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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.align_tts import AlignTTSArgs
@dataclass
class AlignTTSConfig(BaseTTSConfig):
"""Defines parameters for AlignTTS model.
Example:
>>> from TTS.tts.configs.align_tts_config import AlignTTSConfig
>>> config = AlignTTSConfig()
Args:
model(str):
Model name used for selecting the right model at initialization. Defaults to `align_tts`.
positional_encoding (bool):
enable / disable positional encoding applied to the encoder output. Defaults to True.
hidden_channels (int):
Base number of hidden channels. Defines all the layers expect ones defined by the specific encoder or decoder
parameters. Defaults to 256.
hidden_channels_dp (int):
Number of hidden channels of the duration predictor's layers. Defaults to 256.
encoder_type (str):
Type of the encoder used by the model. Look at `TTS.tts.layers.feed_forward.encoder` for more details.
Defaults to `fftransformer`.
encoder_params (dict):
Parameters used to define the encoder network. Look at `TTS.tts.layers.feed_forward.encoder` for more details.
Defaults to `{"hidden_channels_ffn": 1024, "num_heads": 2, "num_layers": 6, "dropout_p": 0.1}`.
decoder_type (str):
Type of the decoder used by the model. Look at `TTS.tts.layers.feed_forward.decoder` for more details.
Defaults to `fftransformer`.
decoder_params (dict):
Parameters used to define the decoder network. Look at `TTS.tts.layers.feed_forward.decoder` for more details.
Defaults to `{"hidden_channels_ffn": 1024, "num_heads": 2, "num_layers": 6, "dropout_p": 0.1}`.
phase_start_steps (List[int]):
A list of number of steps required to start the next training phase. AlignTTS has 4 different training
phases. Thus you need to define 4 different values to enable phase based training. If None, it
trains the whole model together. Defaults to None.
ssim_alpha (float):
Weight for the SSIM loss. If set <= 0, disables the SSIM loss. Defaults to 1.0.
duration_loss_alpha (float):
Weight for the duration predictor's loss. Defaults to 1.0.
mdn_alpha (float):
Weight for the MDN loss. Defaults to 1.0.
spec_loss_alpha (float):
Weight for the MSE spectrogram loss. If set <= 0, disables the L1 loss. Defaults to 1.0.
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.
noam_schedule (bool):
enable / disable the use of Noam LR scheduler. Defaults to False.
warmup_steps (int):
Number of warm-up steps for the Noam scheduler. Defaults 4000.
lr (float):
Initial learning rate. Defaults to `1e-3`.
wd (float):
Weight decay coefficient. Defaults to `1e-7`.
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."""
model: str = "align_tts"
# model specific params
model_args: AlignTTSArgs = field(default_factory=AlignTTSArgs)
phase_start_steps: List[int] = None
ssim_alpha: float = 1.0
spec_loss_alpha: float = 1.0
dur_loss_alpha: float = 1.0
mdn_alpha: float = 1.0
# multi-speaker settings
use_speaker_embedding: bool = False
use_d_vector_file: bool = False
d_vector_file: str = False
# optimizer parameters
optimizer: str = "Adam"
optimizer_params: dict = field(default_factory=lambda: {"betas": [0.9, 0.998], "weight_decay": 1e-6})
lr_scheduler: str = None
lr_scheduler_params: dict = None
lr: float = 1e-4
grad_clip: float = 5.0
# overrides
min_seq_len: int = 13
max_seq_len: int = 200
r: int = 1
# 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.",
]
)
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