Source code for TTS.config.shared_configs

from dataclasses import asdict, dataclass
from typing import List

from coqpit import Coqpit, check_argument

[docs]@dataclass class BaseAudioConfig(Coqpit): """Base config to definge audio processing parameters. It is used to initialize `````` 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. trim_db (int): Silence threshold used for silence trimming. Defaults to 45. 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 # 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 # 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. unused_speakers (List): List of speakers IDs that are not used at the training. Default 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 = "" ununsed_speakers: List[str] = None 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(Coqpit): """Base config to define the basic training parameters that are shared among all the models. Args: model (str): Name of the model that is used in the training. run_name (str): Name of the experiment. This prefixes the output folder name. Defaults to `coqui_tts`. run_description (str): Short description of the experiment. epochs (int): Number training epochs. Defaults to 10000. batch_size (int): Training batch size. eval_batch_size (int): Validation batch size. mixed_precision (bool): Enable / Disable mixed precision training. It reduces the VRAM use and allows larger batch sizes, however it may also cause numerical unstability in some cases. scheduler_after_epoch (bool): If true, run the scheduler step after each epoch else run it after each model step. run_eval (bool): Enable / Disable evaluation (validation) run. Defaults to True. test_delay_epochs (int): Number of epochs before starting to use evaluation runs. Initially, models do not generate meaningful results, hence waiting for a couple of epochs might save some time. print_eval (bool): Enable / Disable console logging for evalutaion steps. If disabled then it only shows the final values at the end of the evaluation. Default to ```False```. print_step (int): Number of steps required to print the next training log. log_dashboard (str): "tensorboard" or "wandb" Set the experiment tracking tool plot_step (int): Number of steps required to log training on Tensorboard. model_param_stats (bool): Enable / Disable logging internal model stats for model diagnostic. It might be useful for model debugging. Defaults to ```False```. project_name (str): Name of the project. Defaults to config.model wandb_entity (str): Name of W&B entity/team. Enables collaboration across a team or org. log_model_step (int): Number of steps required to log a checkpoint as W&B artifact save_step (int):ipt Number of steps required to save the next checkpoint. checkpoint (bool): Enable / Disable checkpointing. keep_all_best (bool): Enable / Disable keeping all the saved best models instead of overwriting the previous one. Defaults to ```False```. keep_after (int): Number of steps to wait before saving all the best models. In use if ```keep_all_best == True```. Defaults to 10000. num_loader_workers (int): Number of workers for training time dataloader. num_eval_loader_workers (int): Number of workers for evaluation time dataloader. output_path (str): Path for training output folder, either a local file path or other URLs supported by both fsspec and tensorboardX, e.g. GCS (gs://) or S3 (s3://) paths. The nonexist part of the given path is created automatically. All training artefacts are saved there. """ model: str = None run_name: str = "coqui_tts" run_description: str = "" # training params epochs: int = 10000 batch_size: int = None eval_batch_size: int = None mixed_precision: bool = False scheduler_after_epoch: bool = False # eval params run_eval: bool = True test_delay_epochs: int = 0 print_eval: bool = False # logging dashboard_logger: str = "tensorboard" print_step: int = 25 plot_step: int = 100 model_param_stats: bool = False project_name: str = None log_model_step: int = None wandb_entity: str = None # checkpointing save_step: int = 10000 checkpoint: bool = True keep_all_best: bool = False keep_after: int = 10000 # dataloading num_loader_workers: int = 0 num_eval_loader_workers: int = 0 use_noise_augment: bool = False # paths output_path: str = None # distributed distributed_backend: str = "nccl" distributed_url: str = "tcp://localhost:54321"