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from dataclasses import asdict, dataclass
from typing import List
from coqpit import Coqpit, check_argument
from trainer import TrainerConfig
@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 ```1```.
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 = 1.0
# 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
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:
formatter (str):
Formatter name that defines used formatter in ```TTS.tts.datasets.formatter```. Defaults to `""`.
dataset_name (str):
Unique name for the dataset. Defaults to `""`.
path (str):
Root path to the dataset files. Defaults to `""`.
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 `""`.
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 `""`.
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.
"""
formatter: str = ""
dataset_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("formatter", 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
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