| import torch | |
| class min_max_scaler(): | |
| def __init__(self, upper_bound=1, lower_bound=0): | |
| self.upper = upper_bound | |
| self.lower = lower_bound | |
| self.minimum = torch.ones(1) * torch.inf | |
| self.maximum = - torch.ones(1) *torch.inf | |
| def fit(self, set_maximum=0.0, set_minimum=-100.0): | |
| """Find min and max of given subset OR set min and max manually. | |
| Since dB-spectrograms are on the scale [-100, 0] by default, default values are set to those values. | |
| Args: | |
| set_maximum (float, optional): set maximum value manually. Defaults to 0.0. | |
| set_minimum (float, optional): set minimum value manually. Defaults to -100.0. | |
| Returns: | |
| None: None | |
| """ | |
| if set_minimum is not None and set_maximum is not None: | |
| self.minimum = set_minimum | |
| self.maximum = set_maximum | |
| return None | |
| def transform(self, spectrogram): | |
| if self.minimum == torch.inf: | |
| raise ValueError("Cannot transform before scaler is fitted with min-max-values") | |
| return (self.upper - self.lower) * (spectrogram - self.minimum) / (self.maximum - self.minimum) + self.lower | |
| def inverse_transform(self, spectrogram): | |
| if self.minimum == torch.inf: | |
| raise ValueError("Cannot inverse transform before scaler is fitted with min-max-values") | |
| return (spectrogram - self.lower) * (self.maximum - self.minimum) / (self.upper - self.lower) + self.minimum |