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"""Test numerical consistency among single input and batched input."""
import os
import torch
from parameterized import parameterized
from torchaudio import transforms as T
from torchaudio_unittest import common_utils
class TestTransforms(common_utils.TorchaudioTestCase):
"""Test suite for classes defined in `transforms` module"""
backend = "default"
def assert_batch_consistency(self, transform, batch, *args, atol=1e-8, rtol=1e-5, seed=42, **kwargs):
n = batch.size(0)
# Compute items separately, then batch the result
torch.random.manual_seed(seed)
items_input = batch.clone()
items_result = torch.stack([transform(items_input[i], *args, **kwargs) for i in range(n)])
# Batch the input and run
torch.random.manual_seed(seed)
batch_input = batch.clone()
batch_result = transform(batch_input, *args, **kwargs)
self.assertEqual(items_input, batch_input, rtol=rtol, atol=atol)
self.assertEqual(items_result, batch_result, rtol=rtol, atol=atol)
def test_batch_AmplitudeToDB(self):
spec = torch.rand((3, 2, 6, 201))
transform = T.AmplitudeToDB()
self.assert_batch_consistency(transform, spec)
def test_batch_Resample(self):
waveform = torch.randn(3, 2, 2786)
transform = T.Resample()
self.assert_batch_consistency(transform, waveform)
def test_batch_MelScale(self):
specgram = torch.randn(3, 2, 201, 256)
atol = 1e-6 if os.name == "nt" else 1e-8
transform = T.MelScale()
self.assert_batch_consistency(transform, specgram, atol=atol)
def test_batch_InverseMelScale(self):
n_mels = 32
n_stft = 5
mel_spec = torch.randn(3, 2, n_mels, 32) ** 2
transform = T.InverseMelScale(n_stft, n_mels)
# Because InverseMelScale runs SGD on randomly initialized values so they do not yield
# exactly same result. For this reason, tolerance is very relaxed here.
self.assert_batch_consistency(transform, mel_spec, atol=1.0, rtol=1e-5)
def test_batch_compute_deltas(self):
specgram = torch.randn(3, 2, 31, 2786)
transform = T.ComputeDeltas()
self.assert_batch_consistency(transform, specgram)
def test_batch_mulaw(self):
waveform = common_utils.get_whitenoise(sample_rate=8000, duration=1, n_channels=6)
waveform = waveform.reshape(3, 2, -1)
# Single then transform then batch
expected = [T.MuLawEncoding()(waveform[i]) for i in range(3)]
expected = torch.stack(expected)
# Batch then transform
computed = T.MuLawEncoding()(waveform)
# shape = (3, 2, 201, 1394)
self.assertEqual(computed, expected)
# Single then transform then batch
expected_decoded = [T.MuLawDecoding()(expected[i]) for i in range(3)]
expected_decoded = torch.stack(expected_decoded)
# Batch then transform
computed_decoded = T.MuLawDecoding()(computed)
# shape = (3, 2, 201, 1394)
self.assertEqual(computed_decoded, expected_decoded)
def test_batch_spectrogram(self):
waveform = common_utils.get_whitenoise(sample_rate=8000, duration=1, n_channels=6)
waveform = waveform.reshape(3, 2, -1)
transform = T.Spectrogram()
self.assert_batch_consistency(transform, waveform)
def test_batch_inverse_spectrogram(self):
waveform = common_utils.get_whitenoise(sample_rate=8000, duration=1, n_channels=6)
specgram = common_utils.get_spectrogram(waveform, n_fft=400)
specgram = specgram.reshape(3, 2, specgram.shape[-2], specgram.shape[-1])
transform = T.InverseSpectrogram(n_fft=400)
self.assert_batch_consistency(transform, specgram)
def test_batch_melspectrogram(self):
waveform = common_utils.get_whitenoise(sample_rate=8000, duration=1, n_channels=6)
waveform = waveform.reshape(3, 2, -1)
transform = T.MelSpectrogram()
self.assert_batch_consistency(transform, waveform)
def test_batch_mfcc(self):
waveform = common_utils.get_whitenoise(sample_rate=8000, duration=1, n_channels=6)
waveform = waveform.reshape(3, 2, -1)
transform = T.MFCC()
self.assert_batch_consistency(transform, waveform, atol=1e-4, rtol=1e-5)
def test_batch_lfcc(self):
waveform = common_utils.get_whitenoise(sample_rate=8000, duration=1, n_channels=6)
waveform = waveform.reshape(3, 2, -1)
transform = T.LFCC()
self.assert_batch_consistency(transform, waveform, atol=1e-4, rtol=1e-5)
def test_batch_TimeStretch(self):
rate = 2
num_freq = 1025
batch = 3
tensor = common_utils.get_whitenoise(sample_rate=8000, n_channels=batch)
spec = common_utils.get_spectrogram(tensor, n_fft=num_freq)
transform = T.TimeStretch(fixed_rate=rate, n_freq=num_freq // 2 + 1, hop_length=512)
self.assert_batch_consistency(transform, spec, atol=1e-5, rtol=1e-5)
def test_batch_Fade(self):
waveform = common_utils.get_whitenoise(sample_rate=8000, duration=1, n_channels=6)
waveform = waveform.reshape(3, 2, -1)
fade_in_len = 3000
fade_out_len = 3000
transform = T.Fade(fade_in_len, fade_out_len)
self.assert_batch_consistency(transform, waveform)
def test_batch_Vol(self):
waveform = common_utils.get_whitenoise(sample_rate=8000, duration=1, n_channels=6)
waveform = waveform.reshape(3, 2, -1)
transform = T.Vol(gain=1.1)
self.assert_batch_consistency(transform, waveform)
def test_batch_spectral_centroid(self):
sample_rate = 44100
waveform = common_utils.get_whitenoise(sample_rate=sample_rate, n_channels=6)
waveform = waveform.reshape(3, 2, -1)
transform = T.SpectralCentroid(sample_rate)
self.assert_batch_consistency(transform, waveform)
def test_batch_pitch_shift(self):
sample_rate = 8000
n_steps = -2
waveform = common_utils.get_whitenoise(sample_rate=sample_rate, duration=0.05, n_channels=6)
waveform = waveform.reshape(3, 2, -1)
transform = T.PitchShift(sample_rate, n_steps, n_fft=400)
self.assert_batch_consistency(transform, waveform)
def test_batch_PSD(self):
waveform = common_utils.get_whitenoise(sample_rate=8000, duration=1, n_channels=6)
specgram = common_utils.get_spectrogram(waveform, n_fft=400)
specgram = specgram.reshape(3, 2, specgram.shape[-2], specgram.shape[-1])
transform = T.PSD()
self.assert_batch_consistency(transform, specgram)
def test_batch_PSD_with_mask(self):
waveform = common_utils.get_whitenoise(sample_rate=8000, duration=1, n_channels=6)
waveform = waveform.to(torch.double)
specgram = common_utils.get_spectrogram(waveform, n_fft=400)
specgram = specgram.reshape(3, 2, specgram.shape[-2], specgram.shape[-1])
mask = torch.rand((3, specgram.shape[-2], specgram.shape[-1]))
transform = T.PSD()
# Single then transform then batch
expected = [transform(specgram[i], mask[i]) for i in range(3)]
expected = torch.stack(expected)
# Batch then transform
computed = transform(specgram, mask)
self.assertEqual(computed, expected)
@parameterized.expand(
[
[True],
[False],
]
)
def test_MVDR(self, multi_mask):
waveform = common_utils.get_whitenoise(sample_rate=8000, duration=1, n_channels=6)
specgram = common_utils.get_spectrogram(waveform, n_fft=400)
specgram = specgram.reshape(3, 2, specgram.shape[-2], specgram.shape[-1])
if multi_mask:
mask_s = torch.rand((3, 2, specgram.shape[-2], specgram.shape[-1]))
mask_n = torch.rand((3, 2, specgram.shape[-2], specgram.shape[-1]))
else:
mask_s = torch.rand((3, specgram.shape[-2], specgram.shape[-1]))
mask_n = torch.rand((3, specgram.shape[-2], specgram.shape[-1]))
transform = T.MVDR(multi_mask=multi_mask)
# Single then transform then batch
expected = [transform(specgram[i], mask_s[i], mask_n[i]) for i in range(3)]
expected = torch.stack(expected)
# Batch then transform
computed = transform(specgram, mask_s, mask_n)
self.assertEqual(computed, expected)
def test_rtf_mvdr(self):
waveform = common_utils.get_whitenoise(sample_rate=8000, duration=1, n_channels=6)
specgram = common_utils.get_spectrogram(waveform, n_fft=400)
batch_size, channel, freq, time = 3, 2, specgram.shape[-2], specgram.shape[-1]
specgram = specgram.reshape(batch_size, channel, freq, time)
rtf = torch.rand(batch_size, freq, channel, dtype=torch.cfloat)
psd_n = torch.rand(batch_size, freq, channel, channel, dtype=torch.cfloat)
reference_channel = 0
transform = T.RTFMVDR()
# Single then transform then batch
expected = [transform(specgram[i], rtf[i], psd_n[i], reference_channel) for i in range(batch_size)]
expected = torch.stack(expected)
# Batch then transform
computed = transform(specgram, rtf, psd_n, reference_channel)
self.assertEqual(computed, expected)
def test_souden_mvdr(self):
waveform = common_utils.get_whitenoise(sample_rate=8000, duration=1, n_channels=6)
specgram = common_utils.get_spectrogram(waveform, n_fft=400)
batch_size, channel, freq, time = 3, 2, specgram.shape[-2], specgram.shape[-1]
specgram = specgram.reshape(batch_size, channel, freq, time)
psd_s = torch.rand(batch_size, freq, channel, channel, dtype=torch.cfloat)
psd_n = torch.rand(batch_size, freq, channel, channel, dtype=torch.cfloat)
reference_channel = 0
transform = T.SoudenMVDR()
# Single then transform then batch
expected = [transform(specgram[i], psd_s[i], psd_n[i], reference_channel) for i in range(batch_size)]
expected = torch.stack(expected)
# Batch then transform
computed = transform(specgram, psd_s, psd_n, reference_channel)
self.assertEqual(computed, expected)
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