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| import numpy as np | |
| import torch | |
| import librosa | |
| from sklearn.base import BaseEstimator, TransformerMixin | |
| from typing import Callable, Optional | |
| class ReductionTransformer(BaseEstimator, TransformerMixin): | |
| def __init__(self, windows_number: int = 300, statistique: Callable[[np.ndarray], np.ndarray] = np.mean): | |
| self.windows_number = windows_number | |
| self.statistique = statistique | |
| def fit(self, X: np.ndarray, y: Optional[np.ndarray] = None): | |
| return self | |
| def fit_transform(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> np.ndarray: | |
| self.fit(X, y) | |
| return self.transform(X, y) | |
| def transform(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> np.ndarray: | |
| X_ = X.copy() | |
| *c_, size_ = X_.shape | |
| windows_size_ = size_//self.windows_number | |
| metrique_clip = X_[..., :self.windows_number*windows_size_] | |
| return np.apply_along_axis(self.statistique, | |
| axis=-1, | |
| arr=metrique_clip.reshape((*c_, self.windows_number, windows_size_))) | |
| def inverse_transform(self, X: np.ndarray) -> np.ndarray: | |
| raise NotImplementedError | |
| class MeanTransformer(BaseEstimator, TransformerMixin): | |
| def __init__(self, windows_number: int = 300): | |
| self.windows_number = windows_number | |
| self.windows_size = 0 | |
| def fit(self, X: np.ndarray, y: Optional[np.ndarray] = None): | |
| return self | |
| def fit_transform(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> np.ndarray: | |
| self.fit(X, y) | |
| return self.transform(X, y) | |
| def transform(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> np.ndarray: | |
| X_ = X.copy() | |
| *c_, size_ = X_.shape | |
| windows_size_ = size_//self.windows_number | |
| self.windows_size = windows_size_ | |
| metrique_clip = X_[..., :self.windows_number*windows_size_] | |
| return np.mean(metrique_clip.reshape((*c_, self.windows_number, windows_size_)), axis=-1) | |
| def inverse_transform(self, X: np.ndarray) -> np.ndarray: | |
| original_size = self.windows_size*self.windows_number | |
| X_reconstruct = np.interp( | |
| x = np.arange(start=0, stop=original_size, step=1), | |
| xp = np.arange(start=0, stop=original_size, step=self.windows_size), | |
| fp = X | |
| ) | |
| return X_reconstruct | |
| class StdTransformer(BaseEstimator, TransformerMixin): | |
| def __init__(self, windows_number: int = 300): | |
| self.windows_number = windows_number | |
| def fit(self, X: np.ndarray, y: Optional[np.ndarray] = None): | |
| return self | |
| def fit_transform(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> np.ndarray: | |
| self.fit(X, y) | |
| return self.transform(X, y) | |
| def transform(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> np.ndarray: | |
| X_ = X.copy() | |
| *c_, size_ = X_.shape | |
| windows_size_ = size_//self.windows_number | |
| metrique_clip = X_[..., :self.windows_number*windows_size_] | |
| return np.std(metrique_clip.reshape((*c_, self.windows_number, windows_size_)), axis=-1) | |
| def inverse_transform(self, X: np.ndarray) -> np.ndarray: | |
| raise NotImplementedError | |
| class MfccTransformer(BaseEstimator, TransformerMixin): | |
| def __init__(self, sr: int = 22050, N_MFCC: int = 12, hop_length: int = 1024, reshape_output: bool = True): | |
| self.sr = sr | |
| self.N_MFCC = N_MFCC | |
| self.hop_length = hop_length | |
| self.reshape_output = reshape_output | |
| def reshape(self, X: np.ndarray) -> np.ndarray: | |
| X_ = X.copy() | |
| c_, *_ = X_.shape | |
| return X_.reshape(c_, -1, self.N_MFCC) | |
| def fit(self, X: np.ndarray, y: Optional[np.ndarray] = None): | |
| return self | |
| def fit_transform(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> np.ndarray: | |
| self.fit(X, y) | |
| return self.transform(X, y) | |
| def transform(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> np.ndarray: | |
| X_ = X.copy() | |
| c_, *_ = X_.shape | |
| mfcc = librosa.feature.mfcc(y=X_, | |
| sr=self.sr, | |
| hop_length=self.hop_length, | |
| n_mfcc=self.N_MFCC | |
| ) | |
| if self.reshape_output: | |
| mfcc = mfcc.reshape(c_, -1) | |
| return mfcc | |
| def inverse_transform(self, X: np.ndarray) -> np.ndarray: | |
| X_reconstruct = librosa.feature.inverse.mfcc_to_audio( | |
| mfcc = X, | |
| n_mels = self.N_MFCC, | |
| ) | |
| return X_reconstruct | |
| class MelTransformer(BaseEstimator, TransformerMixin): | |
| def __init__(self, sr: int = 22050, N_MEL: int = 12, hop_length: int = 1024, reshape_output: bool = True): | |
| self.sr = sr | |
| self.N_MEL = N_MEL | |
| self.hop_length = hop_length | |
| self.reshape_output = reshape_output | |
| def reshape(self, X: np.ndarray) -> np.ndarray: | |
| X_ = X.copy() | |
| c_, *_ = X_.shape | |
| return X_.reshape(c_, -1, self.N_MEL) | |
| def fit(self, X: np.ndarray, y: Optional[np.ndarray] = None): | |
| return self | |
| def fit_transform(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> np.ndarray: | |
| self.fit(X, y) | |
| return self.transform(X, y) | |
| def transform(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> np.ndarray: | |
| X_ = X.copy() | |
| c_, *_ = X_.shape | |
| mel = librosa.feature.melspectrogram(y=X, | |
| sr=self.sr, | |
| hop_length=self.hop_length, | |
| n_mels=self.N_MEL | |
| ) | |
| if self.reshape_output: | |
| mel = mel.reshape(c_, -1) | |
| return mel | |
| def inverse_transform(self, X: np.ndarray) -> np.ndarray: | |
| X_reconstruct = librosa.feature.inverse.mel_to_audio( | |
| M = X, | |
| sr = self.sr, | |
| hop_length = self.hop_length | |
| ) | |
| return X_reconstruct | |
| class TorchTransform(BaseEstimator, TransformerMixin): | |
| def __init__(self): | |
| pass | |
| def fit(self, X: np.ndarray, y: Optional[np.ndarray] = None): | |
| return self | |
| def fit_transform(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> torch.Tensor: | |
| self.fit(X, y) | |
| return self.transform(X, y) | |
| def transform(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> torch.Tensor: | |
| return torch.tensor(X).unsqueeze(dim=1) | |
| def inverse_transform(self, X: torch.Tensor) -> np.ndarray: | |
| return np.array(X.squeeze(dim=1)) | |
| class ShuffleTransformer(BaseEstimator, TransformerMixin): | |
| def __init__(self, p: float = 0.005): | |
| self.p = p | |
| def fit(self, X: np.ndarray, y: Optional[np.ndarray] = None): | |
| return self | |
| def fit_transform(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> np.ndarray: | |
| self.fit(X, y) | |
| return self.transform(X, y) | |
| def transform(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> np.ndarray: | |
| will_swap = np.random.choice(X.shape[0], int(self.p*X.shape[0])) | |
| will_swap_with = np.random.choice(X.shape[0], int(self.p*X.shape[0])) | |
| if hasattr(X, "copy"): | |
| X_ = X.copy() | |
| elif hasattr(X, "clone"): | |
| X_ = X.clone() | |
| else: | |
| X_ = X | |
| X_[will_swap, ...] = X_[will_swap_with, ...] | |
| return X_ | |
| def inverse_transform(self, X: np.ndarray) -> np.ndarray: | |
| raise NotImplementedError | |