| from dataclasses import asdict, dataclass, field |
| from typing import Dict, List |
|
|
| from coqpit import Coqpit, check_argument |
|
|
| from TTS.config import BaseAudioConfig, BaseDatasetConfig, BaseTrainingConfig |
|
|
|
|
| @dataclass |
| class BaseVCConfig(BaseTrainingConfig): |
| """Shared parameters among all the tts models. |
| |
| Args: |
| |
| audio (BaseAudioConfig): |
| Audio processor config object instance. |
| |
| batch_group_size (int): |
| Size of the batch groups used for bucketing. By default, the dataloader orders samples by the sequence |
| length for a more efficient and stable training. If `batch_group_size > 1` then it performs bucketing to |
| prevent using the same batches for each epoch. |
| |
| loss_masking (bool): |
| enable / disable masking loss values against padded segments of samples in a batch. |
| |
| min_text_len (int): |
| Minimum length of input text to be used. All shorter samples will be ignored. Defaults to 0. |
| |
| max_text_len (int): |
| Maximum length of input text to be used. All longer samples will be ignored. Defaults to float("inf"). |
| |
| min_audio_len (int): |
| Minimum length of input audio to be used. All shorter samples will be ignored. Defaults to 0. |
| |
| max_audio_len (int): |
| Maximum length of input audio to be used. All longer samples will be ignored. The maximum length in the |
| dataset defines the VRAM used in the training. Hence, pay attention to this value if you encounter an |
| OOM error in training. Defaults to float("inf"). |
| |
| compute_f0 (int): |
| (Not in use yet). |
| |
| compute_energy (int): |
| (Not in use yet). |
| |
| compute_linear_spec (bool): |
| If True data loader computes and returns linear spectrograms alongside the other data. |
| |
| precompute_num_workers (int): |
| Number of workers to precompute features. Defaults to 0. |
| |
| use_noise_augment (bool): |
| Augment the input audio with random noise. |
| |
| start_by_longest (bool): |
| If True, the data loader will start loading the longest batch first. It is useful for checking OOM issues. |
| Defaults to False. |
| |
| shuffle (bool): |
| If True, the data loader will shuffle the dataset when there is not sampler defined. Defaults to True. |
| |
| drop_last (bool): |
| If True, the data loader will drop the last batch if it is not complete. It helps to prevent |
| issues that emerge from the partial batch statistics. Defaults to True. |
| |
| add_blank (bool): |
| Add blank characters between each other two characters. It improves performance for some models at expense |
| of slower run-time due to the longer input sequence. |
| |
| datasets (List[BaseDatasetConfig]): |
| List of datasets used for training. If multiple datasets are provided, they are merged and used together |
| for training. |
| |
| optimizer (str): |
| Optimizer used for the training. Set one from `torch.optim.Optimizer` or `TTS.utils.training`. |
| Defaults to ``. |
| |
| optimizer_params (dict): |
| Optimizer kwargs. Defaults to `{"betas": [0.8, 0.99], "weight_decay": 0.0}` |
| |
| lr_scheduler (str): |
| Learning rate scheduler for the training. Use one from `torch.optim.Scheduler` schedulers or |
| `TTS.utils.training`. Defaults to ``. |
| |
| lr_scheduler_params (dict): |
| Parameters for the generator learning rate scheduler. Defaults to `{"warmup": 4000}`. |
| |
| test_sentences (List[str]): |
| List of sentences to be used at testing. Defaults to '[]' |
| |
| eval_split_max_size (int): |
| Number maximum of samples to be used for evaluation in proportion split. Defaults to None (Disabled). |
| |
| eval_split_size (float): |
| If between 0.0 and 1.0 represents the proportion of the dataset to include in the evaluation set. |
| If > 1, represents the absolute number of evaluation samples. Defaults to 0.01 (1%). |
| |
| use_speaker_weighted_sampler (bool): |
| Enable / Disable the batch balancer by speaker. Defaults to ```False```. |
| |
| speaker_weighted_sampler_alpha (float): |
| Number that control the influence of the speaker sampler weights. Defaults to ```1.0```. |
| |
| use_language_weighted_sampler (bool): |
| Enable / Disable the batch balancer by language. Defaults to ```False```. |
| |
| language_weighted_sampler_alpha (float): |
| Number that control the influence of the language sampler weights. Defaults to ```1.0```. |
| |
| use_length_weighted_sampler (bool): |
| Enable / Disable the batch balancer by audio length. If enabled the dataset will be divided |
| into 10 buckets considering the min and max audio of the dataset. The sampler weights will be |
| computed forcing to have the same quantity of data for each bucket in each training batch. Defaults to ```False```. |
| |
| length_weighted_sampler_alpha (float): |
| Number that control the influence of the length sampler weights. Defaults to ```1.0```. |
| """ |
|
|
| audio: BaseAudioConfig = field(default_factory=BaseAudioConfig) |
| |
| batch_group_size: int = 0 |
| loss_masking: bool = None |
| |
| min_audio_len: int = 1 |
| max_audio_len: int = float("inf") |
| min_text_len: int = 1 |
| max_text_len: int = float("inf") |
| compute_f0: bool = False |
| compute_energy: bool = False |
| compute_linear_spec: bool = False |
| precompute_num_workers: int = 0 |
| use_noise_augment: bool = False |
| start_by_longest: bool = False |
| shuffle: bool = False |
| drop_last: bool = False |
| |
| datasets: List[BaseDatasetConfig] = field(default_factory=lambda: [BaseDatasetConfig()]) |
| |
| optimizer: str = "radam" |
| optimizer_params: dict = None |
| |
| lr_scheduler: str = None |
| lr_scheduler_params: dict = field(default_factory=lambda: {}) |
| |
| test_sentences: List[str] = field(default_factory=lambda: []) |
| |
| eval_split_max_size: int = None |
| eval_split_size: float = 0.01 |
| |
| use_speaker_weighted_sampler: bool = False |
| speaker_weighted_sampler_alpha: float = 1.0 |
| use_language_weighted_sampler: bool = False |
| language_weighted_sampler_alpha: float = 1.0 |
| use_length_weighted_sampler: bool = False |
| length_weighted_sampler_alpha: float = 1.0 |
|
|
