def merge_conll_mmax(conll_wlist, mmax_wlist):
"""Align elements from conll_wlist and mmax_wlist.
conll_wlist's are lists of 3-tuples in which first element is conll word,
second and third elements are sentence and word id of this word,
respectively. mmax_list is a list of two tuples, in which first element is
MMAX word and second element is MMAX id of this word. This method
determines to which CONLL word given MMAX corresponds and returns an
augmented CONLL list whose tuples contain one additional 4-th element which
is itself a list of MMAX word id's corresponding to given CONLL word.
@param conll_wlist - list of 3-tuples with CONLL word, its sentence and
word id
@param mmax_wlist - list of 2-tuples with MMAX word and its MMAX id
@return list of three elements tuples, in which first element will be
sentence id of CONLL word, second element will be word's id in that
sentence, and the third element will be a list of MMAX word ids
corresponding to given CONLL word
"""
conll_words = [e[0] for e in conll_wlist]
mmax_words = [e[0] for e in mmax_wlist]
s_id = w_id = 0
mw_id_list = []
# aligned_words will be a list of length len(conll_words) in which every
# element will also be a list of the indices of mmax_words which correspond
# to CONLL word at given position
aligned_words = nw_align(conll_words, mmax_words, substitute=_cmp_words)
# join CONLL and MMAX indices
ret = []
for i, mwlist in enumerate(aligned_words):
# append to `ret` a three-tuple with sentence and word indices of
# CONLL word
s_id, w_id = conll_wlist[i][1:]
mw_id_list = [mmax_wlist[mw_id][1] for mw_id in mwlist]
ret.append((s_id, w_id, mw_id_list))
return ret
return performance
# similarity measure used by Needleman-Wunsch algorithm
def note_similarity(score_note, performance_note):
# at the moment we just give a 1 if the pitch matches, 0.5 if it's
# within a tone and 0 if more
# over time this can be tweaked to include velocity, duration, etc
if score_note[0] == performance_note[1]:
return 1
elif abs(score_note[0] - performance_note[1]) < 3:
return 0.5
else:
return 0
if __name__ == "__main__":
score = load_score("../examples/scores/hanon_21_rh.txt")
performance = load_performance("../examples/recordings/hanon_21_rh.txt")
# align score and performance using above similarity function and a penalty
# of -1 for insertions and deletions @@@ might need a lot of tweaking
for i in nw_align(score, performance, note_similarity, -1, -1):
print i
