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import unittest
import numpy as np
import torch
from training.preprocess.compute_yin import (
compute_yin,
cumulativeMeanNormalizedDifferenceFunction,
differenceFunction,
getPitch,
norm_interp_f0,
)
# Create a class to test the ComputePitch class
class TestComputeYin(unittest.TestCase):
def setUp(self):
np.random.seed(0)
def test_difference_function(self):
# Test the differenceFunction function with a simple example
x = np.array([0.0, 1.0, 2.0, 3.0, 4.0])
N = len(x)
tau_max = 3
# Test the output shape
expected_shape = (tau_max,)
actual_shape = differenceFunction(x, N, tau_max).shape
self.assertTrue(actual_shape == expected_shape)
# Test the output values
expected_output = np.array([0.0, 4.0, 12.0])
actual_output = differenceFunction(x, N, tau_max)
np.testing.assert_equal(actual_output, expected_output)
# Test the function with a larger example
x = np.random.randn(1000)
N = len(x)
tau_max = 100
# Test the output shape
expected_shape = (tau_max,)
actual_shape = differenceFunction(x, N, tau_max).shape
self.assertTrue(actual_shape == expected_shape)
# Test the output values
expected_output = np.zeros(tau_max)
for tau in range(1, tau_max):
expected_output[tau] = np.sum((x[: N - tau] - x[tau:N]) ** 2)
actual_output = differenceFunction(x, N, tau_max)
np.testing.assert_allclose(actual_output, expected_output, rtol=1e-6, atol=1e-6)
def test_get_pitch(self):
# Test the getPitch function with a simple example
cmdf = np.array([1.0, 0.5, 0.2, 0.1, 0.05])
tau_min = 1
tau_max = 5
harmo_th = 0.3
# Test the output value when there is a value below the threshold
expected_output = 4
actual_output = getPitch(cmdf, tau_min, tau_max, harmo_th)
self.assertEqual(actual_output, expected_output)
# Test the output value when there are no values below the threshold
cmdf = np.array([1.0, 0.9, 0.8, 0.7, 0.6])
expected_output = 0
actual_output = getPitch(cmdf, tau_min, tau_max, harmo_th)
self.assertEqual(actual_output, expected_output)
# Test the output value when there are no values below the threshold
cmdf = np.random.rand(100)
cmdf[tau_min:tau_max] = 0.5
expected_output = 0
actual_output = getPitch(cmdf, tau_min, tau_max, harmo_th)
self.assertEqual(actual_output, expected_output)
def test_cumulative_mean_normalized_difference_function(self):
# Test the function with a simple example
df = np.array([1, 2, 3, 4, 5])
N = len(df)
# Test the output values
expected_output = np.array([1.0, 1.0, 1.2, 1.333333, 1.428571])
actual_output = cumulativeMeanNormalizedDifferenceFunction(df, N)
np.testing.assert_allclose(actual_output, expected_output, rtol=1e-6, atol=1e-6)
def test_compute_yin(self):
# Test the function with a simple example
sig = torch.from_numpy(np.sin(2 * np.pi * 440 * np.arange(44100) / 44100))
sr = 44100
actual_output = compute_yin(sig, sr)
# Check the result
expected_output = np.load("mocks/test_compute_yin.npy")
np.testing.assert_allclose(actual_output, expected_output)
def test_norm_interp_f0(self):
# Test the norm_interp_f0 function with a simple example
f0 = np.array([0, 100, 0, 200, 0])
actual_output = norm_interp_f0(f0)
expected_output = (
np.array([100, 100, 150, 200, 200]),
np.array([True, False, True, False, True]),
)
np.testing.assert_allclose(actual_output[0], expected_output[0])
# Test the norm_interp_f0 function with a zero-dimensional array
np.testing.assert_array_equal(actual_output[1], expected_output[1])
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