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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() | |