apply isort

egs/vcc20/tts1_en_fi/local/clean_text_css10.py

@@ -9,10 +9,15 @@
 import os
 
 import nltk
-from tacotron_cleaner.cleaners import (collapse_whitespace,
-                                       expand_abbreviations, expand_numbers,
-                                       expand_symbols, lowercase,
-                                       remove_unnecessary_symbols, uppercase)
+from tacotron_cleaner.cleaners import (
+    collapse_whitespace,
+    expand_abbreviations,
+    expand_numbers,
+    expand_symbols,
+    lowercase,
+    remove_unnecessary_symbols,
+    uppercase,
+)
 
 try:
     # For phoneme conversion, use https://github.com/Kyubyong/g2p.

egs/vcc20/vc1_task2/local/clean_text_finnish.py

@@ -7,11 +7,16 @@
 import codecs
 
 import nltk
-from tacotron_cleaner.cleaners import (collapse_whitespace,
-                                       custom_english_cleaners,
-                                       expand_abbreviations, expand_numbers,
-                                       expand_symbols, lowercase,
-                                       remove_unnecessary_symbols, uppercase)
+from tacotron_cleaner.cleaners import (
+    collapse_whitespace,
+    custom_english_cleaners,
+    expand_abbreviations,
+    expand_numbers,
+    expand_symbols,
+    lowercase,
+    remove_unnecessary_symbols,
+    uppercase,
+)
 
 E_lang_tag = "en_US"
 

egs2/TEMPLATE/ssl1/pyscripts/dump_km_label.py

@@ -8,8 +8,7 @@
 import numpy as np
 import torch
 import tqdm
-from sklearn_km import (HubertFeatureReader, MfccFeatureReader,
-                        get_path_iterator)
+from sklearn_km import HubertFeatureReader, MfccFeatureReader, get_path_iterator
 
 logging.basicConfig(
     level=logging.DEBUG,

egs2/TEMPLATE/ssl1/pyscripts/feature_loader.py

@@ -7,7 +7,7 @@
 #     Paper: https://arxiv.org/pdf/2106.07447.pdf
 #     Code in Fairseq: https://github.com/pytorch/fairseq/tree/master/examples/hubert
 
-"""Extract MFCC & intermediate embedding from the Hubert model for k-means clustering."""
+"""Extract MFCC & intermediate embedding from the Hubert model for k-means clustering"""
 
 import logging
 import os

egs2/aishell4/enh1/local/split_train_dev_by_column.py

@@ -7,8 +7,7 @@
 from collections import defaultdict
 from pathlib import Path
 
-from split_train_dev import (int_or_float_or_numstr, split_train_dev,
-                             split_train_dev_v2)
+from split_train_dev import int_or_float_or_numstr, split_train_dev, split_train_dev_v2
 
 
 def get_parser():

egs2/aishell4/enh1/local/split_train_dev_by_prefix.py

@@ -7,8 +7,7 @@
 from collections import defaultdict
 from pathlib import Path
 
-from split_train_dev import (int_or_float_or_numstr, split_train_dev,
-                             split_train_dev_v2)
+from split_train_dev import int_or_float_or_numstr, split_train_dev, split_train_dev_v2
 
 
 def get_parser():

egs2/seame/asr1/local/split_lang_trn.py

@@ -4,9 +4,12 @@
 import argparse
 import os
 
-from preprocess import (extract_mandarin_only, extract_non_mandarin,
-                        insert_space_between_mandarin,
-                        remove_redundant_whitespaces)
+from preprocess import (
+    extract_mandarin_only,
+    extract_non_mandarin,
+    insert_space_between_mandarin,
+    remove_redundant_whitespaces,
+)
 
 if __name__ == "__main__":
     # Parse arguments

espnet/asr/chainer_backend/asr.py

@@ -17,19 +17,27 @@
 # rnnlm
 import espnet.lm.chainer_backend.extlm as extlm_chainer
 import espnet.lm.chainer_backend.lm as lm_chainer
+
 # espnet related
-from espnet.asr.asr_utils import (CompareValueTrigger, adadelta_eps_decay,
-                                  add_results_to_json, chainer_load,
-                                  get_model_conf, restore_snapshot)
+from espnet.asr.asr_utils import (
+    CompareValueTrigger,
+    adadelta_eps_decay,
+    add_results_to_json,
+    chainer_load,
+    get_model_conf,
+    restore_snapshot,
+)
 from espnet.nets.asr_interface import ASRInterface
 from espnet.utils.deterministic_utils import set_deterministic_chainer
 from espnet.utils.dynamic_import import dynamic_import
 from espnet.utils.io_utils import LoadInputsAndTargets
 from espnet.utils.training.batchfy import make_batchset
 from espnet.utils.training.evaluator import BaseEvaluator
 from espnet.utils.training.iterators import (
-    ShufflingEnabler, ToggleableShufflingMultiprocessIterator,
-    ToggleableShufflingSerialIterator)
+    ShufflingEnabler,
+    ToggleableShufflingMultiprocessIterator,
+    ToggleableShufflingSerialIterator,
+)
 from espnet.utils.training.tensorboard_logger import TensorboardLogger
 from espnet.utils.training.train_utils import check_early_stop, set_early_stop
 
@@ -270,8 +278,7 @@ def train(args):
             trigger=(args.sortagrad if args.sortagrad != -1 else args.epochs, "epoch"),
         )
     if args.opt == "noam":
-        from espnet.nets.chainer_backend.transformer.training import \
-            VaswaniRule
+        from espnet.nets.chainer_backend.transformer.training import VaswaniRule
 
         trainer.extend(
             VaswaniRule(

espnet/asr/pytorch_backend/asr.py

@@ -21,14 +21,24 @@
 
 import espnet.lm.pytorch_backend.extlm as extlm_pytorch
 import espnet.nets.pytorch_backend.lm.default as lm_pytorch
-from espnet.asr.asr_utils import (CompareValueTrigger, adadelta_eps_decay,
-                                  add_results_to_json, format_mulenc_args,
-                                  get_model_conf, plot_spectrogram,
-                                  restore_snapshot, snapshot_object,
-                                  torch_load, torch_resume, torch_snapshot)
-from espnet.asr.pytorch_backend.asr_init import (freeze_modules,
-                                                 load_trained_model,
-                                                 load_trained_modules)
+from espnet.asr.asr_utils import (
+    CompareValueTrigger,
+    adadelta_eps_decay,
+    add_results_to_json,
+    format_mulenc_args,
+    get_model_conf,
+    plot_spectrogram,
+    restore_snapshot,
+    snapshot_object,
+    torch_load,
+    torch_resume,
+    torch_snapshot,
+)
+from espnet.asr.pytorch_backend.asr_init import (
+    freeze_modules,
+    load_trained_model,
+    load_trained_modules,
+)
 from espnet.nets.asr_interface import ASRInterface
 from espnet.nets.beam_search_transducer import BeamSearchTransducer
 from espnet.nets.pytorch_backend.e2e_asr import pad_list
@@ -500,8 +510,7 @@ def train(args):
     elif args.opt == "adam":
         optimizer = torch.optim.Adam(model_params, weight_decay=args.weight_decay)
     elif args.opt == "noam":
-        from espnet.nets.pytorch_backend.transformer.optimizer import \
-            get_std_opt
+        from espnet.nets.pytorch_backend.transformer.optimizer import get_std_opt
 
         if "transducer" in mtl_mode:
             if args.noam_adim > 0:

espnet/asr/pytorch_backend/asr_mix.py

@@ -13,19 +13,29 @@
 
 import numpy as np
 import torch
+
 # chainer related
 from chainer import training
 from chainer.training import extensions
 
 import espnet.lm.pytorch_backend.extlm as extlm_pytorch
 import espnet.nets.pytorch_backend.lm.default as lm_pytorch
 from espnet.asr.asr_mix_utils import add_results_to_json
-from espnet.asr.asr_utils import (CompareValueTrigger, adadelta_eps_decay,
-                                  get_model_conf, restore_snapshot,
-                                  snapshot_object, torch_load, torch_resume,
-                                  torch_snapshot)
-from espnet.asr.pytorch_backend.asr import (CustomEvaluator, CustomUpdater,
-                                            load_trained_model)
+from espnet.asr.asr_utils import (
+    CompareValueTrigger,
+    adadelta_eps_decay,
+    get_model_conf,
+    restore_snapshot,
+    snapshot_object,
+    torch_load,
+    torch_resume,
+    torch_snapshot,
+)
+from espnet.asr.pytorch_backend.asr import (
+    CustomEvaluator,
+    CustomUpdater,
+    load_trained_model,
+)
 from espnet.nets.asr_interface import ASRInterface
 from espnet.nets.pytorch_backend.e2e_asr_mix import pad_list
 from espnet.utils.dataset import ChainerDataLoader, TransformDataset
@@ -217,8 +227,7 @@ def train(args):
     elif args.opt == "adam":
         optimizer = torch.optim.Adam(model.parameters(), weight_decay=args.weight_decay)
     elif args.opt == "noam":
-        from espnet.nets.pytorch_backend.transformer.optimizer import \
-            get_std_opt
+        from espnet.nets.pytorch_backend.transformer.optimizer import get_std_opt
 
         optimizer = get_std_opt(
             model.parameters(),

espnet/asr/pytorch_backend/recog.py

@@ -6,8 +6,7 @@
 import torch
 from packaging.version import parse as V
 
-from espnet.asr.asr_utils import (add_results_to_json, get_model_conf,
-                                  torch_load)
+from espnet.asr.asr_utils import add_results_to_json, get_model_conf, torch_load
 from espnet.asr.pytorch_backend.asr import load_trained_model
 from espnet.nets.asr_interface import ASRInterface
 from espnet.nets.batch_beam_search import BatchBeamSearch

espnet/bin/asr_align.py

@@ -48,9 +48,14 @@
 
 import configargparse
 import torch
+
 # imports for CTC segmentation
-from ctc_segmentation import (CtcSegmentationParameters, ctc_segmentation,
-                              determine_utterance_segments, prepare_text)
+from ctc_segmentation import (
+    CtcSegmentationParameters,
+    ctc_segmentation,
+    determine_utterance_segments,
+    prepare_text,
+)
 
 # imports for inference
 from espnet.asr.pytorch_backend.asr_init import load_trained_model

espnet/lm/chainer_backend/lm.py

@@ -18,14 +18,19 @@
 import six
 from chainer import link, reporter, training
 from chainer.dataset import convert
+
 # for classifier link
 from chainer.functions.loss import softmax_cross_entropy
 from chainer.training import extensions
 
 import espnet.nets.chainer_backend.deterministic_embed_id as DL
-from espnet.lm.lm_utils import (MakeSymlinkToBestModel,
-                                ParallelSentenceIterator, compute_perplexity,
-                                count_tokens, read_tokens)
+from espnet.lm.lm_utils import (
+    MakeSymlinkToBestModel,
+    ParallelSentenceIterator,
+    compute_perplexity,
+    count_tokens,
+    read_tokens,
+)
 from espnet.nets.lm_interface import LMInterface
 from espnet.optimizer.factory import dynamic_import_optimizer
 from espnet.scheduler.chainer import ChainerScheduler

espnet/lm/pytorch_backend/lm.py

@@ -18,11 +18,19 @@
 from chainer.training import extensions
 from torch.nn.parallel import data_parallel
 
-from espnet.asr.asr_utils import (snapshot_object, torch_load, torch_resume,
-                                  torch_snapshot)
-from espnet.lm.lm_utils import (MakeSymlinkToBestModel,
-                                ParallelSentenceIterator, count_tokens,
-                                load_dataset, read_tokens)
+from espnet.asr.asr_utils import (
+    snapshot_object,
+    torch_load,
+    torch_resume,
+    torch_snapshot,
+)
+from espnet.lm.lm_utils import (
+    MakeSymlinkToBestModel,
+    ParallelSentenceIterator,
+    count_tokens,
+    load_dataset,
+    read_tokens,
+)
 from espnet.nets.lm_interface import LMInterface, dynamic_import_lm
 from espnet.optimizer.factory import dynamic_import_optimizer
 from espnet.scheduler.pytorch import PyTorchScheduler

espnet/mt/pytorch_backend/mt.py

@@ -16,12 +16,22 @@
 from chainer import training
 from chainer.training import extensions
 
-from espnet.asr.asr_utils import (CompareValueTrigger, adadelta_eps_decay,
-                                  adam_lr_decay, add_results_to_json,
-                                  restore_snapshot, snapshot_object,
-                                  torch_load, torch_resume, torch_snapshot)
-from espnet.asr.pytorch_backend.asr import (CustomEvaluator, CustomUpdater,
-                                            load_trained_model)
+from espnet.asr.asr_utils import (
+    CompareValueTrigger,
+    adadelta_eps_decay,
+    adam_lr_decay,
+    add_results_to_json,
+    restore_snapshot,
+    snapshot_object,
+    torch_load,
+    torch_resume,
+    torch_snapshot,
+)
+from espnet.asr.pytorch_backend.asr import (
+    CustomEvaluator,
+    CustomUpdater,
+    load_trained_model,
+)
 from espnet.nets.mt_interface import MTInterface
 from espnet.nets.pytorch_backend.e2e_asr import pad_list
 from espnet.utils.dataset import ChainerDataLoader, TransformDataset
@@ -154,8 +164,7 @@ def train(args):
             model.parameters(), lr=args.lr, weight_decay=args.weight_decay
         )
     elif args.opt == "noam":
-        from espnet.nets.pytorch_backend.transformer.optimizer import \
-            get_std_opt
+        from espnet.nets.pytorch_backend.transformer.optimizer import get_std_opt
 
         optimizer = get_std_opt(
             model.parameters(),

espnet/nets/beam_search.py

@@ -7,8 +7,7 @@
 import torch
 
 from espnet.nets.e2e_asr_common import end_detect
-from espnet.nets.scorer_interface import (PartialScorerInterface,
-                                          ScorerInterface)
+from espnet.nets.scorer_interface import PartialScorerInterface, ScorerInterface
 
 
 class Hypothesis(NamedTuple):

espnet/nets/beam_search_transducer.py

@@ -10,10 +10,15 @@
 from espnet.nets.pytorch_backend.transducer.joint_network import JointNetwork
 from espnet.nets.pytorch_backend.transducer.rnn_decoder import RNNDecoder
 from espnet.nets.pytorch_backend.transducer.utils import (
-    create_lm_batch_states, init_lm_state, is_prefix, recombine_hyps,
-    select_k_expansions, select_lm_state, subtract)
-from espnet.nets.transducer_decoder_interface import (ExtendedHypothesis,
-                                                      Hypothesis)
+    create_lm_batch_states,
+    init_lm_state,
+    is_prefix,
+    recombine_hyps,
+    select_k_expansions,
+    select_lm_state,
+    subtract,
+)
+from espnet.nets.transducer_decoder_interface import ExtendedHypothesis, Hypothesis
 
 
 class BeamSearchTransducer:

espnet/nets/chainer_backend/deterministic_embed_id.py

@@ -1,6 +1,7 @@
 import chainer
 import numpy
 import six
+
 # from chainer.functions.connection import embed_id
 from chainer import cuda, function_node, link, variable
 from chainer.initializers import normal

espnet/nets/chainer_backend/e2e_asr.py

@@ -215,8 +215,7 @@ def custom_updater(iters, optimizer, converter, device=-1, accum_grad=1):
     @staticmethod
     def custom_parallel_updater(iters, optimizer, converter, devices, accum_grad=1):
         """Get custom_parallel_updater of the model."""
-        from espnet.nets.chainer_backend.rnn.training import \
-            CustomParallelUpdater
+        from espnet.nets.chainer_backend.rnn.training import CustomParallelUpdater
 
         return CustomParallelUpdater(
             iters,