def recognize_file(filename):
query = wavToFeatures(filename)
# query = lda.transform(query)
result = []
sum_scores = 0
for number, refs in numbers.iteritems():
#score = min(DTW(ref, query) for ref in refs)
score = sum(DTW(ref, query) for ref in refs) / len(refs)
result.append((score, number))
sum_scores += score
result.sort()
return result + [sum_scores]
import os
import sys
import glob
#import mlpy
import numpy
from collections import defaultdict
from mfcc import wavToFeatures
from dtw import DTW
# Load reference vectors
numbers = defaultdict(lambda: [])
for filename in glob.glob('../voice/numbers/*/*.wav'):
i = int(os.path.basename(filename).split('_')[0])
numbers[i].append(wavToFeatures(filename))
#x = numpy.concatenate([numpy.concatenate(ns) for ns in numbers.itervalues()])
#y = numpy.concatenate([[i]*sum(len(m) for m in ns) for i, ns in numbers.iteritems()])
#lda = mlpy.LDA()
#lda.learn(x, y)
#for i in numbers:
# numbers[i] = [lda.transform(m) for m in numbers[i]]
def recognize_file(filename):
query = wavToFeatures(filename)
# query = lda.transform(query)
result = []
sum_scores = 0
