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
|
import itertools
from collections import namedtuple
import torch
from parameterized import parameterized
from torchaudio.models import ConvTasNet, DeepSpeech, Wav2Letter, WaveRNN
from torchaudio.models.wavernn import MelResNet, UpsampleNetwork
from torchaudio_unittest import common_utils
from torchaudio_unittest.common_utils import torch_script
class TestWav2Letter(common_utils.TorchaudioTestCase):
def test_waveform(self):
batch_size = 2
num_features = 1
num_classes = 40
input_length = 320
model = Wav2Letter(num_classes=num_classes, num_features=num_features)
x = torch.rand(batch_size, num_features, input_length)
out = model(x)
assert out.size() == (batch_size, num_classes, 2)
def test_mfcc(self):
batch_size = 2
num_features = 13
num_classes = 40
input_length = 2
model = Wav2Letter(num_classes=num_classes, input_type="mfcc", num_features=num_features)
x = torch.rand(batch_size, num_features, input_length)
out = model(x)
assert out.size() == (batch_size, num_classes, 2)
class TestMelResNet(common_utils.TorchaudioTestCase):
def test_waveform(self):
"""Validate the output dimensions of a MelResNet block."""
n_batch = 2
n_time = 200
n_freq = 100
n_output = 128
n_res_block = 10
n_hidden = 128
kernel_size = 5
model = MelResNet(n_res_block, n_freq, n_hidden, n_output, kernel_size)
x = torch.rand(n_batch, n_freq, n_time)
out = model(x)
assert out.size() == (n_batch, n_output, n_time - kernel_size + 1)
class TestUpsampleNetwork(common_utils.TorchaudioTestCase):
def test_waveform(self):
"""Validate the output dimensions of a UpsampleNetwork block."""
upsample_scales = [5, 5, 8]
n_batch = 2
n_time = 200
n_freq = 100
n_output = 256
n_res_block = 10
n_hidden = 128
kernel_size = 5
total_scale = 1
for upsample_scale in upsample_scales:
total_scale *= upsample_scale
model = UpsampleNetwork(upsample_scales, n_res_block, n_freq, n_hidden, n_output, kernel_size)
x = torch.rand(n_batch, n_freq, n_time)
out1, out2 = model(x)
assert out1.size() == (n_batch, n_freq, total_scale * (n_time - kernel_size + 1))
assert out2.size() == (n_batch, n_output, total_scale * (n_time - kernel_size + 1))
class TestWaveRNN(common_utils.TorchaudioTestCase):
def test_waveform(self):
"""Validate the output dimensions of a WaveRNN model."""
upsample_scales = [5, 5, 8]
n_rnn = 512
n_fc = 512
n_classes = 512
hop_length = 200
n_batch = 2
n_time = 200
n_freq = 100
n_output = 256
n_res_block = 10
n_hidden = 128
kernel_size = 5
model = WaveRNN(
upsample_scales, n_classes, hop_length, n_res_block, n_rnn, n_fc, kernel_size, n_freq, n_hidden, n_output
)
x = torch.rand(n_batch, 1, hop_length * (n_time - kernel_size + 1))
mels = torch.rand(n_batch, 1, n_freq, n_time)
out = model(x, mels)
assert out.size() == (n_batch, 1, hop_length * (n_time - kernel_size + 1), n_classes)
def test_infer_waveform(self):
"""Validate the output dimensions of a WaveRNN model's infer method."""
upsample_scales = [5, 5, 8]
n_rnn = 128
n_fc = 128
n_classes = 128
hop_length = 200
n_batch = 2
n_time = 50
n_freq = 25
n_output = 64
n_res_block = 2
n_hidden = 32
kernel_size = 5
model = WaveRNN(
upsample_scales, n_classes, hop_length, n_res_block, n_rnn, n_fc, kernel_size, n_freq, n_hidden, n_output
)
x = torch.rand(n_batch, n_freq, n_time)
lengths = torch.tensor([n_time, n_time // 2])
out, waveform_lengths = model.infer(x, lengths)
assert out.size() == (n_batch, 1, hop_length * n_time)
assert waveform_lengths[0] == hop_length * n_time
assert waveform_lengths[1] == hop_length * n_time // 2
def test_torchscript_infer(self):
"""Scripted model outputs the same as eager mode"""
upsample_scales = [5, 5, 8]
n_rnn = 128
n_fc = 128
n_classes = 128
hop_length = 200
n_batch = 2
n_time = 50
n_freq = 25
n_output = 64
n_res_block = 2
n_hidden = 32
kernel_size = 5
model = WaveRNN(
upsample_scales, n_classes, hop_length, n_res_block, n_rnn, n_fc, kernel_size, n_freq, n_hidden, n_output
)
model.eval()
x = torch.rand(n_batch, n_freq, n_time)
torch.random.manual_seed(0)
out_eager = model.infer(x)
torch.random.manual_seed(0)
out_script = torch_script(model).infer(x)
self.assertEqual(out_eager, out_script)
_ConvTasNetParams = namedtuple(
"_ConvTasNetParams",
[
"enc_num_feats",
"enc_kernel_size",
"msk_num_feats",
"msk_num_hidden_feats",
"msk_kernel_size",
"msk_num_layers",
"msk_num_stacks",
],
)
class TestConvTasNet(common_utils.TorchaudioTestCase):
@parameterized.expand(
list(
itertools.product(
[2, 3],
[
_ConvTasNetParams(128, 40, 128, 256, 3, 7, 2),
_ConvTasNetParams(256, 40, 128, 256, 3, 7, 2),
_ConvTasNetParams(512, 40, 128, 256, 3, 7, 2),
_ConvTasNetParams(512, 40, 128, 256, 3, 7, 2),
_ConvTasNetParams(512, 40, 128, 512, 3, 7, 2),
_ConvTasNetParams(512, 40, 128, 512, 3, 7, 2),
_ConvTasNetParams(512, 40, 256, 256, 3, 7, 2),
_ConvTasNetParams(512, 40, 256, 512, 3, 7, 2),
_ConvTasNetParams(512, 40, 256, 512, 3, 7, 2),
_ConvTasNetParams(512, 40, 128, 512, 3, 6, 4),
_ConvTasNetParams(512, 40, 128, 512, 3, 4, 6),
_ConvTasNetParams(512, 40, 128, 512, 3, 8, 3),
_ConvTasNetParams(512, 32, 128, 512, 3, 8, 3),
_ConvTasNetParams(512, 16, 128, 512, 3, 8, 3),
],
)
)
)
def test_paper_configuration(self, num_sources, model_params):
"""ConvTasNet model works on the valid configurations in the paper"""
batch_size = 32
num_frames = 8000
model = ConvTasNet(
num_sources=num_sources,
enc_kernel_size=model_params.enc_kernel_size,
enc_num_feats=model_params.enc_num_feats,
msk_kernel_size=model_params.msk_kernel_size,
msk_num_feats=model_params.msk_num_feats,
msk_num_hidden_feats=model_params.msk_num_hidden_feats,
msk_num_layers=model_params.msk_num_layers,
msk_num_stacks=model_params.msk_num_stacks,
)
tensor = torch.rand(batch_size, 1, num_frames)
output = model(tensor)
assert output.shape == (batch_size, num_sources, num_frames)
class TestDeepSpeech(common_utils.TorchaudioTestCase):
def test_deepspeech(self):
n_batch = 2
n_feature = 1
n_channel = 1
n_class = 40
n_time = 320
model = DeepSpeech(n_feature=n_feature, n_class=n_class)
x = torch.rand(n_batch, n_channel, n_time, n_feature)
out = model(x)
assert out.size() == (n_batch, n_time, n_class)
|
