def test_train_align_nnet(basic_corpus_dir, large_prosodylab_format_directory, sick_dict_path, generated_dir, large_dataset_dictionary): args = DummyArgs() args.artificial_neural_net = True args.debug = True args.output_model_path = None args.corpus_directory = basic_corpus_dir args.dictionary_path = large_dataset_dictionary args.output_directory = os.path.join(generated_dir, 'nnet_basic_output_selftrained') train_and_align_corpus(args)
def test_align_single_speaker_prosodylab( single_speaker_prosodylab_format_directory, large_dataset_dictionary, prosodylab_output_directory, prosodylab_output_model_path): corpus_dir = single_speaker_prosodylab_format_directory dict_path = large_dataset_dictionary output_directory = prosodylab_output_directory output_model_path = prosodylab_output_model_path args = DummyArgs() args.num_jobs = 2 args.fast = True train_and_align_corpus(corpus_dir, dict_path, output_directory, '', output_model_path, args)
def test_nnet_export_model(large_prosodylab_format_directory, config_directory, generated_dir, large_dataset_dictionary, temp_dir): args = DummyArgs() args.debug = True args.clean = True args.config_path = os.path.join(config_directory, 'long_nnet_train.yaml') args.output_model_path = os.path.join(generated_dir, 'nnet_test_model.zip') args.corpus_directory = large_prosodylab_format_directory args.dictionary_path = large_dataset_dictionary args.output_directory = os.path.join( generated_dir, 'nnet_basic_output_selftrained_outputting_model3') args.temp_directory = temp_dir train_and_align_corpus(args)
def test_train_single_speaker_prosodylab( single_speaker_prosodylab_format_directory, large_dataset_dictionary, prosodylab_output_directory, prosodylab_output_model_path): args = DummyArgs() args.num_jobs = 2 args.fast = True args.corpus_directory = single_speaker_prosodylab_format_directory args.dictionary_path = large_dataset_dictionary args.output_directory = prosodylab_output_directory args.output_model_path = prosodylab_output_model_path train_and_align_corpus(args, skip_input=True) # assert_export_exist(single_speaker_prosodylab_format_directory, prosodylab_output_directory) assert (os.path.exists(args.output_model_path))
def test_train_large_textgrid(large_textgrid_format_directory, large_dataset_dictionary, textgrid_output_directory, textgrid_output_model_path): args = DummyArgs() args.num_jobs = 2 args.fast = True args.corpus_directory = large_textgrid_format_directory args.dictionary_path = large_dataset_dictionary args.output_directory = textgrid_output_directory args.output_model_path = textgrid_output_model_path train_and_align_corpus(args, skip_input=True) # assert_export_exist(large_textgrid_format_directory, textgrid_output_directory) assert (os.path.exists(args.output_model_path))
def test_train_large_textgrid(large_textgrid_format_directory, large_dataset_dictionary, textgrid_output_directory, textgrid_output_model_path): corpus_dir = large_textgrid_format_directory dict_path = large_dataset_dictionary output_directory = textgrid_output_directory output_model_path = textgrid_output_model_path args = DummyArgs() args.num_jobs = 2 args.fast = True train_and_align_corpus(corpus_dir, dict_path, output_directory, '', output_model_path, args) #assert_export_exist(large_textgrid_format_directory, textgrid_output_directory) assert (os.path.exists(output_model_path))
def test_train_single_speaker_prosodylab( single_speaker_prosodylab_format_directory, large_dataset_dictionary, prosodylab_output_directory, prosodylab_output_model_path, temp_dir, basic_train_config): args = DummyArgs() args.num_jobs = 2 args.clean = True args.temp_directory = temp_dir args.corpus_directory = single_speaker_prosodylab_format_directory args.dictionary_path = large_dataset_dictionary args.output_directory = prosodylab_output_directory args.output_model_path = prosodylab_output_model_path args.config_path = basic_train_config train_and_align_corpus(args) # assert_export_exist(single_speaker_prosodylab_format_directory, prosodylab_output_directory) assert (os.path.exists(args.output_model_path))
def test_train_large_textgrid(large_textgrid_format_directory, large_dataset_dictionary, textgrid_output_directory, textgrid_output_model_path, temp_dir, basic_train_config): args = DummyArgs() args.num_jobs = 2 args.clean = True args.corpus_directory = large_textgrid_format_directory args.dictionary_path = large_dataset_dictionary args.output_directory = textgrid_output_directory args.output_model_path = textgrid_output_model_path args.temp_directory = temp_dir args.config_path = basic_train_config train_and_align_corpus(args) # assert_export_exist(large_textgrid_format_directory, textgrid_output_directory) assert (os.path.exists(args.output_model_path))
def test_train_large_prosodylab(large_prosodylab_format_directory, large_dataset_dictionary, prosodylab_output_directory, prosodylab_output_model_path, temp_dir): args = DummyArgs() args.num_jobs = 2 args.fast = True args.corpus_directory = large_prosodylab_format_directory args.dictionary_path = large_dataset_dictionary args.output_directory = prosodylab_output_directory args.output_model_path = prosodylab_output_model_path args.temp_directory = temp_dir train_and_align_corpus(args, skip_input=True) # assert_export_exist(large_prosodylab_format_directory, prosodylab_output_directory) assert (os.path.exists(args.output_model_path)) args.clean = False train_and_align_corpus(args, skip_input=True)
def test_train_large_prosodylab(large_prosodylab_format_directory, large_dataset_dictionary, prosodylab_output_directory, prosodylab_output_model_path, temp_dir, basic_train_config, skip_large): if skip_large: pytest.skip('Large testsets disabled') args = DummyArgs() args.num_jobs = 2 args.clean = True args.corpus_directory = large_prosodylab_format_directory args.dictionary_path = large_dataset_dictionary args.output_directory = prosodylab_output_directory args.output_model_path = prosodylab_output_model_path args.temp_directory = temp_dir args.config_path = basic_train_config train_and_align_corpus(args) # assert_export_exist(large_prosodylab_format_directory, prosodylab_output_directory) assert (os.path.exists(args.output_model_path)) train_and_align_corpus(args)