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import fastdtw
import nnmnkwii.metrics
import numpy
import scipy.interpolate
class DTWAligner(object):
"""
from https://github.com/r9y9/nnmnkwii/blob/4cade86b5c35b4e35615a2a8162ddc638018af0e/nnmnkwii/preprocessing/alignment.py#L14
"""
def __init__(self, x, y, dist=lambda x, y: numpy.linalg.norm(x - y), radius=1):
assert x.ndim == 2 and y.ndim == 2
_, path = fastdtw.fastdtw(x, y, radius=radius, dist=dist)
path = numpy.array(path)
self.normed_path_x = path[:, 0] / len(x)
self.normed_path_y = path[:, 1] / len(y)
def align_x(self, x):
path = self._interp_path(self.normed_path_x, len(x))
return x[path]
def align_y(self, y):
path = self._interp_path(self.normed_path_y, len(y))
return y[path]
def align(self, x, y):
return self.align_x(x), self.align_y(y)
@staticmethod
def align_and_transform(x, y, *args, **kwargs):
aligner = DTWAligner(*args, x=x, y=y, **kwargs)
return aligner.align(x, y)
@staticmethod
def _interp_path(normed_path: numpy.ndarray, target_length: int):
path = numpy.floor(normed_path * target_length).astype(numpy.int)
return path
class MFCCAligner(DTWAligner):
def __init__(self, x, y, *args, **kwargs):
x = self._calc_aligner_feature(x)
y = self._calc_aligner_feature(y)
super().__init__(x, y, *args, dist=nnmnkwii.metrics.melcd, **kwargs)
@classmethod
def _calc_delta(cls, x):
x = numpy.zeros_like(x, x.dtype)
x[:-1] = x[1:] - x[:-1]
x[-1] = 0
return x
@classmethod
def _calc_aligner_feature(cls, x):
d = cls._calc_delta(x)
feature = numpy.concatenate((x, d), axis=1)[:, 1:]
return feature
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