class VariableStepAudioFeatureModelTest(unittest.TestCase):
def setUp(self):
self.audio_file_meta = AudioFileMeta(file_name="file.wav",
channels_count=2,
sample_rate=44100,
file_size_bytes=88200)
self.feature_values = [1.0, 2.0, 3.0, 4.0]
self.feature_steps = [
StepFeature(v,
values=numpy.asanyarray(self.feature_values),
label="text_{}".format(v)) for v in self.feature_values
]
self.variable_step_feature = VampyVariableStepFeature(
task_id="0f961f20-b036-5740-b526-013523dd88c7",
step_features=self.feature_steps)
def test_should_serialize_and_deserialize_feature(self):
serialized = self.variable_step_feature.to_serializable()
deserialized = VampyVariableStepFeature.from_serializable(serialized)
assert_that(deserialized).is_not_none().is_equal_to(
self.variable_step_feature)
def test_should_serialized_to_json(self):
as_json = json.dumps(self.variable_step_feature.to_serializable())
assert_that(as_json).is_not_none().is_not_empty()
def setUp(self):
self.audio_file_meta = AudioFileMeta(file_name="file.wav",
channels_count=2,
sample_rate=44100,
file_size_bytes=88200)
self.feature_values = [1.0, 2.0, 3.0, 4.0]
self.feature_steps = [
StepFeature(v,
values=numpy.asanyarray(self.feature_values),
label="text_{}".format(v)) for v in self.feature_values
]
self.variable_step_feature = VampyVariableStepFeature(
task_id="0f961f20-b036-5740-b526-013523dd88c7",
step_features=self.feature_steps)
def build_feature_object(task_id: str, extracted_data: Dict[str, Any]) -> VampyFeatureAbstraction:
data_type = list(extracted_data.keys())[0]
if data_type == "list":
value_list = [StepFeature(f.get("timestamp").to_float(), f.get("values"), f.get("label") or None)
for f in extracted_data["list"]]
return VampyVariableStepFeature(task_id, step_features=value_list)
elif data_type in ("vector", "matrix"):
data = extracted_data.get("vector") or extracted_data.get("matrix")
return VampyConstantStepFeature(task_id, time_step=data[0].to_float(), matrix=data[1]) # type: ignore
else:
raise NotImplementedError("Can not recognize feature type: {}".format(extracted_data.keys()))
def setUp(self):
self.audio_meta = CompressedAudioFileMeta("some_name.mp3", 1024, 1, 44100, length_sec=4., bit_rate_kbps=128)
self.single_dimensional_feature = array([0.38888031], dtype=float32)
self.two_dimensional_feature = array([0.38888031, 0.3144314, 0.46564227, 0.31890243, 0.22512659], dtype=float32)
self.three_dimensional_feature = array([[0.38888031, 0.3144314],
[0.46564227, 0.31890243],
[0.22512659, 0.31890243]], dtype=float32)
self.constant_step_2d_feature = VampyConstantStepFeature("", time_step=0.1, matrix=self.two_dimensional_feature)
self.constant_step_3d_feature = VampyConstantStepFeature("", time_step=0.1,
matrix=self.three_dimensional_feature)
self.variable_step_single_value_feature = VampyVariableStepFeature("", [StepFeature(timestamp=0.0,
values=array([519.51]))])
self.constant_step_single_value_feature = VampyConstantStepFeature("", time_step=0.1,
matrix=self.single_dimensional_feature)
def test_should_transform_segment_based_simple_variable_feature_into_ratio_metric(self):
segment_share_ratio = SegmentLabelShareRatioTransformation(audio_meta=self.audio_meta, label="b")
segment_variable_step_feature = VampyVariableStepFeature("task_id", [StepFeature(timestamp=0.0,
values=array([0]),
label="a"),
StepFeature(timestamp=1.0,
values=array([1]),
label="b"),
StepFeature(timestamp=2.0,
values=array([0]),
label="a"),
StepFeature(timestamp=3.0,
values=array([1]),
label="b")
])
actual_metric_vector = segment_share_ratio.call(segment_variable_step_feature)
assert_that(actual_metric_vector).is_length(2).contains(0.25)
def test_should_serialize_and_deserialize_feature(self):
serialized = self.variable_step_feature.to_serializable()
deserialized = VampyVariableStepFeature.from_serializable(serialized)
assert_that(deserialized).is_not_none().is_equal_to(
self.variable_step_feature)
def _call_on_variable_step(
self, feature: VampyVariableStepFeature) -> numpy.ndarray:
return feature.values()
def from_compressed_feature(comp_feat: CompressedFeatureDTO) -> Union[VampyConstantStepFeature, VampyVariableStepFeature]:
decompressed_feature = decompress_model(comp_feat.compression, comp_feat.data)
if "time_step" in decompressed_feature.keys():
return VampyConstantStepFeature.from_serializable(decompressed_feature)
else:
return VampyVariableStepFeature.from_serializable(decompressed_feature)