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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.vits import VitsArgs, VitsAudioConfig
@dataclass
class VitsConfig(BaseTTSConfig):
"""Defines parameters for VITS End2End TTS model.
Args:
model (str):
Model name. Do not change unless you know what you are doing.
model_args (VitsArgs):
Model architecture arguments. Defaults to `VitsArgs()`.
audio (VitsAudioConfig):
Audio processing configuration. Defaults to `VitsAudioConfig()`.
grad_clip (List):
Gradient clipping thresholds for each optimizer. Defaults to `[1000.0, 1000.0]`.
lr_gen (float):
Initial learning rate for the generator. Defaults to 0.0002.
lr_disc (float):
Initial learning rate for the discriminator. Defaults to 0.0002.
lr_scheduler_gen (str):
Name of the learning rate scheduler for the generator. One of the `torch.optim.lr_scheduler.*`. Defaults to
`ExponentialLR`.
lr_scheduler_gen_params (dict):
Parameters for the learning rate scheduler of the generator. Defaults to `{'gamma': 0.999875, "last_epoch":-1}`.
lr_scheduler_disc (str):
Name of the learning rate scheduler for the discriminator. One of the `torch.optim.lr_scheduler.*`. Defaults to
`ExponentialLR`.
lr_scheduler_disc_params (dict):
Parameters for the learning rate scheduler of the discriminator. Defaults to `{'gamma': 0.999875, "last_epoch":-1}`.
scheduler_after_epoch (bool):
If true, step the schedulers after each epoch else after each step. Defaults to `False`.
optimizer (str):
Name of the optimizer to use with both the generator and the discriminator networks. One of the
`torch.optim.*`. Defaults to `AdamW`.
kl_loss_alpha (float):
Loss weight for KL loss. Defaults to 1.0.
disc_loss_alpha (float):
Loss weight for the discriminator loss. Defaults to 1.0.
gen_loss_alpha (float):
Loss weight for the generator loss. Defaults to 1.0.
feat_loss_alpha (float):
Loss weight for the feature matching loss. Defaults to 1.0.
mel_loss_alpha (float):
Loss weight for the mel loss. Defaults to 45.0.
return_wav (bool):
If true, data loader returns the waveform as well as the other outputs. Do not change. Defaults to `True`.
compute_linear_spec (bool):
If true, the linear spectrogram is computed and returned alongside the mel output. Do not change. Defaults to `True`.
use_weighted_sampler (bool):
If true, use weighted sampler with bucketing for balancing samples between datasets used in training. Defaults to `False`.
weighted_sampler_attrs (dict):
Key retuned by the formatter to be used for weighted sampler. For example `{"root_path": 2.0, "speaker_name": 1.0}` sets sample probabilities
by overweighting `root_path` by 2.0. Defaults to `{}`.
weighted_sampler_multipliers (dict):
Weight each unique value of a key returned by the formatter for weighted sampling.
For example `{"root_path":{"/raid/datasets/libritts-clean-16khz-bwe-coqui_44khz/LibriTTS/train-clean-100/":1.0, "/raid/datasets/libritts-clean-16khz-bwe-coqui_44khz/LibriTTS/train-clean-360/": 0.5}`.
It will sample instances from `train-clean-100` 2 times more than `train-clean-360`. Defaults to `{}`.
r (int):
Number of spectrogram frames to be generated at a time. Do not change. Defaults to `1`.
add_blank (bool):
If true, a blank token is added in between every character. Defaults to `True`.
test_sentences (List[List]):
List of sentences with speaker and language information to be used for testing.
language_ids_file (str):
Path to the language ids file.
use_language_embedding (bool):
If true, language embedding is used. Defaults to `False`.
Note:
Check :class:`TTS.tts.configs.shared_configs.BaseTTSConfig` for the inherited parameters.
Example:
>>> from TTS.tts.configs.vits_config import VitsConfig
>>> config = VitsConfig()
"""
model: str = "vits"
# model specific params
model_args: VitsArgs = field(default_factory=VitsArgs)
audio: VitsAudioConfig = field(default_factory=VitsAudioConfig)
# optimizer
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})
# loss params
kl_loss_alpha: float = 1.0
disc_loss_alpha: float = 1.0
gen_loss_alpha: float = 1.0
feat_loss_alpha: float = 1.0
mel_loss_alpha: float = 45.0
dur_loss_alpha: float = 1.0
speaker_encoder_loss_alpha: float = 1.0
# data loader params
return_wav: bool = True
compute_linear_spec: bool = True
# sampler params
use_weighted_sampler: bool = False # TODO: move it to the base config
weighted_sampler_attrs: dict = field(default_factory=lambda: {})
weighted_sampler_multipliers: dict = field(default_factory=lambda: {})
# overrides
r: int = 1 # DO NOT CHANGE
add_blank: bool = True
# testing
test_sentences: List[List] = 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."],
]
)
# 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: List[str] = None
d_vector_dim: int = None
def __post_init__(self):
for key, val in self.model_args.items():
if hasattr(self, key):
self[key] = val
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