"ValueError: too many values to unpack (expected 2)" on training.py

[ymzlygw]

hi, Thanks for you implement of deepvoice3!

I'm now try to use it training JSUT dataset. The preprocess.py is worked well( I get the extracted features (mel-spectrograms and linear spectrograms)). But when I run the train.py , Error occurs:

Note that I copy the preset of "en" and rename it "deepvoice3_jp.json" and use it in the command of preporcess.py and train.py. I also set the frontend = "jp" both in .json and hparams.py.

My command is : python train.py --preset=presets/deepvoice3_jp.json --data-root=./preprocess_result/
[Errors Message]:

Command line args:
{'--checkpoint': None,
'--checkpoint-dir': 'checkpoints',
'--checkpoint-postnet': None,
'--checkpoint-seq2seq': None,
'--data-root': './preprocess_result/',
'--help': False,
'--hparams': '',
'--load-embedding': None,
'--log-event-path': None,
'--preset': 'presets/deepvoice3_jp.json',
'--reset-optimizer': False,
'--restore-parts': None,
'--speaker-id': None,
'--train-postnet-only': False,
'--train-seq2seq-only': False}
Training whole model
Training seq2seq model
Hyperparameters:
adam_beta1: 0.5
adam_beta2: 0.9
adam_eps: 1e-06
allow_clipping_in_normalization: True
amsgrad: False
batch_size: 8
binary_divergence_weight: 0.1
builder: deepvoice3
checkpoint_interval: 10000
clip_thresh: 0.1
converter_channels: 256
decoder_channels: 256
downsample_step: 4
dropout: 0.050000000000000044
embedding_weight_std: 0.1
encoder_channels: 512
eval_interval: 10000
fft_size: 1024
fmax: 7600
fmin: 125
force_monotonic_attention: True
freeze_embedding: False
frontend: jp
guided_attention_sigma: 0.4
hop_size: 256
ignore_recognition_level: 2
initial_learning_rate: 0.0005
kernel_size: 3
key_position_rate: 7.6
key_projection: True
lr_schedule: noam_learning_rate_decay
lr_schedule_kwargs: {}
masked_loss_weight: 0.5
max_positions: 512
min_level_db: -100
min_text: 20
n_speakers: 1
name: deepvoice3
nepochs: 2000
num_mels: 80
num_workers: 2
outputs_per_step: 1
padding_idx: 0
pin_memory: True
power: 1.4
preemphasis: 0.97
priority_freq: 3000
priority_freq_weight: 0.0
process_only_htk_aligned: False
query_position_rate: 2.0
ref_level_db: 20
replace_pronunciation_prob: 0.5
rescaling: False
rescaling_max: 0.999
sample_rate: 22050
save_optimizer_state: True
speaker_embed_dim: 16
speaker_embedding_weight_std: 0.05
text_embed_dim: 256
trainable_positional_encodings: False
use_decoder_state_for_postnet_input: True
use_guided_attention: True
use_memory_mask: True
value_projection: True
weight_decay: 0.0
window_ahead: 3
window_backward: 1
Log event path: log/run-test2020-08-03_12:52:24.910593
0it [00:00, ?it/s]
Traceback (most recent call last):
File "train.py", line 1016, in
train_seq2seq=train_seq2seq, train_postnet=train_postnet)
File "train.py", line 623, in train
in tqdm(enumerate(data_loader)):
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/tqdm/std.py", line 1130, in iter
for obj in iterable:
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 345, in next
data = self._next_data()
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 856, in _next_data
return self._process_data(data)
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 881, in _process_data
data.reraise()
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch/_utils.py", line 394, in reraise
raise self.exc_type(msg)
ValueError: Caught ValueError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch/utils/data/_utils/worker.py", line 178, in _worker_loop
data = fetcher.fetch(index)
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py", line 44, in
data = [self.dataset[idx] for idx in possibly_batched_index]
File "train.py", line 255, in getitem
return self.X[idx], self.Mel[idx], self.Y[idx]
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/nnmnkwii/datasets/init.py", line 146, in getitem
return self.__collect_features(paths)
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/nnmnkwii/datasets/init.py", line 131, in _collect_features
return self.file_data_source.collect_features(*paths)
File "train.py", line 134, in collect_features
seq = frontend.text_to_sequence(text, p=hparams.replace_pronunciation_prob)
File "/root/AI/deepvoice3_pytorch/deepvoice3_pytorch/frontend/jp/init.py", line 71, in text_to_sequence
text = mix_pronunciation(text, p)
File "/root/AI/deepvoice3_pytorch/deepvoice3_pytorch/frontend/jp/init.py", line 42, in mix_pronunciation
tokens, yomis = _yomi(tagger.parse(text))
File "/root/AI/deepvoice3_pytorch/deepvoice3_pytorch/frontend/jp/init.py", line 22, in _yomi
token, rest = s
ValueError: too many values to unpack (expected 2)

How does this problem occurs? Thanks and waiting for your reply!