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from dataclasses import dataclass, field
from TTS.vocoder.configs.shared_configs import BaseVocoderConfig
from TTS.vocoder.models.wavegrad import WavegradArgs
@dataclass
class WavegradConfig(BaseVocoderConfig):
"""Defines parameters for WaveGrad vocoder.
Example:
>>> from TTS.vocoder.configs import WavegradConfig
>>> config = WavegradConfig()
Args:
model (str):
Model name used for selecting the right model at initialization. Defaults to `wavegrad`.
generator_model (str): One of the generators from TTS.vocoder.models.*`. Every other non-GAN vocoder model is
considered as a generator too. Defaults to `wavegrad`.
model_params (WavegradArgs): Model parameters. Check `WavegradArgs` for default values.
target_loss (str):
Target loss name that defines the quality of the model. Defaults to `avg_wavegrad_loss`.
epochs (int):
Number of epochs to traing the model. Defaults to 10000.
batch_size (int):
Batch size used at training. Larger values use more memory. Defaults to 96.
seq_len (int):
Audio segment length used at training. Larger values use more memory. Defaults to 6144.
use_cache (bool):
enable / disable in memory caching of the computed features. It can cause OOM error if the system RAM is
not large enough. Defaults to True.
mixed_precision (bool):
enable / disable mixed precision training. Default is True.
eval_split_size (int):
Number of samples used for evalutaion. Defaults to 50.
train_noise_schedule (dict):
Training noise schedule. Defaults to
`{"min_val": 1e-6, "max_val": 1e-2, "num_steps": 1000}`
test_noise_schedule (dict):
Inference noise schedule. For a better performance, you may need to use `bin/tune_wavegrad.py` to find a
better schedule. Defaults to
`
{
"min_val": 1e-6,
"max_val": 1e-2,
"num_steps": 50,
}
`
grad_clip (float):
Gradient clipping threshold. If <= 0.0, no clipping is applied. Defaults to 1.0
lr (float):
Initila leraning rate. Defaults to 1e-4.
lr_scheduler (str):
One of the learning rate schedulers from `torch.optim.scheduler.*`. Defaults to `MultiStepLR`.
lr_scheduler_params (dict):
kwargs for the scheduler. Defaults to `{"gamma": 0.5, "milestones": [100000, 200000, 300000, 400000, 500000, 600000]}`
"""
model: str = "wavegrad"
# Model specific params
generator_model: str = "wavegrad"
model_params: WavegradArgs = field(default_factory=WavegradArgs)
target_loss: str = "loss" # loss value to pick the best model to save after each epoch
# Training - overrides
epochs: int = 10000
batch_size: int = 96
seq_len: int = 6144
use_cache: bool = True
mixed_precision: bool = True
eval_split_size: int = 50
# NOISE SCHEDULE PARAMS
train_noise_schedule: dict = field(default_factory=lambda: {"min_val": 1e-6, "max_val": 1e-2, "num_steps": 1000})
test_noise_schedule: dict = field(
default_factory=lambda: { # inference noise schedule. Try TTS/bin/tune_wavegrad.py to find the optimal values.
"min_val": 1e-6,
"max_val": 1e-2,
"num_steps": 50,
}
)
# optimizer overrides
grad_clip: float = 1.0
lr: float = 1e-4 # Initial learning rate.
lr_scheduler: str = "MultiStepLR" # one of the schedulers from https:#pytorch.org/docs/stable/optim.html
lr_scheduler_params: dict = field(
default_factory=lambda: {"gamma": 0.5, "milestones": [100000, 200000, 300000, 400000, 500000, 600000]}
)
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