ResidualModule and New Parameters, Speed Manipulation

audio_toolset/audio_augmentation.py

@@ -1,58 +1,19 @@
+import librosa
 import numpy as np
 
 
-class NoiseInjection(object):
+class Speed(object):
 
-    def __init__(self, noise_factor: float, sigma=0.5, mu=0.5):
-        assert noise_factor > 0, f'max_shift_ratio has to be greater then 0, but was: {noise_factor}.'
-        self.mu = mu
-        self.sigma = sigma
-        self.noise_factor = noise_factor
+    def __init__(self, max_ratio=0.3, speed_factor=1):
+        self.speed_factor = speed_factor
+        self.max_ratio = max_ratio
 
-    def __call__(self, x: np.ndarray):
-        noise = np.random.normal(loc=self.mu, scale=self.sigma, size=x.shape)
-        augmented_data = x + self.noise_factor * noise
-        # Cast back to same data type
-        augmented_data = augmented_data.astype(x.dtype)
-        return augmented_data
-
-
-class LoudnessManipulator(object):
-
-    def __init__(self, max_factor: float):
-        assert 1 > max_factor > 0, f'max_shift_ratio has to be between [0,1], but was: {max_factor}.'
-
-        self.max_factor = max_factor
-
-    def __call__(self, x: np.ndarray):
-        augmented_data = x + x * (np.random.random() * self.max_factor)
-        # Cast back to same data type
-        augmented_data = augmented_data.astype(x.dtype)
-        return augmented_data
-
-
-class ShiftTime(object):
-
-    valid_shifts = ['right', 'left', 'any']
-
-    def __init__(self, max_shift_ratio: float, shift_direction: str = 'any'):
-        assert 1 > max_shift_ratio > 0, f'max_shift_ratio has to be between [0,1], but was: {max_shift_ratio}.'
-        assert shift_direction.lower() in self.valid_shifts, f'shift_direction has to be one of: {self.valid_shifts}'
-        self.max_shift_ratio = max_shift_ratio
-        self.shift_direction = shift_direction.lower()
-
-    def __call__(self, x: np.ndarray):
-        shift = np.random.randint(max(int(self.max_shift_ratio * x.shape[-1]), 1))
-        if self.shift_direction == 'right':
-            shift = -1 * shift
-        elif self.shift_direction == 'any':
-            direction = np.random.choice([1, -1], 1)
-            shift = direction * shift
-        augmented_data = np.roll(x, shift)
-        # Set to silence for heading/ tailing
-        shift = int(shift)
-        if shift > 0:
-            augmented_data[:shift] = 0
-        else:
-            augmented_data[shift:] = 0
-        return augmented_data
+    def __call__(self, x):
+        start = int(np.random.randint(0, x.shape[-1],1))
+        end = min(int((np.random.uniform(0, self.max_ratio, 1) * x.shape[-1]) + start), x.shape[-1])
+        try:
+            speed_factor = float(np.random.uniform(min(self.speed_factor, 1), max(self.speed_factor, 1), 1))
+            aug_data = librosa.effects.time_stretch(x[start:end], speed_factor)
+            return np.concatenate((x[:start], aug_data, x[end:]), axis=0)[:x.shape[-1]]
+        except ValueError:
+            return x