def test_pronunciation_training(mixed_dict_path, basic_corpus_dir,
generated_dir, pron_train_config_path):
data_directory = os.path.join(generated_dir, "temp", "pron_train_test")
export_path = os.path.join(generated_dir, "pron_train_test_export",
"model.zip")
shutil.rmtree(data_directory, ignore_errors=True)
args = argparse.Namespace(use_mp=True, debug=False, verbose=True)
a = TrainableAligner(corpus_directory=basic_corpus_dir,
dictionary_path=mixed_dict_path,
temporary_directory=data_directory,
**TrainableAligner.parse_parameters(
pron_train_config_path, args=args))
a.train()
a.cleanup()
assert not os.path.exists(export_path)
assert not os.path.exists(
os.path.join(generated_dir, "pron_train_test_export",
os.path.basename(mixed_dict_path)))
a = TrainableAligner(corpus_directory=basic_corpus_dir,
dictionary_path=mixed_dict_path,
temporary_directory=data_directory,
**TrainableAligner.parse_parameters(
pron_train_config_path, args=args))
a.train()
a.export_model(export_path)
assert os.path.exists(export_path)
assert os.path.exists(
os.path.join(
generated_dir,
"pron_train_test_export",
os.path.basename(mixed_dict_path).replace(".txt", ".dict"),
))
def test_basic_mono(
mixed_dict_path,
basic_corpus_dir,
generated_dir,
mono_train_config_path,
mono_align_model_path,
mono_output_directory,
):
data_directory = os.path.join(generated_dir, "temp", "mono_train_test")
shutil.rmtree(data_directory, ignore_errors=True)
args = argparse.Namespace(use_mp=True, debug=False, verbose=True)
a = TrainableAligner(corpus_directory=basic_corpus_dir,
dictionary_path=mixed_dict_path,
temporary_directory=data_directory,
**TrainableAligner.parse_parameters(
mono_train_config_path, args=args))
a.train()
a.export_model(mono_align_model_path)
data_directory = os.path.join(generated_dir, "temp", "mono_align_test")
shutil.rmtree(data_directory, ignore_errors=True)
a = PretrainedAligner(corpus_directory=basic_corpus_dir,
dictionary_path=mixed_dict_path,
acoustic_model_path=mono_align_model_path,
temporary_directory=data_directory,
**PretrainedAligner.parse_parameters(args=args))
a.align()
a.export_files(mono_output_directory)
def test_basic_tri(basic_dict_path, basic_corpus_dir, generated_dir,
tri_train_config_path):
data_directory = os.path.join(generated_dir, "temp", "tri_test")
shutil.rmtree(data_directory, ignore_errors=True)
a = TrainableAligner(
corpus_directory=basic_corpus_dir,
dictionary_path=basic_dict_path,
temporary_directory=data_directory,
debug=True,
verbose=True,
**TrainableAligner.parse_parameters(tri_train_config_path))
a.train()
def test_pitch_feature_training(basic_dict_path, basic_corpus_dir,
generated_dir, pitch_train_config_path):
data_directory = os.path.join(generated_dir, "temp", "tri_pitch_test")
shutil.rmtree(data_directory, ignore_errors=True)
a = TrainableAligner(
corpus_directory=basic_corpus_dir,
dictionary_path=basic_dict_path,
temporary_directory=data_directory,
debug=True,
verbose=True,
**TrainableAligner.parse_parameters(pitch_train_config_path))
assert a.use_pitch
a.train()
assert a.get_feat_dim() == 48
def test_basic_lda(basic_dict_path, basic_corpus_dir, generated_dir,
lda_train_config_path):
data_directory = os.path.join(generated_dir, "temp", "lda_test")
shutil.rmtree(data_directory, ignore_errors=True)
a = TrainableAligner(
corpus_directory=basic_corpus_dir,
dictionary_path=basic_dict_path,
temporary_directory=data_directory,
debug=True,
verbose=True,
**TrainableAligner.parse_parameters(lda_train_config_path))
a.train()
assert len(
a.training_configs[a.final_identifier].realignment_iterations) > 0
assert len(a.training_configs[a.final_identifier].mllt_iterations) > 1
def test_basic_sat(basic_dict_path, basic_corpus_dir, generated_dir,
sat_train_config_path):
data_directory = os.path.join(generated_dir, "temp", "sat_test")
output_model_path = os.path.join(data_directory, "sat_model.zip")
shutil.rmtree(data_directory, ignore_errors=True)
args = argparse.Namespace(use_mp=True, debug=True, verbose=True)
a = TrainableAligner(**TrainableAligner.parse_parameters(
sat_train_config_path, args=args),
corpus_directory=basic_corpus_dir,
dictionary_path=basic_dict_path,
temporary_directory=data_directory,
disable_mp=False)
a.train()
assert len(
a.training_configs[a.final_identifier].realignment_iterations) > 0
assert len(a.training_configs[a.final_identifier].fmllr_iterations) > 1
a.export_model(output_model_path)
assert os.path.exists(output_model_path)
assert os.path.exists(
os.path.join(data_directory, "basic_train_acoustic_model", "sat",
"trans.1.0.ark"))