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#!/usr/bin/python3
# -*- coding: utf-8 -*-
"""
https://huggingface.co/spaces/alibabasglab/ClearVoice/blob/main/checkpoints/FRCRN_SE_16K/config.yaml
https://huggingface.co/spaces/alibabasglab/ClearVoice/blob/main/config/inference/FRCRN_SE_16K.yaml
"""
from toolbox.torchaudio.configuration_utils import PretrainedConfig
class FRCRNConfig(PretrainedConfig):
def __init__(self,
num_gpus: int = -1,
lr: float = 0.001,
lr_scheduler: str = "CosineAnnealingLR",
lr_scheduler_kwargs: dict = None,
max_epochs: int = 100,
weight_decay: float = 0.00001,
clip_grad_norm: float = 10.,
seed: int = 1234,
sample_rate: int = 8000,
segment_size: int = 32000,
nfft: int = 512,
win_size: int = 512,
hop_size: int = 256,
win_type: str = "hann",
use_complex_networks: bool = True,
model_depth: int = 20,
model_complexity: int = 45,
min_snr_db: float = -10,
max_snr_db: float = 20,
num_workers: int = 4,
batch_size: int = 4,
eval_steps: int = 25000,
**kwargs
):
super(FRCRNConfig, self).__init__(**kwargs)
self.num_gpus = num_gpus
self.lr = lr
self.lr_scheduler = lr_scheduler
self.lr_scheduler_kwargs = lr_scheduler_kwargs or dict()
self.max_epochs = max_epochs
self.weight_decay = weight_decay
self.clip_grad_norm = clip_grad_norm
self.seed = seed
self.sample_rate = sample_rate
self.segment_size = segment_size
self.nfft = nfft
self.win_size = win_size
self.hop_size = hop_size
self.win_type = win_type
self.use_complex_networks = use_complex_networks
self.model_depth = model_depth
self.model_complexity = model_complexity
self.min_snr_db = min_snr_db
self.max_snr_db = max_snr_db
self.num_workers = num_workers
self.batch_size = batch_size
self.eval_steps = eval_steps
def main():
config = FRCRNConfig()
config.to_yaml_file("config.yaml")
return
if __name__ == "__main__":
main()
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