from dataclasses import asdict, dataclass, field from typing import List from coqpit import MISSING, Coqpit, check_argument from TTS.config import BaseAudioConfig, BaseDatasetConfig, BaseTrainingConfig @dataclass class GSTConfig(Coqpit): """Defines the Global Style Token Module Args: gst_style_input_wav (str): Path to the wav file used to define the style of the output speech at inference. Defaults to None. gst_style_input_weights (dict): Defines the weights for each style token used at inference. Defaults to None. gst_embedding_dim (int): Defines the size of the GST embedding vector dimensions. Defaults to 256. gst_num_heads (int): Number of attention heads used by the multi-head attention. Defaults to 4. gst_num_style_tokens (int): Number of style token vectors. Defaults to 10. """ gst_style_input_wav: str = None gst_style_input_weights: dict = None gst_embedding_dim: int = 256 gst_use_speaker_embedding: bool = False gst_num_heads: int = 4 gst_num_style_tokens: int = 10 def check_values( self, ): """Check config fields""" c = asdict(self) super().check_values() check_argument("gst_style_input_weights", c, restricted=False) check_argument("gst_style_input_wav", c, restricted=False) check_argument("gst_embedding_dim", c, restricted=True, min_val=0, max_val=1000) check_argument("gst_use_speaker_embedding", c, restricted=False) check_argument("gst_num_heads", c, restricted=True, min_val=2, max_val=10) check_argument("gst_num_style_tokens", c, restricted=True, min_val=1, max_val=1000) @dataclass class CharactersConfig(Coqpit): """Defines character or phoneme set used by the model Args: pad (str): characters in place of empty padding. Defaults to None. eos (str): characters showing the end of a sentence. Defaults to None. bos (str): characters showing the beginning of a sentence. Defaults to None. characters (str): character set used by the model. Characters not in this list are ignored when converting input text to a list of sequence IDs. Defaults to None. punctuations (str): characters considered as punctuation as parsing the input sentence. Defaults to None. phonemes (str): characters considered as parsing phonemes. Defaults to None. unique (bool): remove any duplicate characters in the character lists. It is a bandaid for compatibility with the old models trained with character lists with duplicates. """ pad: str = None eos: str = None bos: str = None characters: str = None punctuations: str = None phonemes: str = None unique: bool = True # for backwards compatibility of models trained with char sets with duplicates def check_values( self, ): """Check config fields""" c = asdict(self) check_argument("pad", c, "characters", restricted=True) check_argument("eos", c, "characters", restricted=True) check_argument("bos", c, "characters", restricted=True) check_argument("characters", c, "characters", restricted=True) check_argument("phonemes", c, restricted=True) check_argument("punctuations", c, "characters", restricted=True) @dataclass class BaseTTSConfig(BaseTrainingConfig): """Shared parameters among all the tts models. Args: audio (BaseAudioConfig): Audio processor config object instance. use_phonemes (bool): enable / disable phoneme use. compute_input_seq_cache (bool): enable / disable precomputation of the phoneme sequences. At the expense of some delay at the beginning of the training, It allows faster data loader time and precise limitation with `max_seq_len` and `min_seq_len`. text_cleaner (str): Name of the text cleaner used for cleaning and formatting transcripts. enable_eos_bos_chars (bool): enable / disable the use of eos and bos characters. test_senteces_file (str): Path to a txt file that has sentences used at test time. The file must have a sentence per line. phoneme_cache_path (str): Path to the output folder caching the computed phonemes for each sample. characters (CharactersConfig): Instance of a CharactersConfig class. 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_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. compute_f0 (int): (Not in use yet). use_noise_augment (bool): Augment the input audio with random noise. 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. """ audio: BaseAudioConfig = field(default_factory=BaseAudioConfig) # phoneme settings use_phonemes: bool = False phoneme_language: str = None compute_input_seq_cache: bool = False text_cleaner: str = MISSING enable_eos_bos_chars: bool = False test_sentences_file: str = "" phoneme_cache_path: str = None # vocabulary parameters characters: CharactersConfig = None # training params batch_group_size: int = 0 loss_masking: bool = None # dataloading min_seq_len: int = 1 max_seq_len: int = float("inf") compute_f0: bool = False use_noise_augment: bool = False add_blank: bool = False # dataset datasets: List[BaseDatasetConfig] = field(default_factory=lambda: [BaseDatasetConfig()])