sort imports

egs/gigaspeech/ASR/pruned_transducer_stateless2/decode.py

@@ -74,14 +74,10 @@ from beam_search import (
     greedy_search_batch,
     modified_beam_search,
 )
-from gigaspeech_scoring import asr_text_post_processing
-from train import get_params, get_transducer_model
+from .gigaspeech_scoring import asr_text_post_processing
+from .train import get_params, get_transducer_model
 
-from icefall.checkpoint import (
-    average_checkpoints,
-    find_checkpoints,
-    load_checkpoint,
-)
+from icefall.checkpoint import average_checkpoints, find_checkpoints, load_checkpoint
 from icefall.utils import (
     AttributeDict,
     setup_logger,

egs/gigaspeech/ASR/pruned_transducer_stateless2/export.py

@@ -51,11 +51,7 @@ import sentencepiece as spm
 import torch
 from train import get_params, get_transducer_model
 
-from icefall.checkpoint import (
-    average_checkpoints,
-    find_checkpoints,
-    load_checkpoint,
-)
+from icefall.checkpoint import average_checkpoints, find_checkpoints, load_checkpoint
 from icefall.utils import str2bool
 
 

icefall/shared/make_kn_lm.py

@@ -15,15 +15,14 @@
 # The data structure is based on: kaldi/egs/wsj/s5/utils/lang/make_phone_lm.py
 # The smoothing algorithm is based on: http://www.speech.sri.com/projects/srilm/manpages/ngram-discount.7.html
 
-import sys
-import os
-import re
+import argparse
 import io
 import math
-import argparse
+import os
+import re
+import sys
 from collections import Counter, defaultdict
 
-
 parser = argparse.ArgumentParser(description="""
     Generate kneser-ney language model as arpa format. By default,
     it will read the corpus from standard input, and output to standard output.