1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
|
"""Test suites for jit-ability and its numerical compatibility"""
import torch
import torchaudio.transforms as T
from parameterized import parameterized
from torchaudio_unittest import common_utils
from torchaudio_unittest.common_utils import skipIfRocm, TestBaseMixin, torch_script
class Transforms(TestBaseMixin):
"""Implements test for Transforms that are performed for different devices"""
def _assert_consistency(self, transform, tensor, *args):
tensor = tensor.to(device=self.device, dtype=self.dtype)
transform = transform.to(device=self.device, dtype=self.dtype)
ts_transform = torch_script(transform)
output = transform(tensor, *args)
ts_output = ts_transform(tensor, *args)
self.assertEqual(ts_output, output)
def _assert_consistency_complex(self, transform, tensor, *args):
assert tensor.is_complex()
tensor = tensor.to(device=self.device, dtype=self.complex_dtype)
transform = transform.to(device=self.device, dtype=self.dtype)
ts_transform = torch_script(transform)
output = transform(tensor, *args)
ts_output = ts_transform(tensor, *args)
self.assertEqual(ts_output, output)
def test_Spectrogram(self):
tensor = torch.rand((1, 1000))
self._assert_consistency(T.Spectrogram(), tensor)
def test_Spectrogram_return_complex(self):
tensor = torch.rand((1, 1000))
self._assert_consistency(T.Spectrogram(power=None, return_complex=True), tensor)
def test_InverseSpectrogram(self):
tensor = common_utils.get_whitenoise(sample_rate=8000)
spectrogram = common_utils.get_spectrogram(tensor, n_fft=400, hop_length=100)
self._assert_consistency_complex(T.InverseSpectrogram(n_fft=400, hop_length=100), spectrogram)
@skipIfRocm
def test_GriffinLim(self):
tensor = torch.rand((1, 201, 6))
self._assert_consistency(T.GriffinLim(length=1000, rand_init=False), tensor)
def test_AmplitudeToDB(self):
spec = torch.rand((6, 201))
self._assert_consistency(T.AmplitudeToDB(), spec)
def test_MelScale(self):
spec_f = torch.rand((1, 201, 6))
self._assert_consistency(T.MelScale(n_stft=201), spec_f)
def test_MelSpectrogram(self):
tensor = torch.rand((1, 1000))
self._assert_consistency(T.MelSpectrogram(), tensor)
def test_MFCC(self):
tensor = torch.rand((1, 1000))
self._assert_consistency(T.MFCC(), tensor)
def test_LFCC(self):
tensor = torch.rand((1, 1000))
self._assert_consistency(T.LFCC(), tensor)
def test_Resample(self):
sr1, sr2 = 16000, 8000
tensor = common_utils.get_whitenoise(sample_rate=sr1)
self._assert_consistency(T.Resample(sr1, sr2), tensor)
def test_MuLawEncoding(self):
tensor = common_utils.get_whitenoise()
self._assert_consistency(T.MuLawEncoding(), tensor)
def test_MuLawDecoding(self):
tensor = torch.rand((1, 10))
self._assert_consistency(T.MuLawDecoding(), tensor)
def test_ComputeDelta(self):
tensor = torch.rand((1, 10))
self._assert_consistency(T.ComputeDeltas(), tensor)
def test_Fade(self):
waveform = common_utils.get_whitenoise()
fade_in_len = 3000
fade_out_len = 3000
self._assert_consistency(T.Fade(fade_in_len, fade_out_len), waveform)
def test_FrequencyMasking(self):
tensor = torch.rand((10, 2, 50, 10, 2))
self._assert_consistency(T.FrequencyMasking(freq_mask_param=60, iid_masks=False), tensor)
def test_TimeMasking(self):
tensor = torch.rand((10, 2, 50, 10, 2))
self._assert_consistency(T.TimeMasking(time_mask_param=30, iid_masks=False), tensor)
def test_Vol(self):
waveform = common_utils.get_whitenoise()
self._assert_consistency(T.Vol(1.1), waveform)
def test_SlidingWindowCmn(self):
tensor = torch.rand((1000, 10))
self._assert_consistency(T.SlidingWindowCmn(), tensor)
def test_Vad(self):
filepath = common_utils.get_asset_path("vad-go-mono-32000.wav")
waveform, sample_rate = common_utils.load_wav(filepath)
self._assert_consistency(T.Vad(sample_rate=sample_rate), waveform)
def test_SpectralCentroid(self):
sample_rate = 44100
waveform = common_utils.get_whitenoise(sample_rate=sample_rate)
self._assert_consistency(T.SpectralCentroid(sample_rate=sample_rate), waveform)
def test_TimeStretch(self):
n_fft = 1025
n_freq = n_fft // 2 + 1
hop_length = 512
fixed_rate = 1.3
tensor = torch.rand((10, 2, n_freq, 10), dtype=torch.cfloat)
batch = 10
num_channels = 2
waveform = common_utils.get_whitenoise(sample_rate=8000, n_channels=batch * num_channels)
tensor = common_utils.get_spectrogram(waveform, n_fft=n_fft)
tensor = tensor.reshape(batch, num_channels, n_freq, -1)
self._assert_consistency_complex(
T.TimeStretch(n_freq=n_freq, hop_length=hop_length, fixed_rate=fixed_rate),
tensor,
)
def test_PitchShift(self):
sample_rate = 8000
n_steps = 4
waveform = common_utils.get_whitenoise(sample_rate=sample_rate)
pitch_shift = T.PitchShift(sample_rate=sample_rate, n_steps=n_steps)
# dry-run for initializing parameters
pitch_shift(waveform)
self._assert_consistency(pitch_shift, waveform)
def test_PSD(self):
tensor = common_utils.get_whitenoise(sample_rate=8000, n_channels=4)
spectrogram = common_utils.get_spectrogram(tensor, n_fft=400, hop_length=100)
spectrogram = spectrogram.to(self.device)
self._assert_consistency_complex(T.PSD(), spectrogram)
def test_PSD_with_mask(self):
tensor = common_utils.get_whitenoise(sample_rate=8000, n_channels=4)
spectrogram = common_utils.get_spectrogram(tensor, n_fft=400, hop_length=100)
spectrogram = spectrogram.to(self.device)
mask = torch.rand(spectrogram.shape[-2:], device=self.device)
self._assert_consistency_complex(T.PSD(), spectrogram, mask)
@parameterized.expand(
[
["ref_channel", True],
["stv_evd", True],
["stv_power", True],
["ref_channel", False],
["stv_evd", False],
["stv_power", False],
]
)
def test_MVDR(self, solution, online):
tensor = common_utils.get_whitenoise(sample_rate=8000, n_channels=4)
spectrogram = common_utils.get_spectrogram(tensor, n_fft=400, hop_length=100)
mask_s = torch.rand(spectrogram.shape[-2:], device=self.device)
mask_n = torch.rand(spectrogram.shape[-2:], device=self.device)
self._assert_consistency_complex(T.MVDR(solution=solution, online=online), spectrogram, mask_s, mask_n)
def test_rtf_mvdr(self):
tensor = common_utils.get_whitenoise(sample_rate=8000, n_channels=4)
specgram = common_utils.get_spectrogram(tensor, n_fft=400, hop_length=100)
channel, freq, _ = specgram.shape
rtf = torch.rand(freq, channel, dtype=self.complex_dtype, device=self.device)
psd_n = torch.rand(freq, channel, channel, dtype=self.complex_dtype, device=self.device)
reference_channel = 0
self._assert_consistency_complex(T.RTFMVDR(), specgram, rtf, psd_n, reference_channel)
def test_souden_mvdr(self):
tensor = common_utils.get_whitenoise(sample_rate=8000, n_channels=4)
specgram = common_utils.get_spectrogram(tensor, n_fft=400, hop_length=100)
channel, freq, _ = specgram.shape
psd_s = torch.rand(freq, channel, channel, dtype=self.complex_dtype, device=self.device)
psd_n = torch.rand(freq, channel, channel, dtype=self.complex_dtype, device=self.device)
reference_channel = 0
self._assert_consistency_complex(T.SoudenMVDR(), specgram, psd_s, psd_n, reference_channel)
@common_utils.nested_params(
["Convolve", "FFTConvolve"],
["full", "valid", "same"],
)
def test_convolve(self, cls, mode):
leading_dims = (2, 3, 2)
L_x, L_y = 32, 55
x = torch.rand(*leading_dims, L_x, dtype=self.dtype, device=self.device)
y = torch.rand(*leading_dims, L_y, dtype=self.dtype, device=self.device)
convolve = getattr(T, cls)(mode=mode).to(device=self.device, dtype=self.dtype)
output = convolve(x, y)
ts_output = torch_script(convolve)(x, y)
self.assertEqual(ts_output, output)
@common_utils.nested_params([True, False])
def test_speed(self, use_lengths):
leading_dims = (3, 2)
time = 200
waveform = torch.rand(*leading_dims, time, dtype=self.dtype, device=self.device, requires_grad=True)
if use_lengths:
lengths = torch.randint(1, time, leading_dims, dtype=self.dtype, device=self.device)
else:
lengths = None
speed = T.Speed(1000, 0.9).to(self.device, self.dtype)
output = speed(waveform, lengths)
ts_output = torch_script(speed)(waveform, lengths)
self.assertEqual(ts_output, output)
@common_utils.nested_params([True, False])
def test_speed_perturbation(self, use_lengths):
leading_dims = (3, 2)
time = 200
waveform = torch.rand(*leading_dims, time, dtype=self.dtype, device=self.device, requires_grad=True)
if use_lengths:
lengths = torch.randint(1, time, leading_dims, dtype=self.dtype, device=self.device)
else:
lengths = None
speed = T.SpeedPerturbation(1000, [0.9]).to(self.device, self.dtype)
output = speed(waveform, lengths)
ts_output = torch_script(speed)(waveform, lengths)
self.assertEqual(ts_output, output)
@common_utils.nested_params([True, False])
def test_add_noise(self, use_lengths):
leading_dims = (2, 3)
L = 31
waveform = torch.rand(*leading_dims, L, dtype=self.dtype, device=self.device, requires_grad=True)
noise = torch.rand(*leading_dims, L, dtype=self.dtype, device=self.device, requires_grad=True)
if use_lengths:
lengths = torch.rand(*leading_dims, dtype=self.dtype, device=self.device, requires_grad=True)
else:
lengths = None
snr = torch.rand(*leading_dims, dtype=self.dtype, device=self.device, requires_grad=True) * 10
add_noise = T.AddNoise().to(self.device, self.dtype)
output = add_noise(waveform, noise, snr, lengths)
ts_output = torch_script(add_noise)(waveform, noise, snr, lengths)
self.assertEqual(ts_output, output)
def test_preemphasis(self):
waveform = torch.rand(3, 4, 10, dtype=self.dtype, device=self.device)
preemphasis = T.Preemphasis(coeff=0.97).to(dtype=self.dtype, device=self.device)
output = preemphasis(waveform)
ts_output = torch_script(preemphasis)(waveform)
self.assertEqual(ts_output, output)
def test_deemphasis(self):
waveform = torch.rand(3, 4, 10, dtype=self.dtype, device=self.device)
deemphasis = T.Deemphasis(coeff=0.97).to(dtype=self.dtype, device=self.device)
output = deemphasis(waveform)
ts_output = torch_script(deemphasis)(waveform)
self.assertEqual(ts_output, output)
class TransformsFloat32Only(TestBaseMixin):
def test_rnnt_loss(self):
logits = torch.tensor(
[
[
[[0.1, 0.6, 0.1, 0.1, 0.1], [0.1, 0.1, 0.6, 0.1, 0.1], [0.1, 0.1, 0.2, 0.8, 0.1]],
[[0.1, 0.6, 0.1, 0.1, 0.1], [0.1, 0.1, 0.2, 0.1, 0.1], [0.7, 0.1, 0.2, 0.1, 0.1]],
]
]
)
tensor = logits.to(device=self.device, dtype=torch.float32)
targets = torch.tensor([[1, 2]], device=tensor.device, dtype=torch.int32)
logit_lengths = torch.tensor([2], device=tensor.device, dtype=torch.int32)
target_lengths = torch.tensor([2], device=tensor.device, dtype=torch.int32)
self._assert_consistency(T.RNNTLoss(), logits, targets, logit_lengths, target_lengths)
|
