to python3.9

best_model_gradio.ipynb

@@ -59,14 +59,14 @@
     "torch.manual_seed(SEED)\n",
     "\n",
     "# Dataloading params\n",
-    "PATHS: list[str] = [\n",
+    "PATHS: list = [\n",
     "    \"../data/\",\n",
     "    \"../new_data/JulienNestor\",\n",
     "    \"../new_data/classroom_data\",\n",
     "    \"../new_data/class\",\n",
     "    \"../new_data/JulienRaph\",\n",
     "]\n",
-    "REMOVE_LABEL: list[str] = [\n",
+    "REMOVE_LABEL: list = [\n",
     "         \"penduleinverse\", \"pendule\", \n",
     "         \"decollage\", \"atterrissage\",\n",
     "         \"plushaut\", \"plusbas\",\n",

dataloading.py

@@ -7,11 +7,11 @@ from pathlib import Path
 from typing import Callable, Literal, Optional
 
 def load_dataset(
-    paths: list[str],
-    remove_label: list[str] = [""],
+    paths: list,
+    remove_label: list = [""],
     sr: int = 22050,
-    method : Literal["fix_length", "time_stretch"] = "fix_length",
-    max_time: float = 4.0) -> tuple[pd.DataFrame, Callable[[np.ndarray, int], np.ndarray]]:
+    method  = "fix_length",
+    max_time: float = 4.0):
     """Folder dataset in memory loader (return fully loaded pandas dataframe).
     - For sklearn, load the whole dataset if possible otherwise use `proportion` to only load a part of the dataset.
     - For pytorch, load the whole dataset if possible otherwise use `proportion` to only load a part of the dataset.
@@ -31,7 +31,7 @@ def load_dataset(
 
     Args:
         paths (list[Path]): list of dataset directory to parse.
-        remove_label (list[str], optional): list of label to remove. Defaults to None.. Defaults to [""].
+        remove_label (list, optional): list of label to remove. Defaults to None.. Defaults to [""].
         shuffle (bool, optional): True to suffle the dataframe. Defaults to True.
         proportion (float, optional): Proportion of file to load. Defaults to 1.0.
         sr (int, optional): Sample Rate to resample audio file. Defaults to 22050.
@@ -41,7 +41,7 @@ def load_dataset(
     Returns:
         df (pd.DataFrame): A pd.DataFrame with such define column: 
         - absolute_path (str): file-system absolute path of the .wav file.
-        - labels (list[str]): list of labels defining the sound file (ie, subdirectories and post _ filename).
+        - labels (list): list of labels defining the sound file (ie, subdirectories and post _ filename).
         - ground_truth (str): ground_truth label meaning the last one after _ in the sound filename.
         - y_original_signal (np.ndarray): sound signal normalize as `float64` and resample with the given sr by `librosa.load`
         - y_original_duration (float): y_original_signal signal duration.
@@ -79,7 +79,7 @@ def load_dataset(
 def uniformize(
         audio: np.ndarray,
         sr: int,
-        method: Literal["fix_length", "time_stretch"] = "fix_length", 
+        method = "fix_length", 
         max_time: float = 4.0
         ):
     if method == "fix_length":

gradio_utils.py

@@ -3,17 +3,15 @@ from typing import Callable, Optional
 
 import numpy as np
 
-
-
 import librosa
 
 import gradio as gr
 
-def predict_gradio(data: tuple[int, np.ndarray], 
-        uniform_lambda: Callable[[np.ndarray, int], np.ndarray],
+def predict_gradio(data, 
+        uniform_lambda,
         sklearn_model,
         label_transform,
-        target_sr: int = 22_050) -> Optional[dict]:
+        target_sr: int = 22_050):
     if data is None:
         return 
 

preprocessing.py

@@ -6,18 +6,18 @@ 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):
+    def __init__(self, windows_number: int = 300, statistique = np.mean):
         self.windows_number = windows_number
         self.statistique = statistique
 
-    def fit(self, X: np.ndarray, y: Optional[np.ndarray] = None):
+    def fit(self, X: np.ndarray, y = None):
         return self
 
-    def fit_transform(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> np.ndarray:
+    def fit_transform(self, X: np.ndarray, y = 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:
+    def transform(self, X: np.ndarray, y = None) -> np.ndarray:
         X_ = X.copy()
         *c_, size_ = X_.shape
         windows_size_ = size_//self.windows_number
@@ -34,14 +34,14 @@ class MeanTransformer(BaseEstimator, TransformerMixin):
         self.windows_number = windows_number
         self.windows_size = 0
 
-    def fit(self, X: np.ndarray, y: Optional[np.ndarray] = None):
+    def fit(self, X: np.ndarray, y = None):
         return self
 
-    def fit_transform(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> np.ndarray:
+    def fit_transform(self, X: np.ndarray, y = 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:
+    def transform(self, X: np.ndarray, y = None) -> np.ndarray:
         X_ = X.copy()
         *c_, size_ = X_.shape
         windows_size_ = size_//self.windows_number
@@ -62,14 +62,14 @@ 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):
+    def fit(self, X: np.ndarray, y = None):
         return self
 
-    def fit_transform(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> np.ndarray:
+    def fit_transform(self, X: np.ndarray, y = 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:
+    def transform(self, X: np.ndarray, y = None) -> np.ndarray:
         X_ = X.copy()
         *c_, size_ = X_.shape
         windows_size_ = size_//self.windows_number
@@ -91,14 +91,14 @@ class MfccTransformer(BaseEstimator, TransformerMixin):
         c_, *_ = X_.shape
         return X_.reshape(c_, -1, self.N_MFCC)
 
-    def fit(self, X: np.ndarray, y: Optional[np.ndarray] = None):
+    def fit(self, X: np.ndarray, y = None):
         return self
 
-    def fit_transform(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> np.ndarray:
+    def fit_transform(self, X: np.ndarray, y = 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:
+    def transform(self, X: np.ndarray, y = None) -> np.ndarray:
         X_ = X.copy()
         c_, *_ = X_.shape
         mfcc = librosa.feature.mfcc(y=X_, 
@@ -130,14 +130,14 @@ class MelTransformer(BaseEstimator, TransformerMixin):
         c_, *_ = X_.shape
         return X_.reshape(c_, -1, self.N_MEL)
 
-    def fit(self, X: np.ndarray, y: Optional[np.ndarray] = None):
+    def fit(self, X: np.ndarray, y = None):
         return self
 
-    def fit_transform(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> np.ndarray:
+    def fit_transform(self, X: np.ndarray, y = 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:
+    def transform(self, X: np.ndarray, y = None) -> np.ndarray:
         X_ = X.copy()
         c_, *_ = X_.shape
         mel = librosa.feature.melspectrogram(y=X,
@@ -162,14 +162,14 @@ class TorchTransform(BaseEstimator, TransformerMixin):
     def __init__(self):
         pass
 
-    def fit(self, X: np.ndarray, y: Optional[np.ndarray] = None):
+    def fit(self, X: np.ndarray, y = None):
         return self
 
-    def fit_transform(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> torch.Tensor:
+    def fit_transform(self, X: np.ndarray, y = 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:
+    def transform(self, X: np.ndarray, y = None) -> torch.Tensor:
         return torch.tensor(X).unsqueeze(dim=1)
 
     def inverse_transform(self, X: torch.Tensor) -> np.ndarray:
@@ -179,14 +179,14 @@ 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):
+    def fit(self, X: np.ndarray, y = None):
         return self
 
-    def fit_transform(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> np.ndarray:
+    def fit_transform(self, X: np.ndarray, y = 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:
+    def transform(self, X: np.ndarray, y = 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"):