import numpy


def time_shift(data, shift):
    return np.roll(data, int(shift))


def add_noise(data, noise_levels=(0, 0.3)):
    noise_level = np.random.uniform(*noise_levels)
    noise = np.random.randn(len(data))
    data_noise = data + noise_level * noise
    return data_noise


def change_speed(data, speed_factor):
    return librosa.effects.time_stretch(data, speed_factor)


def change_pitch(data, sampling_rate):
    n_steps = np.random.randint(-1, 2)
    return librosa.effects.pitch_shift(data, sampling_rate, n_steps)

4db63dc438ec6d37cd2afe45ac55fdf190db1096
<mask token>


def add_noise(data, noise_levels=(0, 0.3)):
    noise_level = np.random.uniform(*noise_levels)
    noise = np.random.randn(len(data))
    data_noise = data + noise_level * noise
    return data_noise


def change_speed(data, speed_factor):
    return librosa.effects.time_stretch(data, speed_factor)


<mask token>

<mask token>


def time_shift(data, shift):
    return np.roll(data, int(shift))


def add_noise(data, noise_levels=(0, 0.3)):
    noise_level = np.random.uniform(*noise_levels)
    noise = np.random.randn(len(data))
    data_noise = data + noise_level * noise
    return data_noise


def change_speed(data, speed_factor):
    return librosa.effects.time_stretch(data, speed_factor)


<mask token>

<mask token>


def time_shift(data, shift):
    return np.roll(data, int(shift))


def add_noise(data, noise_levels=(0, 0.3)):
    noise_level = np.random.uniform(*noise_levels)
    noise = np.random.randn(len(data))
    data_noise = data + noise_level * noise
    return data_noise


def change_speed(data, speed_factor):
    return librosa.effects.time_stretch(data, speed_factor)


def change_pitch(data, sampling_rate):
    n_steps = np.random.randint(-1, 2)
    return librosa.effects.pitch_shift(data, sampling_rate, n_steps)

import numpy


def time_shift(data, shift):
    return np.roll(data, int(shift))


def add_noise(data, noise_levels=(0, 0.3)):
    noise_level = np.random.uniform(*noise_levels)
    noise = np.random.randn(len(data))
    data_noise = data + noise_level * noise
    return data_noise


def change_speed(data, speed_factor):
    return librosa.effects.time_stretch(data, speed_factor)


def change_pitch(data, sampling_rate):
    n_steps = np.random.randint(-1, 2)
    return librosa.effects.pitch_shift(data, sampling_rate, n_steps)
