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import math | |
import os | |
import unittest | |
from dataclasses import dataclass | |
import librosa | |
import numpy as np | |
from coqpit import Coqpit | |
from tests import get_tests_input_path, get_tests_output_path, get_tests_path | |
from TTS.utils.audio import numpy_transforms as np_transforms | |
TESTS_PATH = get_tests_path() | |
OUT_PATH = os.path.join(get_tests_output_path(), "audio_tests") | |
WAV_FILE = os.path.join(get_tests_input_path(), "example_1.wav") | |
os.makedirs(OUT_PATH, exist_ok=True) | |
# pylint: disable=no-self-use | |
class TestNumpyTransforms(unittest.TestCase): | |
def setUp(self) -> None: | |
class AudioConfig(Coqpit): | |
sample_rate: int = 22050 | |
fft_size: int = 1024 | |
num_mels: int = 256 | |
mel_fmax: int = 1800 | |
mel_fmin: int = 0 | |
hop_length: int = 256 | |
win_length: int = 1024 | |
pitch_fmax: int = 640 | |
pitch_fmin: int = 1 | |
trim_db: int = -1 | |
min_silence_sec: float = 0.01 | |
gain: float = 1.0 | |
base: float = 10.0 | |
self.config = AudioConfig() | |
self.sample_wav, _ = librosa.load(WAV_FILE, sr=self.config.sample_rate) | |
def test_build_mel_basis(self): | |
"""Check if the mel basis is correctly built""" | |
print(" > Testing mel basis building.") | |
mel_basis = np_transforms.build_mel_basis(**self.config) | |
self.assertEqual(mel_basis.shape, (self.config.num_mels, self.config.fft_size // 2 + 1)) | |
def test_millisec_to_length(self): | |
"""Check if the conversion from milliseconds to length is correct""" | |
print(" > Testing millisec to length conversion.") | |
win_len, hop_len = np_transforms.millisec_to_length( | |
frame_length_ms=1000, frame_shift_ms=12.5, sample_rate=self.config.sample_rate | |
) | |
self.assertEqual(hop_len, int(12.5 / 1000.0 * self.config.sample_rate)) | |
self.assertEqual(win_len, self.config.sample_rate) | |
def test_amplitude_db_conversion(self): | |
di = np.random.rand(11) | |
o1 = np_transforms.amp_to_db(x=di, gain=1.0, base=10) | |
o2 = np_transforms.db_to_amp(x=o1, gain=1.0, base=10) | |
np.testing.assert_almost_equal(di, o2, decimal=5) | |
def test_preemphasis_deemphasis(self): | |
di = np.random.rand(11) | |
o1 = np_transforms.preemphasis(x=di, coeff=0.95) | |
o2 = np_transforms.deemphasis(x=o1, coeff=0.95) | |
np.testing.assert_almost_equal(di, o2, decimal=5) | |
def test_spec_to_mel(self): | |
mel_basis = np_transforms.build_mel_basis(**self.config) | |
spec = np.random.rand(self.config.fft_size // 2 + 1, 20) # [C, T] | |
mel = np_transforms.spec_to_mel(spec=spec, mel_basis=mel_basis) | |
self.assertEqual(mel.shape, (self.config.num_mels, 20)) | |
def mel_to_spec(self): | |
mel_basis = np_transforms.build_mel_basis(**self.config) | |
mel = np.random.rand(self.config.num_mels, 20) # [C, T] | |
spec = np_transforms.mel_to_spec(mel=mel, mel_basis=mel_basis) | |
self.assertEqual(spec.shape, (self.config.fft_size // 2 + 1, 20)) | |
def test_wav_to_spec(self): | |
spec = np_transforms.wav_to_spec(wav=self.sample_wav, **self.config) | |
self.assertEqual( | |
spec.shape, (self.config.fft_size // 2 + 1, math.ceil(self.sample_wav.shape[0] / self.config.hop_length)) | |
) | |
def test_wav_to_mel(self): | |
mel_basis = np_transforms.build_mel_basis(**self.config) | |
mel = np_transforms.wav_to_mel(wav=self.sample_wav, mel_basis=mel_basis, **self.config) | |
self.assertEqual( | |
mel.shape, (self.config.num_mels, math.ceil(self.sample_wav.shape[0] / self.config.hop_length)) | |
) | |
def test_compute_f0(self): | |
pitch = np_transforms.compute_f0(x=self.sample_wav, **self.config) | |
mel_basis = np_transforms.build_mel_basis(**self.config) | |
mel = np_transforms.wav_to_mel(wav=self.sample_wav, mel_basis=mel_basis, **self.config) | |
assert pitch.shape[0] == mel.shape[1] | |
def test_load_wav(self): | |
wav = np_transforms.load_wav(filename=WAV_FILE, resample=False, sample_rate=22050) | |
wav_resample = np_transforms.load_wav(filename=WAV_FILE, resample=True, sample_rate=16000) | |
self.assertEqual(wav.shape, (self.sample_wav.shape[0],)) | |
self.assertNotEqual(wav_resample.shape, (self.sample_wav.shape[0],)) | |