def smooth(p, gendrop=False, hierarchy=False):
'''
Error smoothing function
Takes the vector output by the neural network
Partitions it into vectors each representing a potential phoneme
Converts those partitions to the closest phoneme vectors
'''
# Partition the input vector into individual phonemes
chunked_list = list(chunks(p, 12))
# Get list of phoneme tuples
phoneme_tuples = phon_to_feat.values()
output_tuple = ()
# For potential phoneme in tuple, find closest phoneme to it using dictionary keying distance to phoneme
for phoneme in chunked_list:
dist_from_realphon = {
dist(phoneme, phoneme_tuples[i]): phoneme_tuples[i]
for i in range(len(phoneme_tuples))
}
smoothed_vector = min(dist_from_realphon.keys())
output_tuple += dist_from_realphon[smoothed_vector]
return output_tuple
def smooth(p):
'''
Error smoothing function
Takes the vector output by the neural network
Takes dictionary that converts suffix to relevant tuple
Converts those partitions to the closest phoneme vectors
'''
# Partition the input vector into individual phonemes
chunked_list = list(chunks(p, n_feat))
# Get list of phoneme tuples
phoneme_tuples = phon_to_feat.values()
output_tuple = ()
# For potential phoneme in tuple, find closest phoneme to it using dictionary keying distance to phoneme
for phoneme in chunked_list:
dist_from_realphon = {
dist(phoneme, phoneme_tuples[i]): phoneme_tuples[i]
for i in range(len(phoneme_tuples))
}
smoothed_vector = min(dist_from_realphon.keys())
output_tuple += dist_from_realphon[smoothed_vector]
return output_tuple
def smooth(p, gendrop=False, hierarchy=False):
'''
Error smoothing function
Takes the vector output by the neural network
Partitions it into vectors each representing a potential phoneme
Converts those partitions to the closest phoneme vectors
'''
# Partition the input vector into individual phonemes
chunked_list = list(chunks(p, 12))
# Get list of phoneme tuples
phoneme_tuples = phon_to_feat.values()
output_tuple = ()
# For potential phoneme in tuple, find closest phoneme to it using dictionary keying distance to phoneme
for phoneme in chunked_list:
dist_from_realphon = {dist(phoneme, phoneme_tuples[i]): phoneme_tuples[i] for i in range(len(phoneme_tuples))}
smoothed_vector = min(dist_from_realphon.keys())
output_tuple += dist_from_realphon[smoothed_vector]
return output_tuple
def smooth(p):
'''
Error smoothing function
Takes the vector output by the neural network
Takes dictionary that converts suffix to relevant tuple
Converts those partitions to the closest phoneme vectors
'''
# Partition the input vector into individual phonemes
chunked_list = list(chunks(p, n_feat))
# Get list of phoneme tuples
phoneme_tuples = phon_to_feat.values()
output_tuple = ()
# For potential phoneme in tuple, find closest phoneme to it using dictionary keying distance to phoneme
for phoneme in chunked_list:
dist_from_realphon = {dist(phoneme, phoneme_tuples[i]): phoneme_tuples[i] for i in range(len(phoneme_tuples))}
smoothed_vector = min(dist_from_realphon.keys())
output_tuple += dist_from_realphon[smoothed_vector]
return output_tuple
