def run():
# ******************** Settings ********************
gs.ALIAS_AND_OUTPUT_SUBDIR = os.path.splitext(
os.path.basename(__file__))[0][7:]
rasr.flow.FlowNetwork.default_flags = {"cache_mode": "task_dependent"}
# ******************** GMM Init ********************
mfcc_cepstrum_options = {
"normalize": False,
"outputs": 16,
"add_epsilon": True,
"epsilon": 1e-10,
}
gt_options_extra_args = {
"normalize": False,
}
train_data_inputs, dev_data_inputs, test_data_inputs = data_setups.get_corpus_data_inputs(
)
init_args = lbs_setups.get_init_args(
dc_detection=False,
mfcc_cepstrum_options=mfcc_cepstrum_options,
gt_options_extra_args=gt_options_extra_args,
)
mono_args = lbs_setups.get_monophone_args(train_align_iter=90,
allow_zero_weights=True)
cart_args = lbs_setups.get_cart_args()
tri_args = lbs_setups.get_triphone_args()
vtln_args = lbs_setups.get_vtln_args(allow_zero_weights=True)
sat_args = lbs_setups.get_sat_args(allow_zero_weights=True)
vtln_sat_args = lbs_setups.get_vtln_sat_args()
final_output_args = lbs_setups.get_final_output()
steps = rasr_util.RasrSteps()
steps.add_step("extract", init_args.feature_extraction_args)
steps.add_step("mono", mono_args)
steps.add_step("cart", cart_args)
steps.add_step("tri", tri_args)
steps.add_step("vtln", vtln_args)
steps.add_step("sat", sat_args)
steps.add_step("vtln+sat", vtln_sat_args)
steps.add_step("output", final_output_args)
# ******************** GMM System ********************
lbs_gmm_system = gmm_system.GmmSystem()
lbs_gmm_system.init_system(
hybrid_init_args=init_args,
train_data=train_data_inputs,
dev_data=dev_data_inputs,
test_data=test_data_inputs,
)
lbs_gmm_system.run(steps)
def run_baseline_training():
train, dev, test = get_corpus_data_inputs()
gs.ALIAS_AND_OUTPUT_SUBDIR = 'experiments/librispeech/librispeech_100_gmm/unfolded_monophone_baseline'
system = gmm_system.GmmSystem()
start = time.time()
system.init_system(hybrid_init_args=get_init_args(),
monophone_args=get_monophone_args(),
triphone_args=None,
vtln_args=None,
sat_args=None,
vtln_sat_args=None,
train_data=train,
dev_data=dev,
test_data=test)
print("init_system took: %.1f" % (time.time() - start))
start = time.time()
system.run(["extract", "mono"])
print("run took: %.1f" % (time.time() - start))
gs.ALIAS_AND_OUTPUT_SUBDIR = ''
return system
def run_nodc_baseline():
train, dev, test = get_corpus_data_inputs()
gs.ALIAS_AND_OUTPUT_SUBDIR = 'experiments/librispeech/librispeech_100_gmm/full_gmm_nodc_baseline'
system = gmm_system.GmmSystem()
start = time.time()
system.init_system(hybrid_init_args=get_init_args(dc_detection=False),
monophone_args=get_monophone_args(),
cart_args=get_cart_args(folded=False),
triphone_args=get_triphone_args(),
vtln_args=get_vtln_args(),
sat_args=get_sat_args(),
vtln_sat_args=get_vtln_sat_args(),
train_data=train,
dev_data=dev,
test_data=test)
print("init_system took: %.1f" % (time.time()-start))
start = time.time()
system.run(["extract", "mono", "cart", "tri", "vtln", "sat", "vtln+sat"])
print("run took: %.1f" % (time.time()-start))
gs.ALIAS_AND_OUTPUT_SUBDIR = ''
return system
def run():
# ******************** Settings ********************
filename_handle = os.path.splitext(os.path.basename(__file__))[0]
gs.ALIAS_AND_OUTPUT_SUBDIR = f"{filename_handle}/"
rasr.flow.FlowNetwork.default_flags = {"cache_mode": "task_dependent"}
# ******************** GMM Init ********************
(
train_data_inputs,
dev_data_inputs,
test_data_inputs,
) = lbs_gmm_setups.get_data_inputs(use_eval_data_subset=True, )
hybrid_init_args = lbs_gmm_setups.get_init_args()
mono_args = lbs_gmm_setups.get_monophone_args(allow_zero_weights=True)
cart_args = lbs_gmm_setups.get_cart_args()
tri_args = lbs_gmm_setups.get_triphone_args()
vtln_args = lbs_gmm_setups.get_vtln_args(allow_zero_weights=True)
sat_args = lbs_gmm_setups.get_sat_args(allow_zero_weights=True)
vtln_sat_args = lbs_gmm_setups.get_vtln_sat_args()
final_output_args = lbs_gmm_setups.get_final_output()
steps = rasr_util.RasrSteps()
steps.add_step("extract", hybrid_init_args.feature_extraction_args)
steps.add_step("mono", mono_args)
steps.add_step("cart", cart_args)
steps.add_step("tri", tri_args)
steps.add_step("vtln", vtln_args)
steps.add_step("sat", sat_args)
steps.add_step("vtln+sat", vtln_sat_args)
steps.add_step("output", final_output_args)
# ******************** GMM System ********************
lbs_gmm_system = gmm_system.GmmSystem()
lbs_gmm_system.init_system(
hybrid_init_args=hybrid_init_args,
train_data=train_data_inputs,
dev_data=dev_data_inputs,
test_data=test_data_inputs,
)
lbs_gmm_system.run(steps)
train_corpus_path = lbs_gmm_system.corpora["train-other-960"].corpus_file
total_train_num_segments = 281241
cv_size = 3000 / total_train_num_segments
all_segments = corpus_recipe.SegmentCorpusJob(
train_corpus_path, 1).out_single_segment_files[1]
splitted_segments_job = corpus_recipe.ShuffleAndSplitSegmentsJob(
all_segments, {
"train": 1 - cv_size,
"cv": cv_size
})
train_segments = splitted_segments_job.out_segments["train"]
cv_segments = splitted_segments_job.out_segments["cv"]
devtrain_segments = text.TailJob(train_segments,
num_lines=1000,
zip_output=False).out
# ******************** NN Init ********************
nn_train_data = lbs_gmm_system.outputs["train-other-960"][
"final"].as_returnn_rasr_data_input(shuffle_data=True)
nn_train_data.update_crp_with(segment_path=train_segments, concurrent=1)
nn_train_data_inputs = {
"train-other-960.train": nn_train_data,
}
nn_cv_data = lbs_gmm_system.outputs["train-other-960"][
"final"].as_returnn_rasr_data_input()
nn_cv_data.update_crp_with(segment_path=cv_segments, concurrent=1)
nn_cv_data_inputs = {
"train-other-960.cv": nn_cv_data,
}
nn_devtrain_data = lbs_gmm_system.outputs["train-other-960"][
"final"].as_returnn_rasr_data_input()
nn_devtrain_data.update_crp_with(segment_path=devtrain_segments,
concurrent=1)
nn_devtrain_data_inputs = {
"train-other-960.devtrain": nn_devtrain_data,
}
nn_dev_data_inputs = {
# "dev-clean": lbs_gmm_system.outputs["dev-clean"][
# "final"
# ].as_returnn_rasr_data_input(),
"dev-other":
lbs_gmm_system.outputs["dev-other"]
["final"].as_returnn_rasr_data_input(),
}
nn_test_data_inputs = {
# "test-clean": lbs_gmm_system.outputs["test-clean"][
# "final"
# ].as_returnn_rasr_data_input(),
# "test-other": lbs_gmm_system.outputs["test-other"][
# "final"
# ].as_returnn_rasr_data_input(),
}
nn_args = lbs_nn_setups.get_nn_args()
nn_steps = rasr_util.RasrSteps()
nn_steps.add_step("nn", nn_args)
# ******************** NN System ********************
returnn_repo = CloneGitRepositoryJob(
url="[email protected]:rwth-i6/returnn.git",
checkout_folder_name="returnn")
lbs_nn_system = nn_system.NnSystem(
returnn_root=returnn_repo.out_repository)
lbs_nn_system.init_system(
hybrid_init_args=hybrid_init_args,
train_data=nn_train_data_inputs,
cv_data=nn_cv_data_inputs,
devtrain_data=nn_devtrain_data_inputs,
dev_data=nn_dev_data_inputs,
test_data=nn_test_data_inputs,
train_cv_pairing=[
tuple(["train-other-960.train", "train-other-960.cv"])
],
)
lbs_nn_system.run(nn_steps)
gs.ALIAS_AND_OUTPUT_SUBDIR = ""