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Unverified Commit bec90472 authored by Aziz's avatar Aziz Committed by GitHub
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Redactor librosa compatibility test (#1259)

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test/torchaudio_unittest/functional/librosa_compatibility_test.py 0 → 100644
View file @ bec90472
import itertools
import unittest
from distutils.version import StrictVersion
import torch
from parameterized import parameterized
import torchaudio.functional as F
from torchaudio._internal.module_utils import is_module_available
LIBROSA_AVAILABLE = is_module_available('librosa')
if LIBROSA_AVAILABLE:
import numpy as np
import librosa
from torchaudio_unittest import common_utils
@unittest.skipIf(not LIBROSA_AVAILABLE, "Librosa not available")
class TestFunctional(common_utils.TorchaudioTestCase):
"""Test suite for functions in `functional` module."""
def test_griffinlim(self):
# NOTE: This test is flaky without a fixed random seed
# See https://github.com/pytorch/audio/issues/382
torch.random.manual_seed(42)
tensor = torch.rand((1, 1000))
n_fft = 400
ws = 400
hop = 100
window = torch.hann_window(ws)
normalize = False
momentum = 0.99
n_iter = 8
length = 1000
rand_init = False
init = 'random' if rand_init else None
specgram = F.spectrogram(tensor, 0, window, n_fft, hop, ws, 2, normalize).sqrt()
ta_out = F.griffinlim(specgram, window, n_fft, hop, ws, 1, normalize,
n_iter, momentum, length, rand_init)
lr_out = librosa.griffinlim(specgram.squeeze(0).numpy(), n_iter=n_iter, hop_length=hop,
momentum=momentum, init=init, length=length)
lr_out = torch.from_numpy(lr_out).unsqueeze(0)
self.assertEqual(ta_out, lr_out, atol=5e-5, rtol=1e-5)
def _test_create_fb(self, n_mels=40, sample_rate=22050, n_fft=2048, fmin=0.0, fmax=8000.0, norm=None):
librosa_fb = librosa.filters.mel(sr=sample_rate,
n_fft=n_fft,
n_mels=n_mels,
fmax=fmax,
fmin=fmin,
htk=True,
norm=norm)
fb = F.create_fb_matrix(sample_rate=sample_rate,
n_mels=n_mels,
f_max=fmax,
f_min=fmin,
n_freqs=(n_fft // 2 + 1),
norm=norm)
for i_mel_bank in range(n_mels):
self.assertEqual(
fb[:, i_mel_bank], torch.tensor(librosa_fb[i_mel_bank]), atol=1e-4, rtol=1e-5)
def test_create_fb(self):
self._test_create_fb()
self._test_create_fb(n_mels=128, sample_rate=44100)
self._test_create_fb(n_mels=128, fmin=2000.0, fmax=5000.0)
self._test_create_fb(n_mels=56, fmin=100.0, fmax=9000.0)
self._test_create_fb(n_mels=56, fmin=800.0, fmax=900.0)
self._test_create_fb(n_mels=56, fmin=1900.0, fmax=900.0)
self._test_create_fb(n_mels=10, fmin=1900.0, fmax=900.0)
if StrictVersion(librosa.__version__) < StrictVersion("0.7.2"):
return
self._test_create_fb(n_mels=128, sample_rate=44100, norm="slaney")
self._test_create_fb(n_mels=128, fmin=2000.0, fmax=5000.0, norm="slaney")
self._test_create_fb(n_mels=56, fmin=100.0, fmax=9000.0, norm="slaney")
self._test_create_fb(n_mels=56, fmin=800.0, fmax=900.0, norm="slaney")
self._test_create_fb(n_mels=56, fmin=1900.0, fmax=900.0, norm="slaney")
self._test_create_fb(n_mels=10, fmin=1900.0, fmax=900.0, norm="slaney")
def test_amplitude_to_DB(self):
spec = torch.rand((6, 201))
amin = 1e-10
db_multiplier = 0.0
top_db = 80.0
# Power to DB
multiplier = 10.0
ta_out = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db)
lr_out = librosa.core.power_to_db(spec.numpy())
lr_out = torch.from_numpy(lr_out)
self.assertEqual(ta_out, lr_out, atol=5e-5, rtol=1e-5)
# Amplitude to DB
multiplier = 20.0
ta_out = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db)
lr_out = librosa.core.amplitude_to_db(spec.numpy())
lr_out = torch.from_numpy(lr_out)
self.assertEqual(ta_out, lr_out, atol=5e-5, rtol=1e-5)
@unittest.skipIf(not LIBROSA_AVAILABLE, "Librosa not available")
class TestPhaseVocoder(common_utils.TorchaudioTestCase):
@parameterized.expand(list(itertools.product(
[(2, 1025, 400, 2)],
[0.5, 1.01, 1.3],
[256]
)))
def test_phase_vocoder(self, shape, rate, hop_length):
# Due to cummulative sum, numerical error in using torch.float32 will
# result in bottom right values of the stretched sectrogram to not
# match with librosa.
torch.random.manual_seed(42)
complex_specgrams = torch.randn(*shape)
complex_specgrams = complex_specgrams.type(torch.float64)
phase_advance = torch.linspace(
0,
np.pi * hop_length,
complex_specgrams.shape[-3],
dtype=torch.float64)[..., None]
complex_specgrams_stretch = F.phase_vocoder(complex_specgrams, rate=rate, phase_advance=phase_advance)
# == Test shape
expected_size = list(complex_specgrams.size())
expected_size[-2] = int(np.ceil(expected_size[-2] / rate))
assert complex_specgrams.dim() == complex_specgrams_stretch.dim()
assert complex_specgrams_stretch.size() == torch.Size(expected_size)
# == Test values
index = [0] * (complex_specgrams.dim() - 3) + [slice(None)] * 3
mono_complex_specgram = complex_specgrams[index].numpy()
mono_complex_specgram = mono_complex_specgram[..., 0] + \
mono_complex_specgram[..., 1] * 1j
expected_complex_stretch = librosa.phase_vocoder(
mono_complex_specgram,
rate=rate,
hop_length=hop_length)
complex_stretch = complex_specgrams_stretch[index].numpy()
complex_stretch = complex_stretch[..., 0] + 1j * complex_stretch[..., 1]
self.assertEqual(complex_stretch, torch.from_numpy(expected_complex_stretch), atol=1e-5, rtol=1e-5)
test/torchaudio_unittest/librosa_compatibility_test.py
View file @ bec90472
"""Test suites for numerical compatibility with librosa""" """Test suites for numerical compatibility with librosa"""
import os import os
import unittest import unittest
from distutils.version import StrictVersion
import torch import torch
import torchaudio import torchaudio
import torchaudio.functional as F import torchaudio.functional as F
from torchaudio._internal.module_utils import is_module_available from torchaudio._internal.module_utils import is_module_available
from parameterized import parameterized, param from parameterized import parameterized, param
import itertools
LIBROSA_AVAILABLE = is_module_available('librosa') LIBROSA_AVAILABLE = is_module_available('librosa')
if LIBROSA_AVAILABLE: if LIBROSA_AVAILABLE:
import numpy as np
import librosa import librosa
import scipy import scipy
from torchaudio_unittest import common_utils from torchaudio_unittest import common_utils
@unittest.skipIf(not LIBROSA_AVAILABLE, "Librosa not available")
class TestFunctional(common_utils.TorchaudioTestCase):
"""Test suite for functions in `functional` module."""
def test_griffinlim(self):
# NOTE: This test is flaky without a fixed random seed
# See https://github.com/pytorch/audio/issues/382
torch.random.manual_seed(42)
tensor = torch.rand((1, 1000))
n_fft = 400
ws = 400
hop = 100
window = torch.hann_window(ws)
normalize = False
momentum = 0.99
n_iter = 8
length = 1000
rand_init = False
init = 'random' if rand_init else None
specgram = F.spectrogram(tensor, 0, window, n_fft, hop, ws, 2, normalize).sqrt()
ta_out = F.griffinlim(specgram, window, n_fft, hop, ws, 1, normalize,
n_iter, momentum, length, rand_init)
lr_out = librosa.griffinlim(specgram.squeeze(0).numpy(), n_iter=n_iter, hop_length=hop,
momentum=momentum, init=init, length=length)
lr_out = torch.from_numpy(lr_out).unsqueeze(0)
self.assertEqual(ta_out, lr_out, atol=5e-5, rtol=1e-5)
def _test_create_fb(self, n_mels=40, sample_rate=22050, n_fft=2048, fmin=0.0, fmax=8000.0, norm=None):
librosa_fb = librosa.filters.mel(sr=sample_rate,
n_fft=n_fft,
n_mels=n_mels,
fmax=fmax,
fmin=fmin,
htk=True,
norm=norm)
fb = F.create_fb_matrix(sample_rate=sample_rate,
n_mels=n_mels,
f_max=fmax,
f_min=fmin,
n_freqs=(n_fft // 2 + 1),
norm=norm)
for i_mel_bank in range(n_mels):
self.assertEqual(
fb[:, i_mel_bank], torch.tensor(librosa_fb[i_mel_bank]), atol=1e-4, rtol=1e-5)
def test_create_fb(self):
self._test_create_fb()
self._test_create_fb(n_mels=128, sample_rate=44100)
self._test_create_fb(n_mels=128, fmin=2000.0, fmax=5000.0)
self._test_create_fb(n_mels=56, fmin=100.0, fmax=9000.0)
self._test_create_fb(n_mels=56, fmin=800.0, fmax=900.0)
self._test_create_fb(n_mels=56, fmin=1900.0, fmax=900.0)
self._test_create_fb(n_mels=10, fmin=1900.0, fmax=900.0)
if StrictVersion(librosa.__version__) < StrictVersion("0.7.2"):
return
self._test_create_fb(n_mels=128, sample_rate=44100, norm="slaney")
self._test_create_fb(n_mels=128, fmin=2000.0, fmax=5000.0, norm="slaney")
self._test_create_fb(n_mels=56, fmin=100.0, fmax=9000.0, norm="slaney")
self._test_create_fb(n_mels=56, fmin=800.0, fmax=900.0, norm="slaney")
self._test_create_fb(n_mels=56, fmin=1900.0, fmax=900.0, norm="slaney")
self._test_create_fb(n_mels=10, fmin=1900.0, fmax=900.0, norm="slaney")
def test_amplitude_to_DB(self):
spec = torch.rand((6, 201))
amin = 1e-10
db_multiplier = 0.0
top_db = 80.0
# Power to DB
multiplier = 10.0
ta_out = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db)
lr_out = librosa.core.power_to_db(spec.numpy())
lr_out = torch.from_numpy(lr_out)
self.assertEqual(ta_out, lr_out, atol=5e-5, rtol=1e-5)
# Amplitude to DB
multiplier = 20.0
ta_out = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db)
lr_out = librosa.core.amplitude_to_db(spec.numpy())
lr_out = torch.from_numpy(lr_out)
self.assertEqual(ta_out, lr_out, atol=5e-5, rtol=1e-5)
@unittest.skipIf(not LIBROSA_AVAILABLE, "Librosa not available")
class TestPhaseVocoder(common_utils.TorchaudioTestCase):
@parameterized.expand(list(itertools.product(
[(2, 1025, 400, 2)],
[0.5, 1.01, 1.3],
[256]
)))
def test_phase_vocoder(self, shape, rate, hop_length):
# Due to cummulative sum, numerical error in using torch.float32 will
# result in bottom right values of the stretched sectrogram to not
# match with librosa.
torch.random.manual_seed(42)
complex_specgrams = torch.randn(*shape)
complex_specgrams = complex_specgrams.type(torch.float64)
phase_advance = torch.linspace(
0,
np.pi * hop_length,
complex_specgrams.shape[-3],
dtype=torch.float64)[..., None]
complex_specgrams_stretch = F.phase_vocoder(complex_specgrams, rate=rate, phase_advance=phase_advance)
# == Test shape
expected_size = list(complex_specgrams.size())
expected_size[-2] = int(np.ceil(expected_size[-2] / rate))
assert complex_specgrams.dim() == complex_specgrams_stretch.dim()
assert complex_specgrams_stretch.size() == torch.Size(expected_size)
# == Test values
index = [0] * (complex_specgrams.dim() - 3) + [slice(None)] * 3
mono_complex_specgram = complex_specgrams[index].numpy()
mono_complex_specgram = mono_complex_specgram[..., 0] + \
mono_complex_specgram[..., 1] * 1j
expected_complex_stretch = librosa.phase_vocoder(
mono_complex_specgram,
rate=rate,
hop_length=hop_length)
complex_stretch = complex_specgrams_stretch[index].numpy()
complex_stretch = complex_stretch[..., 0] + 1j * complex_stretch[..., 1]
self.assertEqual(complex_stretch, torch.from_numpy(expected_complex_stretch), atol=1e-5, rtol=1e-5)
def _load_audio_asset(*asset_paths, **kwargs): def _load_audio_asset(*asset_paths, **kwargs):
file_path = common_utils.get_asset_path(*asset_paths) file_path = common_utils.get_asset_path(*asset_paths)
sound, sample_rate = torchaudio.load(file_path, **kwargs) sound, sample_rate = torchaudio.load(file_path, **kwargs)
......
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