = False
d_vector_dim: int = 0
# optimizer parameters
optimizer: str = "Adam"
optimizer_params: dict = field(default_factory=lambda: {"betas": [0.9, 0.998], "weight_decay": 1e-6})
lr_scheduler: str = "NoamLR"
lr_scheduler_params: dict = field(default_factory=lambda: {"warmup_steps": 4000})
lr: float = 1e-4
grad_clip: float = 5.0
# loss params
spec_loss_type: str = "mse"
duration_loss_type: str = "mse"
use_ssim_loss: bool = True
ssim_loss_alpha: float = 1.0
spec_loss_alpha: float = 1.0
aligner_loss_alpha: float = 1.0
pitch_loss_alpha: float = 0.1
energy_loss_alpha: float = 0.1
dur_loss_alpha: float = 0.1
binary_align_loss_alpha: float = 0.1
binary_loss_warmup_epochs: int = 150
# overrides
min_seq_len: int = 13
max_seq_len: int = 200
r: int = 1 # DO NOT CHANGE
# dataset configs
compute_f0: bool = True
f0_cache_path: str = None
# dataset configs
compute_energy: bool = True
energy_cache_path: str = None
# testing
test_sentences: 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