def test_acoustic_njobs(corpus, features, njobs, tmpdir, lang_args):
output_dir = str(tmpdir.mkdir('am-mono'))
flog = os.path.join(output_dir, 'am-mono.log')
log = utils.logger.get_log(flog)
am = acoustic.Monophone(corpus, features, output_dir, lang_args, log)
am.njobs = njobs
am.options['total-gaussians'].value = 10
am.options['num-iterations'].value = 2
am.options['realign-iterations'].value = [1]
try:
am.compute()
except RuntimeError as err:
# dump the log to stdout
sys.stdout.write('####################\n')
sys.stdout.write(open(flog, 'r').read())
# log_inner = os.path.join(
# output_dir, 'recipe/exp/mono/log/align.0.14.log')
# sys.stdout.write(open(log_inner, 'r').read())
sys.stdout.write('####################\n')
raise err
acoustic.check_acoustic_model(output_dir)
assert acoustic.model_type(output_dir) == 'mono'
assert str(am.options['num-iterations']) == '2'
assert_expr_in_log(flog, ' --num-iters 2 ')
assert_no_expr_in_log(flog, 'error')
def am_nnet(corpus, features, am_trisa, tmpdir_factory, lang_args):
output_dir = str(tmpdir_factory.mktemp('am_nnet'))
flog = os.path.join(output_dir, 'am_nnet.log')
log = utils.logger.get_log(flog)
am = acoustic.NeuralNetwork(corpus,
features,
am_trisa,
output_dir,
lang_args,
log=log)
am.options['num-epochs'].value = 2
am.options['num-epochs-extra'].value = 1
am.options['num-hidden-layers'].value = 1
am.options['num-iters-final'].value = 1
am.options['pnorm-input-dim'].value = 1
am.options['pnorm-output-dim'].value = 1
am.options['num-utts-subset'].value = 2
am.compute()
acoustic.check_acoustic_model(output_dir)
# TODO this assert fails since some preliminary steps of the recipe
# generate errors (but this do not impact the recipe)
# # assert_no_expr_in_log(flog, 'error')
return output_dir
def check_parameters(self):
super(Align, self).check_parameters()
self._check_level()
self._check_with_posteriors()
self._check_acoustic_scale()
check_acoustic_model(self.am_dir)
check_language_model(self.lm_dir)
self.meta.source += (', feat = {}, lm = {}, am = {}'.format(
self.feat_dir, self.lm_dir, self.am_dir))
def check_parameters(self):
"""Raise if the decoding parameters are not correct"""
super(Decode, self).check_parameters()
features.Features.check_features(self.feat_dir)
language.check_language_model(self.lm_dir)
acoustic.check_acoustic_model(self.am_dir)
self.meta.source += '\n' + '\n'.join(('features = {}'.format(
self.feat_dir), 'language model = {}'.format(
self.lm_dir), 'acoustic model = {}'.format(self.am_dir)))
def check_parameters(self):
super(Align, self).check_parameters()
self._check_level()
self._check_with_posteriors()
self._check_acoustic_scale()
# late import to avoid circular import (align imports acoustic
# for the convert_alignment_to_kaldi_format function)
from abkhazia.acoustic import check_acoustic_model
check_acoustic_model(self.am_dir)
check_language_model(self.lm_dir)
self.meta.source += (', feat = {}, lm = {}, am = {}'.format(
self.feat_dir, self.lm_dir, self.am_dir))
def am_tri(corpus, features, am_mono, tmpdir_factory, lang_args):
output_dir = str(tmpdir_factory.mktemp('am_tri'))
flog = os.path.join(output_dir, 'am_tri.log')
log = utils.logger.get_log(flog)
am = acoustic.Triphone(corpus,
features,
am_mono,
output_dir,
lang_args,
log=log)
am.options['total-gaussians'].value = 5
am.options['num-iterations'].value = 2
am.options['realign-iterations'].value = [1]
am.options['num-leaves'].value = 20
am.compute()
acoustic.check_acoustic_model(output_dir)
assert_no_expr_in_log(flog, 'error')
return output_dir
