def create_hparams(hparams_string=None, verbose=False):
# Combine parameters needed for training acoustic and duration model.
hparams = AcousticModelTrainer.create_hparams(hparams_string,
verbose=False)
hparams_duration = DurationModelTrainer.create_hparams(hparams_string,
verbose=False)
hparams.override_from_hparam(hparams_duration)
# Add parameters required for full TTS.
hparams.add_hparams(front_end=None,
front_end_accent=None,
festival_dir=None,
file_symbol_dict=None,
num_phoneme_states=None,
duration_labels_dir=None,
duration_norm_file_name=None,
duration_model=None,
question_labels_norm_file=None,
world_features_dir=None,
acoustic_model=None,
synth_load_org_lf0=False,
synth_load_org_vuv=False,
synth_load_org_bap=False)
if verbose:
logging.info(hparams.get_debug_string())
return hparams
def _get_duration_hparams(self):
hparams = DurationModelTrainer.create_hparams()
hparams.out_dir = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
type(self).__name__)
hparams.seed = 1234
hparams.epochs = 0
hparams.use_gpu = False
hparams.model_type = "RNNDYN-1_RELU_32-1_FC_5"
hparams.model_name = "DM.nn"
return hparams
def _get_duration_trainer(self, hparams):
dir_durations = os.path.join("integration", "fixtures", "dur")
if hparams.phoneme_label_type == "HTK full":
dir_phoneme_labels = os.path.join("integration", "fixtures",
"labels", "label_state_align")
elif hparams.phoneme_label_type == "mono_no_align":
dir_phoneme_labels = os.path.join("integration", "fixtures",
"labels", "mono_no_align")
else:
raise NotImplementedError("Unknown label type {}.".format(
hparams.phoneme_label_type))
file_symbol_dict = os.path.join("integration", "fixtures", "labels",
"mono_phone.list")
return DurationModelTrainer(dir_phoneme_labels, dir_durations,
self.id_list, file_symbol_dict, hparams)
def _get_hparams(self):
hparams = DurationModelTrainer.create_hparams()
# General parameters
# hparams.voice = "full"
hparams.data_dir = os.path.realpath(
os.path.join("integration", "fixtures", "database"))
hparams.out_dir = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
type(self).__name__)
hparams.seed = 1
# Training parameters.
hparams.epochs = 3
hparams.use_gpu = False
hparams.model_type = "RNNDYN-1_RELU_32-1_FC_5"
hparams.batch_size_train = 64
hparams.batch_size_val = 64
hparams.add_hparam("learning_rate",
0.001) # Test backwards compatibility.
hparams.model_name = "test_model.nn"
hparams.epochs_per_checkpoint = 2
return hparams
def run_DM_AM(hparams, input_strings):
"""
A function for TTS with a pre-trained duration and acoustic model.
:param hparams: Hyper-parameter container. The following parameters are used:
front_end: Full path to the makeLabels.sh script in scripts/tts_frontend, depends on the language.
festival_dir: Full path to the directory with the festival bin/ folder.
front_end_accent (optional): Give an accent to the front_end, used in tts_frontend.
duration_labels_dir: Full path to the folder containing the normalisation parameters used to train the duration model.
file_symbol_dict: A file containing all the used phonemes (has been used to train the duration model, usually mono_phone.list).
duration_model: Full path to the pre-trained duration model.
num_phoneme_states: Number of states per phoneme, for each a duration is predicted by the duration model.
question_file: Full path to question file used to train the acoustic model.
question_labels_norm_file: Full path to normalisation file of questions used to train the acoustic model.
num_questions: Number of questions which form the input dimension to the acoustic model.
acoustic_model: Full path to acoustic model.
:param input_strings:
:return:
"""
# Create a temporary directory to store all files.
with tempfile.TemporaryDirectory() as tmp_dir_name:
# tmp_dir_name = os.path.realpath("TMP")
# makedirs_safe(tmp_dir_name)
hparams.out_dir = tmp_dir_name
print("Created temporary directory", tmp_dir_name)
id_list = ["synth" + str(idx) for idx in range(len(input_strings))]
# Write the text to synthesise into a single synth.txt file with ids.
utts_file = os.path.join(tmp_dir_name, "synth.txt")
with open(utts_file, "w") as text_file:
for idx, text in enumerate(input_strings):
text_file.write("synth{}\t{}\n".format(
idx, text)) # TODO: Remove parenthesis etc.
# Call the front end on the synth.txt file.
front_end_arguments = [
hparams.front_end, hparams.festival_dir, utts_file
]
if hasattr(hparams, "front_end_accent"
) and hparams.front_end_accent is not None:
front_end_arguments.append(hparams.front_end_accent)
front_end_arguments.append(tmp_dir_name)
subprocess.check_call(front_end_arguments)
# Remove durations from mono labels.
dir_mono_no_align = os.path.join(tmp_dir_name, "mono_no_align")
dir_mono = os.path.join(tmp_dir_name, "labels", "mono")
if os.path.isdir(dir_mono_no_align):
shutil.rmtree(dir_mono_no_align)
os.rename(dir_mono, dir_mono_no_align)
for id_name in id_list:
with open(os.path.join(dir_mono_no_align, id_name + ".lab"),
"r") as f:
old = f.read()
monophones = old.split()[2::3]
with open(os.path.join(dir_mono_no_align, id_name + ".lab"),
"w") as f:
f.write("\n".join(monophones))
# Run duration model.
hparams.batch_size_test = len(input_strings)
hparams.test_set_perc = 0.0
hparams.val_set_perc = 0.0
hparams.phoneme_label_type = "mono_no_align"
hparams.output_norm_params_file_prefix = hparams.duration_norm_file_name if hasattr(
hparams, "duration_norm_file_name") else None
duration_model_trainer = DurationModelTrainer(
os.path.join(tmp_dir_name,
"mono_no_align"), hparams.duration_labels_dir,
id_list, hparams.file_symbol_dict, hparams)
assert hparams.duration_model is not None, "Path to duration model in hparams.duration_model is needed."
hparams.model_path = hparams.duration_model
hparams.model_name = os.path.basename(hparams.duration_model)
# Predict durations. Durations are already converted to multiples of hparams.min_phoneme_length.
hparams.load_from_checkpoint = True
duration_model_trainer.init(hparams)
_, output_dict_post = duration_model_trainer.forward(
hparams, id_list)
hparams.output_norm_params_file_prefix = None # Reset again.
# Write duration to full labels.
dir_full = os.path.join(tmp_dir_name, "labels", "full")
dir_label_state_align = os.path.join(tmp_dir_name, "labels",
"label_state_align")
makedirs_safe(dir_label_state_align)
for id_name in id_list:
with open(os.path.join(dir_full, id_name + ".lab"), "r") as f:
full = f.read().split()[2::3]
with open(
os.path.join(dir_label_state_align, id_name + ".lab"),
"w") as f:
current_time = 0
timings = output_dict_post[id_name]
for idx, monophone in enumerate(full):
for state in range(hparams.num_phoneme_states):
next_time = current_time + int(timings[idx, state])
f.write("{}\t{}\t{}[{}]\n".format(
current_time, next_time, monophone, state + 2))
current_time = next_time
# Generate questions from HTK full labels.
QuestionLabelGen.gen_data(dir_label_state_align,
hparams.question_file,
dir_out=tmp_dir_name,
file_id_list="synth",
id_list=id_list,
return_dict=False)
# Run acoustic model and synthesise.
shutil.copy2(hparams.question_labels_norm_file,
tmp_dir_name + "/min-max.bin"
) # Get normalisation parameters in same directory.
acoustic_model_trainer = AcousticModelTrainer(
hparams.world_features_dir, tmp_dir_name, id_list,
hparams.num_questions, hparams)
assert hparams.acoustic_model is not None, "Path to acoustic model in hparams.acoustic_model is needed."
hparams.model_path = hparams.acoustic_model
hparams.model_name = os.path.basename(hparams.acoustic_model)
hparams.load_from_checkpoint = True
acoustic_model_trainer.init(hparams)
hparams.model_name = "" # No suffix in synthesised files.
_, output_dict_post = acoustic_model_trainer.synth(
hparams, id_list)
logging.info("Synthesized files are in {}.".format(
hparams.synth_dir))
return 0