def test_pytorch_deep_speech(art_warning, expected_values, use_amp,
device_type):
# Only import if deepspeech_pytorch module is available
import torch
from art.estimators.speech_recognition.pytorch_deep_speech import PyTorchDeepSpeech
try:
# Load data for testing
expected_data = expected_values()
x1 = expected_data[0]
x2 = expected_data[1]
x3 = expected_data[2]
expected_sizes = expected_data[3]
expected_transcriptions1 = expected_data[4]
expected_transcriptions2 = expected_data[5]
expected_probs = expected_data[6]
expected_gradients1 = expected_data[7]
expected_gradients2 = expected_data[8]
expected_gradients3 = expected_data[9]
# Create signal data
x = np.array([
np.array(x1 * 100, dtype=ART_NUMPY_DTYPE),
np.array(x2 * 100, dtype=ART_NUMPY_DTYPE),
np.array(x3 * 100, dtype=ART_NUMPY_DTYPE),
])
# Create labels
y = np.array(["SIX", "HI", "GOOD"])
# Test probability outputs
speech_recognizer = PyTorchDeepSpeech(pretrained_model="librispeech",
device_type=device_type,
use_amp=use_amp)
probs, sizes = speech_recognizer.predict(x, batch_size=2)
np.testing.assert_array_almost_equal(probs[1][1],
expected_probs,
decimal=3)
np.testing.assert_array_almost_equal(sizes, expected_sizes)
# Test transcription outputs
transcriptions = speech_recognizer.predict(x,
batch_size=2,
transcription_output=True)
assert (expected_transcriptions1 == transcriptions).all()
# Test transcription outputs, corner case
transcriptions = speech_recognizer.predict(np.array([x[0]]),
batch_size=2,
transcription_output=True)
assert (expected_transcriptions2 == transcriptions).all()
# Now test loss gradients
# Compute gradients
grads = speech_recognizer.loss_gradient(x, y)
assert grads[0].shape == (1300, )
assert grads[1].shape == (1500, )
assert grads[2].shape == (1400, )
np.testing.assert_array_almost_equal(grads[0][0:20],
expected_gradients1,
decimal=-2)
np.testing.assert_array_almost_equal(grads[1][0:20],
expected_gradients2,
decimal=-2)
np.testing.assert_array_almost_equal(grads[2][0:20],
expected_gradients3,
decimal=-2)
# Now test fit function
# Create the optimizer
parameters = speech_recognizer.model.parameters()
speech_recognizer._optimizer = torch.optim.SGD(parameters, lr=0.01)
# Before train
transcriptions1 = speech_recognizer.predict(x,
batch_size=2,
transcription_output=True)
# Train the estimator
speech_recognizer.fit(x=x, y=y, batch_size=2, nb_epochs=5)
# After train
transcriptions2 = speech_recognizer.predict(x,
batch_size=2,
transcription_output=True)
assert not ((transcriptions1 == transcriptions2).all())
except ARTTestException as e:
art_warning(e)
class TestPyTorchDeepSpeech:
"""
This class tests the PyTorchDeepSpeech estimator.
"""
@pytest.fixture
def setup_class(self):
master_seed(seed=1234)
# Small data for testing
x1 = np.array(
[
-1.0376293e-03,
-1.0681478e-03,
-1.0986663e-03,
-1.1291848e-03,
-1.1291848e-03,
-1.1291848e-03,
-1.1902219e-03,
-1.1597034e-03,
-1.1902219e-03,
-1.1291848e-03,
-1.1291848e-03,
-1.0681478e-03,
-9.1555528e-04,
]
* 100
)
x2 = np.array(
[
-1.8311106e-04,
-1.2207404e-04,
-6.1037019e-05,
0.0000000e00,
3.0518509e-05,
0.0000000e00,
-3.0518509e-05,
0.0000000e00,
0.0000000e00,
9.1555528e-05,
2.1362957e-04,
3.3570360e-04,
4.2725913e-04,
4.5777764e-04,
-1.8311106e-04,
]
* 100
)
x3 = np.array(
[
-8.2399976e-04,
-7.0192572e-04,
-5.4933317e-04,
-4.2725913e-04,
-3.6622211e-04,
-2.7466659e-04,
-2.1362957e-04,
5.4933317e-04,
5.7985168e-04,
6.1037019e-04,
6.7140721e-04,
7.0192572e-04,
6.7140721e-04,
-1.5259255e-04,
]
* 100
)
self.x = np.array([x1, x2, x3])
def test_all(self, _test_all):
pass
@pytest.fixture(params=[False, True])
def _test_all(self, request, setup_class):
# Only import if deep speech module is available
import torch
from art.estimators.speech_recognition.pytorch_deep_speech import PyTorchDeepSpeech
# Test probability outputs
if request.param is True:
self.speech_recognizer_amp = PyTorchDeepSpeech(
pretrained_model="librispeech", device_type="gpu", use_amp=True
)
probs, sizes = self.speech_recognizer_amp.predict(self.x, batch_size=2)
else:
self.speech_recognizer = PyTorchDeepSpeech(pretrained_model="librispeech")
probs, sizes = self.speech_recognizer.predict(self.x, batch_size=2)
expected_sizes = np.asarray([5, 5, 5])
np.testing.assert_array_almost_equal(sizes, expected_sizes)
expected_probs = np.asarray(
[
1.0000000e00,
7.0154901e-14,
1.9170589e-13,
8.2194836e-13,
8.9967915e-13,
1.8518193e-12,
1.7883164e-10,
1.8951663e-12,
1.8818237e-13,
3.2806991e-12,
3.5664666e-16,
3.3147299e-14,
2.3439516e-13,
8.4845603e-12,
1.2017718e-13,
1.1180213e-12,
6.5572378e-15,
3.0194697e-12,
4.9065188e-15,
1.9765363e-13,
4.1670646e-11,
2.6884213e-12,
1.1436632e-13,
7.1931783e-15,
2.8135227e-11,
4.5599673e-14,
6.4587983e-13,
2.4159567e-15,
4.6668241e-13,
]
)
np.testing.assert_array_almost_equal(probs[1][1], expected_probs, decimal=3)
# Test transcription outputs
if request.param is True:
transcriptions = self.speech_recognizer_amp.predict(self.x, batch_size=2, transcription_output=True)
else:
transcriptions = self.speech_recognizer.predict(self.x, batch_size=2, transcription_output=True)
expected_transcriptions = np.array(["", "", ""])
assert (expected_transcriptions == transcriptions).all()
# Test transcription outputs, corner case
if request.param is True:
transcriptions = self.speech_recognizer_amp.predict(
np.array([self.x[0]]), batch_size=2, transcription_output=True
)
else:
transcriptions = self.speech_recognizer.predict(
np.array([self.x[0]]), batch_size=2, transcription_output=True
)
expected_transcriptions = np.array([""])
assert (expected_transcriptions == transcriptions).all()
# Now test loss gradients
# Create labels
y = np.array(["SIX", "HI", "GOOD"])
# Compute gradients
if request.param is True:
grads = self.speech_recognizer_amp.loss_gradient(self.x, y)
else:
grads = self.speech_recognizer.loss_gradient(self.x, y)
assert grads[0].shape == (1300,)
assert grads[1].shape == (1500,)
assert grads[2].shape == (1400,)
if request.param is True:
expected_gradients1 = np.asarray(
[
-3485.7,
659.0,
-111.7,
283.6,
1691.9,
715.0,
1480.4,
-3522.3,
-4087.9,
-8824.2,
-304.7,
2013.4,
-445.1,
4125.0,
1754.1,
-503.6,
1160.0,
7051.7,
-1992.2,
350.4,
]
)
else:
expected_gradients1 = np.asarray(
[
-3482.77892371,
665.64673575,
-116.24408896,
265.93803869,
1667.02236699,
688.33557577,
1455.14911883,
-3524.90476617,
-4082.06471587,
-8802.39419605,
-277.74274789,
2034.54679277,
-428.53153241,
4114.63683848,
1722.53840709,
-513.68916798,
1159.88786568,
7072.47761446,
-1963.71829047,
382.65287411,
]
)
np.testing.assert_array_almost_equal(grads[0][0:20], expected_gradients1, decimal=0)
if request.param is True:
expected_gradients2 = np.asarray(
[
20924.5,
3046.3,
-7872.5,
15525.1,
-15766.9,
-18494.1,
19139.6,
6446.2,
26323.1,
4230.0,
-31122.4,
-2890.9,
12936.7,
13834.1,
17649.9,
8866.1,
-16454.6,
-6953.1,
-17899.6,
4100.7,
]
)
else:
expected_gradients2 = np.asarray(
[
20992.44844133,
3048.78701634,
-7849.13725934,
15557.59663939,
-15760.10725159,
-18422.9438386,
19132.22699435,
6508.51437337,
26292.5249963,
4232.62414548,
-31128.82664215,
-2894.85284984,
13008.74538039,
13845.08921681,
17657.67725957,
8807.42144017,
-16477.89414508,
-6977.8092622,
-17914.22352666,
4086.51150059,
]
)
np.testing.assert_array_almost_equal(grads[1][0:20], expected_gradients2, decimal=0)
if request.param is True:
expected_gradients3 = np.asarray(
[
-1687.3,
6715.0,
16448.4,
-3848.9,
16521.1,
-15736.1,
-26204.0,
-8992.2,
9697.9,
13999.6,
-7595.3,
14181.0,
-24507.2,
5481.9,
7166.7,
-6182.3,
2510.3,
-7229.0,
-10821.9,
-11134.2,
]
)
else:
expected_gradients3 = np.asarray(
[
-1693.10472689,
6711.39788693,
16480.14166546,
-3786.95541286,
16448.3969823,
-15702.45621671,
-26162.89260564,
-8979.81601681,
9657.87483965,
13955.78845296,
-7552.01438108,
14170.60635269,
-24434.37243957,
5502.81163675,
7171.56926943,
-6154.06511686,
2483.93980406,
-7244.24618697,
-10798.70438903,
-11129.57632319,
]
)
np.testing.assert_array_almost_equal(grads[2][0:20], expected_gradients3, decimal=0)
# Now test fit function
if request.param is True:
# Create the optimizer
parameters = self.speech_recognizer_amp.model.parameters()
self.speech_recognizer_amp._optimizer = torch.optim.SGD(parameters, lr=0.01)
# Before train
transcriptions1 = self.speech_recognizer_amp.predict(self.x, batch_size=2, transcription_output=True)
# Train the estimator
self.speech_recognizer_amp.fit(x=self.x, y=y, batch_size=2, nb_epochs=5)
# After train
transcriptions2 = self.speech_recognizer_amp.predict(self.x, batch_size=2, transcription_output=True)
assert not ((transcriptions1 == transcriptions2).all())
else:
# Create the optimizer
parameters = self.speech_recognizer.model.parameters()
self.speech_recognizer._optimizer = torch.optim.SGD(parameters, lr=0.01)
# Before train
transcriptions1 = self.speech_recognizer.predict(self.x, batch_size=2, transcription_output=True)
# Train the estimator
self.speech_recognizer.fit(x=self.x, y=y, batch_size=2, nb_epochs=5)
# After train
transcriptions2 = self.speech_recognizer.predict(self.x, batch_size=2, transcription_output=True)
assert not ((transcriptions1 == transcriptions2).all())