def train(self, corpus, text_corpus):
'''
Inherited method to do processor training. This just makes utterance objects from
the XML files of the corpus, and filters the utterances in the
corpus to find the ones this processor is configured to train on, and which have
not been flagged as bad (status != OK).
The actual processor-specific work should be subclassed in do_training
'''
corpus = [Utterance(fname) for fname in corpus]
corpus = [utt for utt in corpus if utt.get(self.train_on_utts_which_have)]
corpus = [utt for utt in corpus if utt.get('status') == 'OK']
self.do_training(corpus, text_corpus)
# pylab.subplot('515')
#
# pylab.plot(recon)
pylab.show()
### utt audio access:
if False:
import numpy
utt = Utterance('/afs/inf.ed.ac.uk/group/cstr/projects/blizzard_entries/blizzard2016/tool/Ossian/train/en/speakers/fls_2016_segmented_TOY/english_blizz16_02_prom_annotation/utt/AMidsummerNightsDream_000.utt')
print utt
utt.pretty_print()
print [utt.get('acoustic_stream_names') ]
print '-----'
for (word_i,word) in enumerate(utt.xpath('//token[@token_class="word"]')):
print word.get('norm_text')
d = utt.get_acoustic_features(word, 'lf0')
i = utt.get_acoustic_features(word, 'lf0', interpolate_fzero=True)
m = utt.get_acoustic_statistics(word, 'mgc', dim=0)
s = utt.get_acoustic_statistics(word, 'mgc')
l = utt.get_acoustic_statistics(utt, 'lf0', interpolate_fzero=True)
print d
def __call__(self, utterance_file, utterance_location, mode):
utterance = Utterance(utterance_file,
utterance_location=utterance_location)
return self.apply_to_utt(utterance, voice_mode=mode, save=True)