mdtwr.py

import numpy as np
import rpy2.robjects.numpy2ri
rpy2.robjects.numpy2ri.activate()
from rpy2.robjects.packages import importr
import rpy2.robjects as robj
from python_speech_features import mfcc
from python_speech_features import delta
from python_speech_features import logfbank
import scipy.io.wavfile as wav

(rate1,sig1) = wav.read("Santhosh2.wav")
mfcc_feat1 = mfcc(sig1,rate1)
d_mfcc_feat1 = delta(mfcc_feat1, 2)
fbank_feat1 = logfbank(sig1,rate1)

(rate2,sig2) = wav.read("landysandy2.wav")
mfcc_feat2 = mfcc(sig2,rate2)
d_mfcc_feat2 = delta(mfcc_feat2, 2)
fbank_feat2 = logfbank(sig2,rate2)

R = robj.r
DTW = importr('dtw')

# Generate our data
template = fbank_feat1
# template = np.array([[1,2,3,4,5],[1,2,3,4,5]]).transpose()
rt,ct = template.shape
# query = template
query = fbank_feat2
rq,cq = query.shape

#converting numpy matrices to R matrices
templateR=R.matrix(template,nrow=rt,ncol=ct)
queryR=R.matrix(query,nrow=rq,ncol=cq)

# Calculate the alignment vector and corresponding distance
alignment = R.dtw(templateR,queryR,keep=True, step_pattern=R.rabinerJuangStepPattern(4,"c"),open_begin=True,open_end=True)

dist = alignment.rx('distance')[0][0]

print(dist)
print(fbank_feat1.shape)
print(fbank_feat2.shape)