from dataclasses import asdict, dataclass
from typing import List
from coqpit import Coqpit, check_argument
@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.
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
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"