def test_audio_dataset_from_directory_multiclass(self):
directory = self._prepare_directory(num_classes=4, count=15)
dataset = audio_dataset.audio_dataset_from_directory(
directory,
batch_size=8,
output_sequence_length=30,
label_mode=None)
batch = next(iter(dataset))
self.assertEqual(batch.shape, (8, 30, 1))
dataset = audio_dataset.audio_dataset_from_directory(
directory,
batch_size=8,
output_sequence_length=30,
label_mode=None)
sample_count = 0
iterator = iter(dataset)
for batch in dataset:
sample_count += next(iterator).shape[0]
self.assertEqual(sample_count, 15)
dataset = audio_dataset.audio_dataset_from_directory(
directory,
batch_size=8,
output_sequence_length=30,
label_mode="int")
batch = next(iter(dataset))
self.assertLen(batch, 2)
self.assertEqual(batch[0].shape, (8, 30, 1))
self.assertEqual(batch[0].dtype.name, "float32")
self.assertEqual(batch[1].shape, (8, ))
self.assertEqual(batch[1].dtype.name, "int32")
dataset = audio_dataset.audio_dataset_from_directory(
directory,
batch_size=8,
output_sequence_length=30,
label_mode="categorical",
)
batch = next(iter(dataset))
self.assertLen(batch, 2)
self.assertEqual(batch[0].shape, (8, 30, 1))
self.assertEqual(batch[0].dtype.name, "float32")
self.assertEqual(batch[1].shape, (8, 4))
self.assertEqual(batch[1].dtype.name, "float32")
def test_audio_dataset_from_directory_ragged(self):
directory = self._prepare_directory(num_classes=2,
count=16,
different_sequence_lengths=True)
dataset = audio_dataset.audio_dataset_from_directory(directory,
ragged=True,
batch_size=8)
batch = next(iter(dataset))
self.assertEqual(batch[0].shape.as_list(), [8, None, None])
def test_audio_dataset_from_directory_not_batched(self):
directory = self._prepare_directory(num_classes=2, count=2)
dataset = audio_dataset.audio_dataset_from_directory(
directory,
batch_size=None,
output_sequence_length=30,
label_mode=None,
shuffle=False,
)
sample = next(iter(dataset))
self.assertEqual(len(sample.shape), 2)
def test_sample_count(self):
directory = self._prepare_directory(num_classes=4, count=15)
dataset = audio_dataset.audio_dataset_from_directory(
directory,
batch_size=8,
output_sequence_length=30,
label_mode=None)
sample_count = 0
for batch in dataset:
sample_count += batch.shape[0]
self.assertEqual(sample_count, 15)
def test_audio_dataset_from_directory_manual_labels(self):
directory = self._prepare_directory(num_classes=2, count=2)
dataset = audio_dataset.audio_dataset_from_directory(
directory,
batch_size=8,
output_sequence_length=30,
labels=[0, 1],
shuffle=False,
)
batch = next(iter(dataset))
self.assertLen(batch, 2)
self.assertAllClose(batch[1], [0, 1])
def test_audio_dataset_from_directory_binary(self):
directory = self._prepare_directory(num_classes=2)
dataset = audio_dataset.audio_dataset_from_directory(
directory,
batch_size=8,
output_sequence_length=30,
label_mode="int")
batch = next(iter(dataset))
self.assertLen(batch, 2)
self.assertEqual(batch[0].shape, (8, 30, 1))
self.assertEqual(batch[0].dtype.name, "float32")
self.assertEqual(batch[1].shape, (8, ))
self.assertEqual(batch[1].dtype.name, "int32")
dataset = audio_dataset.audio_dataset_from_directory(
directory,
batch_size=8,
output_sequence_length=30,
label_mode="binary",
)
batch = next(iter(dataset))
self.assertLen(batch, 2)
self.assertEqual(batch[0].shape, (8, 30, 1))
self.assertEqual(batch[0].dtype.name, "float32")
self.assertEqual(batch[1].shape, (8, 1))
self.assertEqual(batch[1].dtype.name, "float32")
dataset = audio_dataset.audio_dataset_from_directory(
directory,
batch_size=8,
output_sequence_length=30,
label_mode="categorical",
)
batch = next(iter(dataset))
self.assertLen(batch, 2)
self.assertEqual(batch[0].shape, (8, 30, 1))
self.assertEqual(batch[0].dtype.name, "float32")
self.assertEqual(batch[1].shape, (8, 2))
self.assertEqual(batch[1].dtype.name, "float32")
def test_audio_dataset_from_directory_no_output_sequence_length_same_lengths(
self, ):
# This test case tests `audio_dataset_from_directory` when `ragged` and `output_sequence_length`
# are not passed while the input sequence lengths are the same
directory = self._prepare_directory(num_classes=2,
count=16,
different_sequence_lengths=False)
# The tensor shapes are different and output_sequence_length is None
# should work fine and pad each sequence to the length of the longest sequence
# in it's batch
dataset = audio_dataset.audio_dataset_from_directory(directory,
batch_size=2)
sequence_lengths = list(set([batch[0].shape[1] for batch in dataset]))
self.assertEqual(len(sequence_lengths), 1)
def test_audio_dataset_from_directory_follow_links(self):
directory = self._prepare_directory(num_classes=2,
count=25,
nested_dirs=True)
dataset = audio_dataset.audio_dataset_from_directory(
directory,
batch_size=8,
output_sequence_length=30,
label_mode=None,
follow_links=True,
)
sample_count = 0
for batch in dataset:
sample_count += batch.shape[0]
self.assertEqual(sample_count, 25)
def test_audio_dataset_from_directory_validation_split(self):
directory = self._prepare_directory(num_classes=2, count=10)
dataset = audio_dataset.audio_dataset_from_directory(
directory,
batch_size=10,
output_sequence_length=30,
validation_split=0.2,
subset="training",
seed=1337,
)
batch = next(iter(dataset))
self.assertLen(batch, 2)
self.assertEqual(batch[0].shape, (8, 30, 1))
dataset = audio_dataset.audio_dataset_from_directory(
directory,
batch_size=10,
output_sequence_length=30,
validation_split=0.2,
subset="validation",
seed=1337,
)
batch = next(iter(dataset))
self.assertLen(batch, 2)
self.assertEqual(batch[0].shape, (2, 30, 1))
def test_static_shape_in_graph(self):
directory = self._prepare_directory(num_classes=2)
dataset = audio_dataset.audio_dataset_from_directory(
directory,
batch_size=8,
output_sequence_length=30,
label_mode="int")
test_case = self
@tf.function
def symbolic_fn(ds):
for x, _ in ds.take(1):
test_case.assertListEqual(x.shape.as_list(), [None, 30, None])
symbolic_fn(dataset)
def test_audio_dataset_from_directory_no_output_sequence_length_no_ragged(
self, ):
# This test case tests `audio_dataset_from_directory` when `ragged` and `output_sequence_length`
# are not passed while the input sequence lengths are different.
directory = self._prepare_directory(num_classes=2,
count=16,
different_sequence_lengths=True)
# The tensor shapes are different and output_sequence_length is None
# should work fine and pad each sequence to the length of the longest sequence
# in it's batch
min_sequence_length, max_sequence_length = 10, 30
possible_sequence_lengths = [
i for i in range(min_sequence_length, max_sequence_length + 1)
]
dataset = audio_dataset.audio_dataset_from_directory(directory,
batch_size=2)
sequence_lengths = list(set([b.shape[1] for b, _ in dataset]))
for seq_len in sequence_lengths:
self.assertIn(seq_len, possible_sequence_lengths)
def test_audio_dataset_from_directory_standalone(self):
# Test retrieving audio samples withouts labels from a directory and its subdirs.
# Save a few extra audio in the parent directory.
directory = self._prepare_directory(count=7, num_classes=2)
for i, audio in enumerate(self._get_audio_samples(3)):
filename = "audio_%s.wav" % (i, )
with open(os.path.join(directory, filename), "wb") as f:
f.write(audio.numpy())
dataset = audio_dataset.audio_dataset_from_directory(
directory, batch_size=5, output_sequence_length=30, labels=None)
batch = next(iter(dataset))
# We return plain audio
self.assertEqual(batch.shape, (5, 30, 1))
self.assertEqual(batch.dtype.name, "float32")
# Count samples
batch_count = 0
sample_count = 0
for batch in dataset:
batch_count += 1
sample_count += batch.shape[0]
self.assertEqual(batch_count, 2)
self.assertEqual(sample_count, 10)
def test_audio_dataset_from_directory_errors(self):
directory = self._prepare_directory(num_classes=3, count=5)
with self.assertRaisesRegex(
ValueError,
"`sampling_rate` should be higher than 0. Received:"):
_ = audio_dataset.audio_dataset_from_directory(
directory,
ragged=False,
output_sequence_length=10,
sampling_rate=-1,
)
with self.assertRaisesRegex(
ValueError,
"`sampling_rate` should have an integer value. Received:",
):
_ = audio_dataset.audio_dataset_from_directory(
directory,
ragged=False,
output_sequence_length=10,
sampling_rate=1.2,
)
with self.assertRaisesRegex(
ValueError,
"Cannot set both `ragged` and `output_sequence_length`"):
_ = audio_dataset.audio_dataset_from_directory(
directory, ragged=True, output_sequence_length=30)
with self.assertRaisesRegex(ValueError, "`labels` argument should be"):
_ = audio_dataset.audio_dataset_from_directory(directory,
labels="other")
with self.assertRaisesRegex(ValueError,
"`label_mode` argument must be"):
_ = audio_dataset.audio_dataset_from_directory(directory,
label_mode="other")
with self.assertRaisesRegex(
ValueError, 'only pass `class_names` if `labels="inferred"`'):
_ = audio_dataset.audio_dataset_from_directory(
directory,
labels=[0, 0, 1, 1, 1],
class_names=["class_0", "class_1", "class_2"],
)
with self.assertRaisesRegex(
ValueError,
"Expected the lengths of `labels` to match the number of files",
):
_ = audio_dataset.audio_dataset_from_directory(directory,
labels=[0, 0, 1, 1])
with self.assertRaisesRegex(ValueError,
"`class_names` passed did not match"):
_ = audio_dataset.audio_dataset_from_directory(
directory, class_names=["class_0", "class_2"])
with self.assertRaisesRegex(ValueError, "there must be exactly 2"):
_ = audio_dataset.audio_dataset_from_directory(directory,
label_mode="binary")
with self.assertRaisesRegex(
ValueError, "`validation_split` must be between 0 and 1"):
_ = audio_dataset.audio_dataset_from_directory(directory,
validation_split=2)
with self.assertRaisesRegex(ValueError,
'`subset` must be either "training",'):
_ = audio_dataset.audio_dataset_from_directory(
directory, validation_split=0.2, subset="other")
with self.assertRaisesRegex(ValueError,
"`validation_split` must be set"):
_ = audio_dataset.audio_dataset_from_directory(directory,
validation_split=0,
subset="training")
with self.assertRaisesRegex(ValueError, "must provide a `seed`"):
_ = audio_dataset.audio_dataset_from_directory(
directory, validation_split=0.2, subset="training")
def test_audio_dataset_from_directory_no_audio(self):
directory = self._prepare_directory(num_classes=2, count=0)
with self.assertRaisesRegex(ValueError,
"No audio files found in directory"):
_ = audio_dataset.audio_dataset_from_directory(directory)
