class BarkKmeans(ff.BaseModel):
docs = ff.Feature(
ff.IteratorNode,
store=False)
shuffle = ff.NumpyFeature(
zounds.ShuffledSamples,
nsamples=int(1e6),
needs=docs,
store=True)
unitnorm = ff.PickleFeature(
zounds.UnitNorm,
needs=shuffle,
store=False)
kmeans = ff.PickleFeature(
zounds.KMeans,
centroids=128,
needs=unitnorm,
store=False)
pipeline = ff.PickleFeature(
zounds.PreprocessingPipeline,
needs=(unitnorm, kmeans),
store=True)
class DctKmeansWithLogAmplitude(ff.BaseModel):
"""
A pipeline that applies a logarithmic weighting to the magnitudes of the
spectrum before learning centroids,
"""
docs = ff.Feature(
ff.IteratorNode,
store=False)
# randomize the order of the data
shuffle = ff.NumpyFeature(
zounds.ReservoirSampler,
nsamples=1e6,
needs=docs,
store=True)
log = ff.PickleFeature(
zounds.Log,
needs=shuffle,
store=False)
# give each frame unit norm, since we care about the shape of the spectrum
# and not its magnitude
unit_norm = ff.PickleFeature(
zounds.UnitNorm,
needs=log,
store=False)
# learn 512 centroids, or basis functions
kmeans = ff.PickleFeature(
zounds.KMeans,
centroids=512,
needs=unit_norm,
store=False)
# assemble the previous steps into a re-usable pipeline, which can perform
# forward and backward transformations
pipeline = ff.PickleFeature(
zounds.PreprocessingPipeline,
needs=(log, unit_norm, kmeans),
store=True)
class Rbm(featureflow.BaseModel, Settings):
iterator = featureflow.Feature(Iterator, store=False)
shuffle = featureflow.NumpyFeature(ReservoirSampler,
nsamples=1000,
needs=iterator,
store=True)
unitnorm = featureflow.PickleFeature(UnitNorm,
needs=shuffle,
store=False)
meanstd = featureflow.PickleFeature(MeanStdNormalization,
needs=unitnorm,
store=False)
rbm = featureflow.PickleFeature(KMeans,
centroids=3,
needs=meanstd,
store=False)
pipeline = featureflow.PickleFeature(PreprocessingPipeline,
needs=(unitnorm, meanstd, rbm),
store=True)
class DctKmeans(ff.BaseModel):
"""
A pipeline that does example-wise normalization by giving each example
unit-norm, and learns 512 centroids from those examples.
"""
docs = ff.Feature(
ff.IteratorNode,
store=False)
# randomize the order of the data
shuffle = ff.NumpyFeature(
zounds.ReservoirSampler,
nsamples=1e6,
needs=docs,
store=True)
# give each frame unit norm, since we care about the shape of the spectrum
# and not its magnitude
unit_norm = ff.PickleFeature(
zounds.UnitNorm,
needs=shuffle,
store=False)
# learn 512 centroids, or basis functions
kmeans = ff.PickleFeature(
zounds.KMeans,
centroids=512,
needs=unit_norm,
store=False)
# assemble the previous steps into a re-usable pipeline, which can perform
# forward and backward transformations
pipeline = ff.PickleFeature(
zounds.PreprocessingPipeline,
needs=(unit_norm, kmeans),
store=True)