Source code for TTS.config.shared_configs

from dataclasses import asdict, dataclass
from typing import List

from coqpit import Coqpit, check_argument
from trainer import TrainerConfig


[docs]@dataclass class BaseAudioConfig(Coqpit): """Base config to definge audio processing parameters. It is used to initialize ```TTS.utils.audio.AudioProcessor.``` Args: fft_size (int): Number of STFT frequency levels aka.size of the linear spectogram frame. Defaults to 1024. win_length (int): Each frame of audio is windowed by window of length ```win_length``` and then padded with zeros to match ```fft_size```. Defaults to 1024. hop_length (int): Number of audio samples between adjacent STFT columns. Defaults to 1024. frame_shift_ms (int): Set ```hop_length``` based on milliseconds and sampling rate. frame_length_ms (int): Set ```win_length``` based on milliseconds and sampling rate. stft_pad_mode (str): Padding method used in STFT. 'reflect' or 'center'. Defaults to 'reflect'. sample_rate (int): Audio sampling rate. Defaults to 22050. resample (bool): Enable / Disable resampling audio to ```sample_rate```. Defaults to ```False```. preemphasis (float): Preemphasis coefficient. Defaults to 0.0. ref_level_db (int): 20 Reference Db level to rebase the audio signal and ignore the level below. 20Db is assumed the sound of air. Defaults to 20. do_sound_norm (bool): Enable / Disable sound normalization to reconcile the volume differences among samples. Defaults to False. log_func (str): Numpy log function used for amplitude to DB conversion. Defaults to 'np.log10'. do_trim_silence (bool): Enable / Disable trimming silences at the beginning and the end of the audio clip. Defaults to ```True```. do_amp_to_db_linear (bool, optional): enable/disable amplitude to dB conversion of linear spectrograms. Defaults to True. do_amp_to_db_mel (bool, optional): enable/disable amplitude to dB conversion of mel spectrograms. Defaults to True. pitch_fmax (float, optional): Maximum frequency of the F0 frames. Defaults to ```640```. pitch_fmin (float, optional): Minimum frequency of the F0 frames. Defaults to ```0```. trim_db (int): Silence threshold used for silence trimming. Defaults to 45. do_rms_norm (bool, optional): enable/disable RMS volume normalization when loading an audio file. Defaults to False. db_level (int, optional): dB level used for rms normalization. The range is -99 to 0. Defaults to None. power (float): Exponent used for expanding spectrogra levels before running Griffin Lim. It helps to reduce the artifacts in the synthesized voice. Defaults to 1.5. griffin_lim_iters (int): Number of Griffing Lim iterations. Defaults to 60. num_mels (int): Number of mel-basis frames that defines the frame lengths of each mel-spectrogram frame. Defaults to 80. mel_fmin (float): Min frequency level used for the mel-basis filters. ~50 for male and ~95 for female voices. It needs to be adjusted for a dataset. Defaults to 0. mel_fmax (float): Max frequency level used for the mel-basis filters. It needs to be adjusted for a dataset. spec_gain (int): Gain applied when converting amplitude to DB. Defaults to 20. signal_norm (bool): enable/disable signal normalization. Defaults to True. min_level_db (int): minimum db threshold for the computed melspectrograms. Defaults to -100. symmetric_norm (bool): enable/disable symmetric normalization. If set True normalization is performed in the range [-k, k] else [0, k], Defaults to True. max_norm (float): ```k``` defining the normalization range. Defaults to 4.0. clip_norm (bool): enable/disable clipping the our of range values in the normalized audio signal. Defaults to True. stats_path (str): Path to the computed stats file. Defaults to None. """ # stft parameters fft_size: int = 1024 win_length: int = 1024 hop_length: int = 256 frame_shift_ms: int = None frame_length_ms: int = None stft_pad_mode: str = "reflect" # audio processing parameters sample_rate: int = 22050 resample: bool = False preemphasis: float = 0.0 ref_level_db: int = 20 do_sound_norm: bool = False log_func: str = "np.log10" # silence trimming do_trim_silence: bool = True trim_db: int = 45 # rms volume normalization do_rms_norm: bool = False db_level: float = None # griffin-lim params power: float = 1.5 griffin_lim_iters: int = 60 # mel-spec params num_mels: int = 80 mel_fmin: float = 0.0 mel_fmax: float = None spec_gain: int = 20 do_amp_to_db_linear: bool = True do_amp_to_db_mel: bool = True # f0 params pitch_fmax: float = 640.0 pitch_fmin: float = 0.0 # normalization params signal_norm: bool = True min_level_db: int = -100 symmetric_norm: bool = True max_norm: float = 4.0 clip_norm: bool = True stats_path: str = None
[docs] def check_values( self, ): """Check config fields""" c = asdict(self) check_argument("num_mels", c, restricted=True, min_val=10, max_val=2056) check_argument("fft_size", c, restricted=True, min_val=128, max_val=4058) check_argument("sample_rate", c, restricted=True, min_val=512, max_val=100000) check_argument( "frame_length_ms", c, restricted=True, min_val=10, max_val=1000, alternative="win_length", ) check_argument("frame_shift_ms", c, restricted=True, min_val=1, max_val=1000, alternative="hop_length") check_argument("preemphasis", c, restricted=True, min_val=0, max_val=1) check_argument("min_level_db", c, restricted=True, min_val=-1000, max_val=10) check_argument("ref_level_db", c, restricted=True, min_val=0, max_val=1000) check_argument("power", c, restricted=True, min_val=1, max_val=5) check_argument("griffin_lim_iters", c, restricted=True, min_val=10, max_val=1000) # normalization parameters check_argument("signal_norm", c, restricted=True) check_argument("symmetric_norm", c, restricted=True) check_argument("max_norm", c, restricted=True, min_val=0.1, max_val=1000) check_argument("clip_norm", c, restricted=True) check_argument("mel_fmin", c, restricted=True, min_val=0.0, max_val=1000) check_argument("mel_fmax", c, restricted=True, min_val=500.0, allow_none=True) check_argument("spec_gain", c, restricted=True, min_val=1, max_val=100) check_argument("do_trim_silence", c, restricted=True) check_argument("trim_db", c, restricted=True)
@dataclass class BaseDatasetConfig(Coqpit): """Base config for TTS datasets. Args: name (str): Dataset name that defines the preprocessor in use. Defaults to None. path (str): Root path to the dataset files. Defaults to None. meta_file_train (str): Name of the dataset meta file. Or a list of speakers to be ignored at training for multi-speaker datasets. Defaults to None. ignored_speakers (List): List of speakers IDs that are not used at the training. Default None. language (str): Language code of the dataset. If defined, it overrides `phoneme_language`. Defaults to None. meta_file_val (str): Name of the dataset meta file that defines the instances used at validation. meta_file_attn_mask (str): Path to the file that lists the attention mask files used with models that require attention masks to train the duration predictor. """ name: str = "" path: str = "" meta_file_train: str = "" ignored_speakers: List[str] = None language: str = "" meta_file_val: str = "" meta_file_attn_mask: str = "" def check_values( self, ): """Check config fields""" c = asdict(self) check_argument("name", c, restricted=True) check_argument("path", c, restricted=True) check_argument("meta_file_train", c, restricted=True) check_argument("meta_file_val", c, restricted=False) check_argument("meta_file_attn_mask", c, restricted=False) @dataclass class BaseTrainingConfig(TrainerConfig): """Base config to define the basic 🐸TTS training parameters that are shared among all the models. It is based on ```Trainer.TrainingConfig```. Args: model (str): Name of the model that is used in the training. num_loader_workers (int): Number of workers for training time dataloader. num_eval_loader_workers (int): Number of workers for evaluation time dataloader. """ model: str = None # dataloading num_loader_workers: int = 0 num_eval_loader_workers: int = 0 use_noise_augment: bool = False