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import math
import unittest
import torch
import torchaudio
import torchaudio.transforms as transforms
import torchaudio.functional as F
from torchaudio_unittest import common_utils
class Tester(common_utils.TorchaudioTestCase):
backend = 'default'
# create a sinewave signal for testing
sample_rate = 16000
freq = 440
volume = .3
waveform = (torch.cos(2 * math.pi * torch.arange(0, 4 * sample_rate).float() * freq / sample_rate))
waveform.unsqueeze_(0) # (1, 64000)
waveform = (waveform * volume * 2**31).long()
def scale(self, waveform, factor=2.0**31):
# scales a waveform by a factor
if not waveform.is_floating_point():
waveform = waveform.to(torch.get_default_dtype())
return waveform / factor
def test_mu_law_companding(self):
quantization_channels = 256
waveform = self.waveform.clone()
if not waveform.is_floating_point():
waveform = waveform.to(torch.get_default_dtype())
waveform /= torch.abs(waveform).max()
self.assertTrue(waveform.min() >= -1. and waveform.max() <= 1.)
waveform_mu = transforms.MuLawEncoding(quantization_channels)(waveform)
self.assertTrue(waveform_mu.min() >= 0. and waveform_mu.max() <= quantization_channels)
waveform_exp = transforms.MuLawDecoding(quantization_channels)(waveform_mu)
self.assertTrue(waveform_exp.min() >= -1. and waveform_exp.max() <= 1.)
def test_AmplitudeToDB(self):
filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav')
waveform = common_utils.load_wav(filepath)[0]
mag_to_db_transform = transforms.AmplitudeToDB('magnitude', 80.)
power_to_db_transform = transforms.AmplitudeToDB('power', 80.)
mag_to_db_torch = mag_to_db_transform(torch.abs(waveform))
power_to_db_torch = power_to_db_transform(torch.pow(waveform, 2))
self.assertEqual(mag_to_db_torch, power_to_db_torch)
def test_melscale_load_save(self):
specgram = torch.ones(1, 1000, 100)
melscale_transform = transforms.MelScale()
melscale_transform(specgram)
melscale_transform_copy = transforms.MelScale(n_stft=1000)
melscale_transform_copy.load_state_dict(melscale_transform.state_dict())
fb = melscale_transform.fb
fb_copy = melscale_transform_copy.fb
self.assertEqual(fb_copy.size(), (1000, 128))
self.assertEqual(fb, fb_copy)
def test_melspectrogram_load_save(self):
waveform = self.waveform.float()
mel_spectrogram_transform = transforms.MelSpectrogram()
mel_spectrogram_transform(waveform)
mel_spectrogram_transform_copy = transforms.MelSpectrogram()
mel_spectrogram_transform_copy.load_state_dict(mel_spectrogram_transform.state_dict())
window = mel_spectrogram_transform.spectrogram.window
window_copy = mel_spectrogram_transform_copy.spectrogram.window
fb = mel_spectrogram_transform.mel_scale.fb
fb_copy = mel_spectrogram_transform_copy.mel_scale.fb
self.assertEqual(window, window_copy)
# the default for n_fft = 400 and n_mels = 128
self.assertEqual(fb_copy.size(), (201, 128))
self.assertEqual(fb, fb_copy)
def test_mel2(self):
top_db = 80.
s2db = transforms.AmplitudeToDB('power', top_db)
waveform = self.waveform.clone() # (1, 16000)
waveform_scaled = self.scale(waveform) # (1, 16000)
mel_transform = transforms.MelSpectrogram()
# check defaults
spectrogram_torch = s2db(mel_transform(waveform_scaled)) # (1, 128, 321)
self.assertTrue(spectrogram_torch.dim() == 3)
self.assertTrue(spectrogram_torch.ge(spectrogram_torch.max() - top_db).all())
self.assertEqual(spectrogram_torch.size(1), mel_transform.n_mels)
# check correctness of filterbank conversion matrix
self.assertTrue(mel_transform.mel_scale.fb.sum(1).le(1.).all())
self.assertTrue(mel_transform.mel_scale.fb.sum(1).ge(0.).all())
# check options
kwargs = {'window_fn': torch.hamming_window, 'pad': 10, 'win_length': 500,
'hop_length': 125, 'n_fft': 800, 'n_mels': 50}
mel_transform2 = transforms.MelSpectrogram(**kwargs)
spectrogram2_torch = s2db(mel_transform2(waveform_scaled)) # (1, 50, 513)
self.assertTrue(spectrogram2_torch.dim() == 3)
self.assertTrue(spectrogram_torch.ge(spectrogram_torch.max() - top_db).all())
self.assertEqual(spectrogram2_torch.size(1), mel_transform2.n_mels)
self.assertTrue(mel_transform2.mel_scale.fb.sum(1).le(1.).all())
self.assertTrue(mel_transform2.mel_scale.fb.sum(1).ge(0.).all())
# check on multi-channel audio
filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav')
x_stereo = common_utils.load_wav(filepath)[0] # (2, 278756), 44100
spectrogram_stereo = s2db(mel_transform(x_stereo)) # (2, 128, 1394)
self.assertTrue(spectrogram_stereo.dim() == 3)
self.assertTrue(spectrogram_stereo.size(0) == 2)
self.assertTrue(spectrogram_torch.ge(spectrogram_torch.max() - top_db).all())
self.assertEqual(spectrogram_stereo.size(1), mel_transform.n_mels)
# check filterbank matrix creation
fb_matrix_transform = transforms.MelScale(
n_mels=100, sample_rate=16000, f_min=0., f_max=None, n_stft=400)
self.assertTrue(fb_matrix_transform.fb.sum(1).le(1.).all())
self.assertTrue(fb_matrix_transform.fb.sum(1).ge(0.).all())
self.assertEqual(fb_matrix_transform.fb.size(), (400, 100))
def test_mfcc(self):
audio_orig = self.waveform.clone()
audio_scaled = self.scale(audio_orig) # (1, 16000)
sample_rate = 16000
n_mfcc = 40
n_mels = 128
mfcc_transform = torchaudio.transforms.MFCC(sample_rate=sample_rate,
n_mfcc=n_mfcc,
norm='ortho')
# check defaults
torch_mfcc = mfcc_transform(audio_scaled) # (1, 40, 321)
self.assertTrue(torch_mfcc.dim() == 3)
self.assertTrue(torch_mfcc.shape[1] == n_mfcc)
self.assertTrue(torch_mfcc.shape[2] == 321)
# check melkwargs are passed through
melkwargs = {'win_length': 200}
mfcc_transform2 = torchaudio.transforms.MFCC(sample_rate=sample_rate,
n_mfcc=n_mfcc,
norm='ortho',
melkwargs=melkwargs)
torch_mfcc2 = mfcc_transform2(audio_scaled) # (1, 40, 641)
self.assertTrue(torch_mfcc2.shape[2] == 641)
# check norms work correctly
mfcc_transform_norm_none = torchaudio.transforms.MFCC(sample_rate=sample_rate,
n_mfcc=n_mfcc,
norm=None)
torch_mfcc_norm_none = mfcc_transform_norm_none(audio_scaled) # (1, 40, 321)
norm_check = torch_mfcc.clone()
norm_check[:, 0, :] *= math.sqrt(n_mels) * 2
norm_check[:, 1:, :] *= math.sqrt(n_mels / 2) * 2
self.assertTrue(torch_mfcc_norm_none.allclose(norm_check))
def test_resample_size(self):
input_path = common_utils.get_asset_path('sinewave.wav')
waveform, sample_rate = common_utils.load_wav(input_path)
upsample_rate = sample_rate * 2
downsample_rate = sample_rate // 2
invalid_resample = torchaudio.transforms.Resample(sample_rate, upsample_rate, resampling_method='foo')
self.assertRaises(ValueError, invalid_resample, waveform)
upsample_resample = torchaudio.transforms.Resample(
sample_rate, upsample_rate, resampling_method='sinc_interpolation')
up_sampled = upsample_resample(waveform)
# we expect the upsampled signal to have twice as many samples
self.assertTrue(up_sampled.size(-1) == waveform.size(-1) * 2)
downsample_resample = torchaudio.transforms.Resample(
sample_rate, downsample_rate, resampling_method='sinc_interpolation')
down_sampled = downsample_resample(waveform)
# we expect the downsampled signal to have half as many samples
self.assertTrue(down_sampled.size(-1) == waveform.size(-1) // 2)
def test_compute_deltas(self):
channel = 13
n_mfcc = channel * 3
time = 1021
win_length = 2 * 7 + 1
specgram = torch.randn(channel, n_mfcc, time)
transform = transforms.ComputeDeltas(win_length=win_length)
computed = transform(specgram)
self.assertTrue(computed.shape == specgram.shape, (computed.shape, specgram.shape))
def test_compute_deltas_transform_same_as_functional(self, atol=1e-6, rtol=1e-8):
channel = 13
n_mfcc = channel * 3
time = 1021
win_length = 2 * 7 + 1
specgram = torch.randn(channel, n_mfcc, time)
transform = transforms.ComputeDeltas(win_length=win_length)
computed_transform = transform(specgram)
computed_functional = F.compute_deltas(specgram, win_length=win_length)
self.assertEqual(computed_functional, computed_transform, atol=atol, rtol=rtol)
def test_compute_deltas_twochannel(self):
specgram = torch.tensor([1., 2., 3., 4.]).repeat(1, 2, 1)
expected = torch.tensor([[[0.5, 1.0, 1.0, 0.5],
[0.5, 1.0, 1.0, 0.5]]])
transform = transforms.ComputeDeltas(win_length=3)
computed = transform(specgram)
assert computed.shape == expected.shape, (computed.shape, expected.shape)
self.assertEqual(computed, expected, atol=1e-6, rtol=1e-8)
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