# -*- coding: utf-8 -*-
import numpy as np
from dtw import dtw
from fastdtw import fastdtw
from dtw_c import dtw_c
from sprocket.util import melcd
def estimate_twf(orgdata, tardata, distance='melcd', fast=True, otflag=None):
"""time warping function estimator
Parameters
---------
orgdata : array, shape(`T_org`, `dim`)
Array of source feature
tardata : array, shape(`T_tar`, `dim`)
Array of target feature
distance : str, optional
distance function
`melcd` : mel-cepstrum distortion
fast : bool, optional
Use fastdtw instead of dtw
Default set to `True`
otflag : str,
Perform alignment into either original or target length
`org` : align into original length
`tar` : align into target length
Default set to None
Returns
---------
twf : array, shape(`2`, `T`)
Time warping function between original and target
"""
if distance == 'melcd':
def distance_func(x, y): return melcd(x, y)
else:
raise ValueError('other distance metrics than melcd does not support.')
if otflag is None:
# use dtw or fastdtw
if fast:
_, path = fastdtw(orgdata, tardata, dist=distance_func)
twf = np.array(path).T
else:
_, _, _, twf = dtw(orgdata, tardata, distance_func)
else:
# use dtw_c to align target/original feature vector
ldim = orgdata.shape[1] - 1
if otflag == 'org':
_, twf, _, _ = dtw_c.dtw_org_to_trg(tardata, orgdata,
0, ldim, 5.0, 100.0, 100.0)
else:
_, twf, _, _ = dtw_c.dtw_org_to_trg(orgdata, tardata,
0, ldim, 5.0, 100.0, 100.0)
twf[:, 1] = np.array(range(twf.shape[0])) # replace target index by frame number
twf = twf.T
if otflag == 'org':
twf = twf[::-1, :] # swap cols
assert twf.shape[0] == orgdata.shape[0]
else:
assert twf.shape[1] == tardata.shape[0]
return twf
def align_data(org_data, tar_data, twf):
"""Get aligned joint feature vector
Paramters
---------
orgdata : array, shape (`T_org`, `dim_org`)
Acoustic feature vector of original speaker
tardata : array, shape (`T_tar`, `dim_tar`)
Acoustic feature vector of target speaker
twf : array, shape (`2`, `T`)
Time warping function between original and target
Returns
-------
jdata : array, shape (`T_new` `dim_org + dim_tar`)
Joint feature vector between source and target
"""
jdata = np.c_[org_data[twf[0]], tar_data[twf[1]]]
return jdata
def modify_twf(twf, otflag=None):
"""Align specified length
Paramters
---------
twf : array, shape(`2`, `T`)
Time warping function between original and target
otflag : str,
Perform alignment into either original or target length
`org` : align into original length
`tar` : align into target length
Returns
-------
mod_twf : array, shape (`2`, `T_new`)
Time warping function of modified alignment
"""
if otflag == 'org':
of, indice = np.unique(twf[0], return_index=True)
mod_twf = np.c_[of, twf[1][indice]].T
elif otflag == 'tar':
tf, indice = np.unique(twf[1], return_index=True)
mod_twf = np.c_[twf[0][indice], tf].T
return mod_twf
