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initial prototype for vits2 #2838
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from dataclasses import dataclass, field | ||
from typing import List | ||
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from TTS.tts.configs.shared_configs import BaseTTSConfig | ||
from TTS.tts.models.vits2 import Vits2Args, Vits2AudioConfig | ||
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@dataclass | ||
class Vits2Config(BaseTTSConfig): | ||
"""Defines parameters for VITS2 End2End TTS model. | ||
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Args: | ||
model (str): | ||
Model name. Do not change unless you know what you are doing. | ||
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model_args (Vits2Args): | ||
Model architecture arguments. Defaults to `Vits2Args()`. | ||
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audio (Vits2AudioConfig): | ||
Audio processing configuration. Defaults to `Vits2AudioConfig()`. | ||
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grad_clip (List): | ||
Gradient clipping thresholds for each optimizer. Defaults to `[1000.0, 1000.0]`. | ||
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lr_gen (float): | ||
Initial learning rate for the generator. Defaults to 0.0002. | ||
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lr_disc (float): | ||
Initial learning rate for the discriminator. Defaults to 0.0002. | ||
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lr_scheduler_gen (str): | ||
Name of the learning rate scheduler for the generator. One of the `torch.optim.lr_scheduler.*`. Defaults to | ||
`ExponentialLR`. | ||
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lr_scheduler_gen_params (dict): | ||
Parameters for the learning rate scheduler of the generator. Defaults to `{'gamma': 0.999875, "last_epoch":-1}`. | ||
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lr_scheduler_disc (str): | ||
Name of the learning rate scheduler for the discriminator. One of the `torch.optim.lr_scheduler.*`. Defaults to | ||
`ExponentialLR`. | ||
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lr_scheduler_disc_params (dict): | ||
Parameters for the learning rate scheduler of the discriminator. Defaults to `{'gamma': 0.999875, "last_epoch":-1}`. | ||
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scheduler_after_epoch (bool): | ||
If true, step the schedulers after each epoch else after each step. Defaults to `False`. | ||
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optimizer (str): | ||
Name of the optimizer to use with both the generator and the discriminator networks. One of the | ||
`torch.optim.*`. Defaults to `AdamW`. | ||
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kl_loss_alpha (float): | ||
Loss weight for KL loss. Defaults to 1.0. | ||
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disc_loss_alpha (float): | ||
Loss weight for the discriminator loss. Defaults to 1.0. | ||
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gen_loss_alpha (float): | ||
Loss weight for the generator loss. Defaults to 1.0. | ||
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feat_loss_alpha (float): | ||
Loss weight for the feature matching loss. Defaults to 1.0. | ||
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mel_loss_alpha (float): | ||
Loss weight for the mel loss. Defaults to 45.0. | ||
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return_wav (bool): | ||
If true, data loader returns the waveform as well as the other outputs. Do not change. Defaults to `True`. | ||
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compute_linear_spec (bool): | ||
If true, the linear spectrogram is computed and returned alongside the mel output. Do not change. Defaults to `True`. | ||
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use_weighted_sampler (bool): | ||
If true, use weighted sampler with bucketing for balancing samples between datasets used in training. Defaults to `False`. | ||
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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 `{}`. | ||
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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 `{}`. | ||
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r (int): | ||
Number of spectrogram frames to be generated at a time. Do not change. Defaults to `1`. | ||
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add_blank (bool): | ||
If true, a blank token is added in between every character. Defaults to `True`. | ||
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test_sentences (List[List]): | ||
List of sentences with speaker and language information to be used for testing. | ||
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language_ids_file (str): | ||
Path to the language ids file. | ||
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use_language_embedding (bool): | ||
If true, language embedding is used. Defaults to `False`. | ||
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Note: | ||
Check :class:`TTS.tts.configs.shared_configs.BaseTTSConfig` for the inherited parameters. | ||
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Example: | ||
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>>> from TTS.tts.configs.vits2_config import Vits2Config | ||
>>> config = Vits2Config() | ||
""" | ||
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model: str = "vits2" | ||
# model specific params | ||
model_args: Vits2Args = field(default_factory=Vits2Args) | ||
audio: Vits2AudioConfig = field(default_factory=Vits2AudioConfig) | ||
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# optimizer | ||
grad_clip: List[float] = field(default_factory=lambda: [1000, 1000]) | ||
lr_gen: float = 0.0002 | ||
lr_disc: float = 0.0002 | ||
lr_dur: float = 0.0002 | ||
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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}) | ||
lr_scheduler_dur: str = "ExponentialLR" | ||
lr_scheduler_dur_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1}) | ||
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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}) | ||
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# 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 | ||
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# data loader params | ||
return_wav: bool = True | ||
compute_linear_spec: bool = True | ||
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# 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: {}) | ||
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# overrides | ||
r: int = 1 # DO NOT CHANGE | ||
add_blank: bool = True | ||
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# 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."], | ||
] | ||
) | ||
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# 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 | ||
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# use d-vectors | ||
use_d_vector_file: bool = False | ||
d_vector_file: List[str] = None | ||
d_vector_dim: int = None | ||
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def __post_init__(self): | ||
for key, val in self.model_args.items(): | ||
if hasattr(self, key): | ||
self[key] = val |
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Better if you move this into
layers/vits2