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0b416c6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 | import numpy as np
import itertools as itools
from ridge_utils.DataSequence import DataSequence
DEFAULT_BAD_WORDS = frozenset(["sentence_start", "sentence_end", "br", "lg", "ls", "ns", "sp"])
def make_word_ds(grids, trfiles, bad_words=DEFAULT_BAD_WORDS):
"""Creates DataSequence objects containing the words from each grid, with any words appearing
in the [bad_words] set removed.
"""
ds = dict()
stories = list(set(trfiles.keys()) & set(grids.keys()))
for st in stories:
grtranscript = grids[st].tiers[1].make_simple_transcript()
## Filter out bad words
goodtranscript = [x for x in grtranscript
if x[2].lower().strip("{}").strip() not in bad_words]
d = DataSequence.from_grid(goodtranscript, trfiles[st][0])
ds[st] = d
return ds
def make_phoneme_ds(grids, trfiles):
"""Creates DataSequence objects containing the phonemes from each grid.
"""
ds = dict()
stories = grids.keys()
for st in stories:
grtranscript = grids[st].tiers[0].make_simple_transcript()
d = DataSequence.from_grid(grtranscript, trfiles[st][0])
ds[st] = d
return ds
phonemes = ['AA', 'AE','AH','AO','AW','AY','B','CH','D', 'DH', 'EH', 'ER', 'EY',
'F', 'G', 'HH', 'IH', 'IY', 'JH','K', 'L', 'M', 'N', 'NG', 'OW', 'OY',
'P', 'R', 'S', 'SH', 'T', 'TH', 'UH', 'UW', 'V', 'W', 'Y', 'Z', 'ZH']
def make_character_ds(grids, trfiles):
ds = dict()
stories = grids.keys()
for st in stories:
grtranscript = grids[st].tiers[2].make_simple_transcript()
fixed_grtranscript = [(s,e,map(int, c.split(","))) for s,e,c in grtranscript if c]
d = DataSequence.from_grid(fixed_grtranscript, trfiles[st][0])
ds[st] = d
return ds
def make_dialogue_ds(grids, trfiles):
ds = dict()
for st, gr in grids.iteritems():
grtranscript = gr.tiers[3].make_simple_transcript()
fixed_grtranscript = [(s,e,c) for s,e,c in grtranscript if c]
ds[st] = DataSequence.from_grid(fixed_grtranscript, trfiles[st][0])
return ds
def histogram_phonemes(ds, phonemeset=phonemes):
"""Histograms the phonemes in the DataSequence [ds].
"""
olddata = ds.data
N = len(ds.data)
newdata = np.zeros((N, len(phonemeset)))
phind = dict(enumerate(phonemeset))
for ii,ph in enumerate(olddata):
try:
#ind = phonemeset.index(ph.upper().strip("0123456789"))
ind = phind[ph.upper().strip("0123456789")]
newdata[ii][ind] = 1
except Exception as e:
pass
return DataSequence(newdata, ds.split_inds, ds.data_times, ds.tr_times)
def histogram_phonemes2(ds, phonemeset=phonemes):
"""Histograms the phonemes in the DataSequence [ds].
"""
olddata = np.array([ph.upper().strip("0123456789") for ph in ds.data])
newdata = np.vstack([olddata==ph for ph in phonemeset]).T
return DataSequence(newdata, ds.split_inds, ds.data_times, ds.tr_times)
def make_semantic_model(ds: DataSequence, lsasms: list, sizes: list):
"""
ds
datasequence to operate on
lsasms
semantic models to use
sizes
sizes of resulting vectors from each semantic model
"""
newdata = []
num_lsasms = len(lsasms)
for w in ds.data:
v = []
for i in range(num_lsasms):
lsasm = lsasms[i]
size = sizes[i]
try:
v = np.concatenate((v, lsasm[str.encode(w.lower())]))
except KeyError as e:
v = np.concatenate((v, np.zeros((size)))) #lsasm.data.shape[0],))
newdata.append(v)
return DataSequence(np.array(newdata), ds.split_inds, ds.data_times, ds.tr_times)
def make_character_model(dss):
"""Make character indicator model for a dict of datasequences.
"""
stories = dss.keys()
storychars = dict([(st,np.unique(np.hstack(ds.data))) for st,ds in dss.iteritems()])
total_chars = sum(map(len, storychars.values()))
char_inds = dict()
ncharsdone = 0
for st in stories:
char_inds[st] = dict(zip(storychars[st], range(ncharsdone, ncharsdone+len(storychars[st]))))
ncharsdone += len(storychars[st])
charmodels = dict()
for st,ds in dss.iteritems():
charmat = np.zeros((len(ds.data), total_chars))
for ti,charlist in enumerate(ds.data):
for char in charlist:
charmat[ti, char_inds[st][char]] = 1
charmodels[st] = DataSequence(charmat, ds.split_inds, ds.data_times, ds.tr_times)
return charmodels, char_inds
def make_dialogue_model(ds):
return DataSequence(np.ones((len(ds.data),1)), ds.split_inds, ds.data_times, ds.tr_times)
def modulate(ds, vec):
"""Multiplies each row (each word/phoneme) by the corresponding value in [vec].
"""
return DataSequence((ds.data.T*vec).T, ds.split_inds, ds.data_times, ds.tr_times)
def catmats(*seqs):
keys = seqs[0].keys()
return dict([(k, DataSequence(np.hstack([s[k].data for s in seqs]), seqs[0][k].split_inds)) for k in keys])
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