black and isort formatted the project

egs/commonvoice/ASR/pruned_transducer_stateless7/onnx_check.py

@@ -79,10 +79,10 @@ It will generate the following 3 files inside $repo/exp:
 import argparse
 import logging
 
-from icefall import is_module_available
+import torch
 from onnx_pretrained import OnnxModel
 
-import torch
+from icefall import is_module_available
 
 
 def get_parser():

egs/csj/ASR/pruned_transducer_stateless7_streaming/export-for-ncnn.py

@@ -70,9 +70,9 @@ import logging
 from pathlib import Path
 
 import torch
+from do_not_use_it_directly import add_model_arguments, get_params, get_transducer_model
 from scaling_converter import convert_scaled_to_non_scaled
 from tokenizer import Tokenizer
-from do_not_use_it_directly import add_model_arguments, get_params, get_transducer_model
 
 from icefall.checkpoint import (
     average_checkpoints,

egs/gigaspeech/ASR/local/preprocess_gigaspeech.py

@@ -23,6 +23,7 @@ from pathlib import Path
 
 from lhotse import CutSet, SupervisionSegment
 from lhotse.recipes.utils import read_manifests_if_cached
+
 from icefall.utils import str2bool
 
 # Similar text filtering and normalization procedure as in:

egs/gigaspeech/ASR/pruned_transducer_stateless2/decode.py

@@ -76,6 +76,7 @@ from beam_search import (
 )
 from gigaspeech_scoring import asr_text_post_processing
 from train import get_params, get_transducer_model
+
 from icefall.checkpoint import (
     average_checkpoints,
     average_checkpoints_with_averaged_model,

egs/gigaspeech/ASR/zipformer/ctc_decode.py

@@ -88,7 +88,7 @@ import sentencepiece as spm
 import torch
 import torch.nn as nn
 from asr_datamodule import GigaSpeechAsrDataModule
-from train import add_model_arguments, get_params, get_model
+from train import add_model_arguments, get_model, get_params
 
 from icefall.checkpoint import (
     average_checkpoints,

egs/gigaspeech/ASR/zipformer/streaming_decode.py

@@ -51,7 +51,7 @@ from streaming_beam_search import (
 )
 from torch import Tensor, nn
 from torch.nn.utils.rnn import pad_sequence
-from train import add_model_arguments, get_params, get_model
+from train import add_model_arguments, get_model, get_params
 
 from icefall.checkpoint import (
     average_checkpoints,

egs/gigaspeech/KWS/zipformer/decode.py

@@ -42,12 +42,10 @@ import sentencepiece as spm
 import torch
 import torch.nn as nn
 from asr_datamodule import GigaSpeechAsrDataModule
-from beam_search import (
-    keywords_search,
-)
+from beam_search import keywords_search
+from lhotse.cut import Cut
 from train import add_model_arguments, get_model, get_params
 
-from lhotse.cut import Cut
 from icefall import ContextGraph
 from icefall.checkpoint import (
     average_checkpoints,

egs/gigaspeech/KWS/zipformer/finetune.py

@@ -76,6 +76,20 @@ from torch import Tensor
 from torch.cuda.amp import GradScaler
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.tensorboard import SummaryWriter
+from train import (
+    add_model_arguments,
+    add_training_arguments,
+    compute_loss,
+    compute_validation_loss,
+    display_and_save_batch,
+    get_adjusted_batch_count,
+    get_model,
+    get_params,
+    load_checkpoint_if_available,
+    save_checkpoint,
+    scan_pessimistic_batches_for_oom,
+    set_batch_count,
+)
 
 from icefall import diagnostics
 from icefall.checkpoint import remove_checkpoints
@@ -95,21 +109,6 @@ from icefall.utils import (
     str2bool,
 )
 
-from train import (
-    add_model_arguments,
-    add_training_arguments,
-    compute_loss,
-    compute_validation_loss,
-    display_and_save_batch,
-    get_adjusted_batch_count,
-    get_model,
-    get_params,
-    load_checkpoint_if_available,
-    save_checkpoint,
-    scan_pessimistic_batches_for_oom,
-    set_batch_count,
-)
-
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
 
 

egs/librispeech/ASR/conformer_ctc3/test_model.py

@@ -24,8 +24,7 @@ To run this file, do:
 """
 
 import torch
-
-from train import get_params, get_ctc_model
+from train import get_ctc_model, get_params
 
 
 def test_model():

egs/librispeech/ASR/conv_emformer_transducer_stateless2/export-onnx.py

@@ -59,9 +59,9 @@ import onnx
 import torch
 import torch.nn as nn
 from decoder import Decoder
+from do_not_use_it_directly import add_model_arguments, get_params, get_transducer_model
 from emformer import Emformer
 from scaling_converter import convert_scaled_to_non_scaled
-from do_not_use_it_directly import add_model_arguments, get_params, get_transducer_model
 
 from icefall.checkpoint import (
     average_checkpoints,

egs/librispeech/ASR/conv_emformer_transducer_stateless2/jit_pretrained.py

@@ -39,7 +39,7 @@ Usage of this script:
 import argparse
 import logging
 import math
-from typing import List
+from typing import List, Optional
 
 import kaldifeat
 import sentencepiece as spm
@@ -47,7 +47,6 @@ import torch
 import torchaudio
 from kaldifeat import FbankOptions, OnlineFbank, OnlineFeature
 from torch.nn.utils.rnn import pad_sequence
-from typing import Optional, List
 
 
 def get_parser():

egs/librispeech/ASR/long_file_recog/recognize.py

@@ -31,28 +31,28 @@ https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stat
 """
 
 import argparse
-import torch.multiprocessing as mp
-import torch
-import torch.nn as nn
 import logging
 from concurrent.futures import ThreadPoolExecutor
-from typing import List, Optional, Tuple
-
 from pathlib import Path
+from typing import List, Optional, Tuple
 
 import k2
 import sentencepiece as spm
+import torch
+import torch.multiprocessing as mp
+import torch.nn as nn
 from asr_datamodule import AsrDataModule
 from beam_search import (
     fast_beam_search_one_best,
     greedy_search_batch,
     modified_beam_search,
 )
-from icefall.utils import AttributeDict, convert_timestamp, setup_logger
 from lhotse import CutSet, load_manifest_lazy
 from lhotse.cut import Cut
-from lhotse.supervision import AlignmentItem
 from lhotse.serialization import SequentialJsonlWriter
+from lhotse.supervision import AlignmentItem
+
+from icefall.utils import AttributeDict, convert_timestamp, setup_logger
 
 
 def get_parser():

egs/librispeech/ASR/lstm_transducer_stateless2/onnx_check.py

@@ -73,12 +73,11 @@ It will generate the following 3 files inside $repo/exp:
 import argparse
 import logging
 
+import torch
 from onnx_pretrained import OnnxModel
 
 from icefall import is_module_available
 
-import torch
-
 
 def get_parser():
     parser = argparse.ArgumentParser(

egs/librispeech/ASR/pruned_transducer_stateless/my_profile.py

@@ -22,11 +22,12 @@ Usage: ./pruned_transducer_stateless/my_profile.py
 
 import argparse
 import logging
+
 import sentencepiece as spm
 import torch
+from train import add_model_arguments, get_encoder_model, get_params
 
 from icefall.profiler import get_model_profile
-from train import get_encoder_model, add_model_arguments, get_params
 
 
 def get_parser():

egs/librispeech/ASR/pruned_transducer_stateless/onnx_decode.py

@@ -75,8 +75,7 @@ import sentencepiece as spm
 import torch
 import torch.nn as nn
 from asr_datamodule import LibriSpeechAsrDataModule
-
-from onnx_pretrained import greedy_search, OnnxModel
+from onnx_pretrained import OnnxModel, greedy_search
 
 from icefall.utils import setup_logger, store_transcripts, write_error_stats
 

egs/librispeech/ASR/pruned_transducer_stateless3/onnx_check.py

@@ -78,10 +78,10 @@ It will generate the following 3 files inside $repo/exp:
 import argparse
 import logging
 
-from icefall import is_module_available
+import torch
 from onnx_pretrained import OnnxModel
 
-import torch
+from icefall import is_module_available
 
 
 def get_parser():

egs/librispeech/ASR/pruned_transducer_stateless3/onnx_decode.py

@@ -76,8 +76,7 @@ import torch
 import torch.nn as nn
 from asr_datamodule import AsrDataModule
 from librispeech import LibriSpeech
-
-from onnx_pretrained import greedy_search, OnnxModel
+from onnx_pretrained import OnnxModel, greedy_search
 
 from icefall.utils import setup_logger, store_transcripts, write_error_stats
 

egs/librispeech/ASR/pruned_transducer_stateless4/my_profile.py

@@ -22,15 +22,15 @@ Usage: ./pruned_transducer_stateless4/my_profile.py
 
 import argparse
 import logging
+from typing import Tuple
+
 import sentencepiece as spm
 import torch
-
-from typing import Tuple
+from scaling import BasicNorm, DoubleSwish
 from torch import Tensor, nn
+from train import add_model_arguments, get_encoder_model, get_joiner_model, get_params
 
 from icefall.profiler import get_model_profile
-from scaling import BasicNorm, DoubleSwish
-from train import get_encoder_model, get_joiner_model, add_model_arguments, get_params
 
 
 def get_parser():

egs/librispeech/ASR/pruned_transducer_stateless5/onnx_decode.py

@@ -82,8 +82,7 @@ import sentencepiece as spm
 import torch
 import torch.nn as nn
 from asr_datamodule import LibriSpeechAsrDataModule
-
-from onnx_pretrained import greedy_search, OnnxModel
+from onnx_pretrained import OnnxModel, greedy_search
 
 from icefall.utils import setup_logger, store_transcripts, write_error_stats
 

egs/librispeech/ASR/pruned_transducer_stateless7/alignment.py

@@ -20,7 +20,6 @@ from typing import List
 
 import k2
 import torch
-
 from beam_search import Hypothesis, HypothesisList, get_hyps_shape
 
 # The force alignment problem can be formulated as finding

egs/librispeech/ASR/pruned_transducer_stateless7/decode_gigaspeech.py

@@ -107,9 +107,6 @@ import k2
 import sentencepiece as spm
 import torch
 import torch.nn as nn
-
-# from asr_datamodule import LibriSpeechAsrDataModule
-from gigaspeech import GigaSpeechAsrDataModule
 from beam_search import (
     beam_search,
     fast_beam_search_nbest,
@@ -120,6 +117,9 @@ from beam_search import (
     greedy_search_batch,
     modified_beam_search,
 )
+
+# from asr_datamodule import LibriSpeechAsrDataModule
+from gigaspeech import GigaSpeechAsrDataModule
 from gigaspeech_scoring import asr_text_post_processing
 from train import add_model_arguments, get_params, get_transducer_model
 

egs/librispeech/ASR/pruned_transducer_stateless7/generate_model_from_checkpoint.py

@@ -65,16 +65,15 @@ from typing import Dict, List
 
 import sentencepiece as spm
 import torch
-
 from train import add_model_arguments, get_params, get_transducer_model
 
-from icefall.utils import str2bool
 from icefall.checkpoint import (
     average_checkpoints,
     average_checkpoints_with_averaged_model,
     find_checkpoints,
     load_checkpoint,
 )
+from icefall.utils import str2bool
 
 
 def get_parser():

egs/librispeech/ASR/pruned_transducer_stateless7/my_profile.py

@@ -22,15 +22,15 @@ Usage: ./pruned_transducer_stateless7/my_profile.py
 
 import argparse
 import logging
+from typing import Tuple
+
 import sentencepiece as spm
 import torch
-
-from typing import Tuple
+from scaling import BasicNorm, DoubleSwish
 from torch import Tensor, nn
+from train import add_model_arguments, get_encoder_model, get_joiner_model, get_params
 
 from icefall.profiler import get_model_profile
-from scaling import BasicNorm, DoubleSwish
-from train import get_encoder_model, get_joiner_model, add_model_arguments, get_params
 
 
 def get_parser():

egs/librispeech/ASR/pruned_transducer_stateless7/onnx_decode.py

@@ -75,8 +75,7 @@ import sentencepiece as spm
 import torch
 import torch.nn as nn
 from asr_datamodule import LibriSpeechAsrDataModule
-
-from onnx_pretrained import greedy_search, OnnxModel
+from onnx_pretrained import OnnxModel, greedy_search
 
 from icefall.utils import setup_logger, store_transcripts, write_error_stats
 

egs/librispeech/ASR/pruned_transducer_stateless7/test_model.py

@@ -24,7 +24,6 @@ To run this file, do:
 """
 
 import torch
-
 from scaling_converter import convert_scaled_to_non_scaled
 from train import get_params, get_transducer_model
 

egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/ctc_guide_decode_bs.py

@@ -118,8 +118,8 @@ from beam_search import (
     greedy_search_batch,
     modified_beam_search,
 )
-from train import add_model_arguments, get_params, get_transducer_model
 from torch.nn.utils.rnn import pad_sequence
+from train import add_model_arguments, get_params, get_transducer_model
 
 from icefall.checkpoint import (
     average_checkpoints,

egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/lconv.py

@@ -18,10 +18,7 @@ from typing import List, Optional, Tuple, Union
 
 import torch
 import torch.nn as nn
-from scaling import (
-    ActivationBalancer,
-    ScaledConv1d,
-)
+from scaling import ActivationBalancer, ScaledConv1d
 
 
 class LConv(nn.Module):

egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/onnx_pretrained.py

@@ -52,7 +52,7 @@ import onnxruntime as ort
 import sentencepiece as spm
 import torch
 import torchaudio
-from torch.nn.utils.rnn import pad_sequence, pack_padded_sequence
+from torch.nn.utils.rnn import pack_padded_sequence, pad_sequence
 
 from icefall.utils import make_pad_mask
 

egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/onnx_wrapper.py

@@ -14,6 +14,7 @@
 
 import torch
 from torch import nn
+
 from icefall.utils import make_pad_mask
 
 

egs/librispeech/ASR/pruned_transducer_stateless7_streaming/ncnn_custom_layer.py

@@ -4,7 +4,6 @@
 import ncnn
 import numpy as np
 
-
 layer_list = []
 
 

egs/librispeech/ASR/pruned_transducer_stateless7_streaming/streaming-ncnn-decode.py

@@ -42,7 +42,6 @@ import ncnn
 import torch
 import torchaudio
 from kaldifeat import FbankOptions, OnlineFbank, OnlineFeature
-
 from ncnn_custom_layer import RegisterCustomLayers
 
 

egs/librispeech/ASR/tiny_transducer_ctc/decode.py

@@ -1,10 +1,11 @@
 import argparse
 import logging
 import math
+import pprint
 from collections import defaultdict
 from pathlib import Path
 from typing import Dict, List, Optional, Tuple
-import pprint
+
 import k2
 import sentencepiece as spm
 import torch

egs/librispeech/ASR/zipformer/ctc_decode.py

@@ -88,7 +88,7 @@ import sentencepiece as spm
 import torch
 import torch.nn as nn
 from asr_datamodule import LibriSpeechAsrDataModule
-from train import add_model_arguments, get_params, get_model
+from train import add_model_arguments, get_model, get_params
 
 from icefall.checkpoint import (
     average_checkpoints,

egs/librispeech/ASR/zipformer/model.py

@@ -22,9 +22,9 @@ import k2
 import torch
 import torch.nn as nn
 from encoder_interface import EncoderInterface
+from scaling import ScaledLinear
 
 from icefall.utils import add_sos, make_pad_mask
-from scaling import ScaledLinear
 
 
 class AsrModel(nn.Module):

egs/librispeech/ASR/zipformer/my_profile.py

@@ -22,24 +22,24 @@ Usage: ./zipformer/my_profile.py
 
 import argparse
 import logging
+from typing import Tuple
+
 import sentencepiece as spm
 import torch
-
-from typing import Tuple
-from torch import Tensor, nn
-
-from icefall.utils import make_pad_mask
-from icefall.profiler import get_model_profile
 from scaling import BiasNorm
+from torch import Tensor, nn
 from train import (
+    add_model_arguments,
     get_encoder_embed,
     get_encoder_model,
     get_joiner_model,
-    add_model_arguments,
     get_params,
 )
 from zipformer import BypassModule
 
+from icefall.profiler import get_model_profile
+from icefall.utils import make_pad_mask
+
 
 def get_parser():
     parser = argparse.ArgumentParser(

egs/librispeech/ASR/zipformer/onnx_decode.py

@@ -77,11 +77,10 @@ from typing import List, Tuple
 import torch
 import torch.nn as nn
 from asr_datamodule import LibriSpeechAsrDataModule
-
-from onnx_pretrained import greedy_search, OnnxModel
+from k2 import SymbolTable
+from onnx_pretrained import OnnxModel, greedy_search
 
 from icefall.utils import setup_logger, store_transcripts, write_error_stats
-from k2 import SymbolTable
 
 
 def get_parser():

egs/librispeech/ASR/zipformer/onnx_pretrained_ctc_H.py

@@ -27,11 +27,10 @@ https://huggingface.co/csukuangfj/sherpa-onnx-zipformer-ctc-en-2023-10-02
 import argparse
 import logging
 import math
-from typing import List, Tuple
+from typing import Dict, List, Tuple
 
 import k2
 import kaldifeat
-from typing import Dict
 import kaldifst
 import onnxruntime as ort
 import torch

egs/librispeech/ASR/zipformer/onnx_pretrained_ctc_HL.py

@@ -27,11 +27,10 @@ https://huggingface.co/csukuangfj/sherpa-onnx-zipformer-ctc-en-2023-10-02
 import argparse
 import logging
 import math
-from typing import List, Tuple
+from typing import Dict, List, Tuple
 
 import k2
 import kaldifeat
-from typing import Dict
 import kaldifst
 import onnxruntime as ort
 import torch

egs/librispeech/ASR/zipformer/onnx_pretrained_ctc_HLG.py

@@ -27,11 +27,10 @@ https://huggingface.co/csukuangfj/sherpa-onnx-zipformer-ctc-en-2023-10-02
 import argparse
 import logging
 import math
-from typing import List, Tuple
+from typing import Dict, List, Tuple
 
 import k2
 import kaldifeat
-from typing import Dict
 import kaldifst
 import onnxruntime as ort
 import torch

egs/librispeech/ASR/zipformer/scaling.py

@@ -15,15 +15,16 @@
 # limitations under the License.
 
 
-from typing import Optional, Tuple, Union
 import logging
-import k2
-from torch.cuda.amp import custom_fwd, custom_bwd
-import random
-import torch
 import math
+import random
+from typing import Optional, Tuple, Union
+
+import k2
+import torch
 import torch.nn as nn
 from torch import Tensor
+from torch.cuda.amp import custom_bwd, custom_fwd
 
 
 def logaddexp_onnx(x: Tensor, y: Tensor) -> Tensor:

egs/librispeech/ASR/zipformer/streaming_decode.py

@@ -51,7 +51,7 @@ from streaming_beam_search import (
 )
 from torch import Tensor, nn
 from torch.nn.utils.rnn import pad_sequence
-from train import add_model_arguments, get_params, get_model
+from train import add_model_arguments, get_model, get_params
 
 from icefall.checkpoint import (
     average_checkpoints,

egs/librispeech/ASR/zipformer/subsampling.py

@@ -16,11 +16,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Tuple
 import warnings
+from typing import Tuple
 
 import torch
-from torch import Tensor, nn
 from scaling import (
     Balancer,
     BiasNorm,
@@ -34,6 +33,7 @@ from scaling import (
     SwooshR,
     Whiten,
 )
+from torch import Tensor, nn
 
 
 class ConvNeXt(nn.Module):

egs/librispeech/ASR/zipformer_adapter/decode.py

@@ -858,7 +858,9 @@ def main():
 
     logging.info("About to create model")
     model = get_model(params)
-    import pdb; pdb.set_trace()
+    import pdb
+
+    pdb.set_trace()
 
     if not params.use_averaged_model:
         if params.iter > 0:
@@ -877,9 +879,13 @@ def main():
                 )
             logging.info(f"averaging {filenames}")
             model.to(device)
-            model.load_state_dict(average_checkpoints(filenames, device=device), strict=False)
+            model.load_state_dict(
+                average_checkpoints(filenames, device=device), strict=False
+            )
         elif params.avg == 1:
-            load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model, strict=False)
+            load_checkpoint(
+                f"{params.exp_dir}/epoch-{params.epoch}.pt", model, strict=False
+            )
         else:
             start = params.epoch - params.avg + 1
             filenames = []
@@ -888,7 +894,9 @@ def main():
                     filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
             logging.info(f"averaging {filenames}")
             model.to(device)
-            model.load_state_dict(average_checkpoints(filenames, device=device), strict=False)
+            model.load_state_dict(
+                average_checkpoints(filenames, device=device), strict=False
+            )
     else:
         if params.iter > 0:
             filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
@@ -917,7 +925,7 @@ def main():
                     filename_end=filename_end,
                     device=device,
                 ),
-                strict=False
+                strict=False,
             )
         else:
             assert params.avg > 0, params.avg
@@ -936,7 +944,7 @@ def main():
                     filename_end=filename_end,
                     device=device,
                 ),
-                strict=False
+                strict=False,
             )
 
     model.to(device)

egs/librispeech/ASR/zipformer_adapter/decode_gigaspeech.py

@@ -121,7 +121,7 @@ from beam_search import (
     modified_beam_search_lm_shallow_fusion,
     modified_beam_search_LODR,
 )
-from train import add_model_arguments, add_finetune_arguments, get_model, get_params
+from train import add_finetune_arguments, add_model_arguments, get_model, get_params
 
 from icefall import ContextGraph, LmScorer, NgramLm
 from icefall.checkpoint import (

egs/librispeech/ASR/zipformer_adapter/export-onnx.py

@@ -72,7 +72,7 @@ import torch.nn as nn
 from decoder import Decoder
 from onnxruntime.quantization import QuantType, quantize_dynamic
 from scaling_converter import convert_scaled_to_non_scaled
-from train import add_model_arguments, add_finetune_arguments, get_model, get_params
+from train import add_finetune_arguments, add_model_arguments, get_model, get_params
 from zipformer import Zipformer2
 
 from icefall.checkpoint import (

egs/librispeech/ASR/zipformer_adapter/onnx_decode.py

@@ -77,11 +77,10 @@ from typing import List, Tuple
 import torch
 import torch.nn as nn
 from asr_datamodule import LibriSpeechAsrDataModule
-
-from onnx_pretrained import greedy_search, OnnxModel
+from k2 import SymbolTable
+from onnx_pretrained import OnnxModel, greedy_search
 
 from icefall.utils import setup_logger, store_transcripts, write_error_stats
-from k2 import SymbolTable
 
 conversational_filler = [
     "UH",
@@ -182,6 +181,7 @@ def get_parser():
 
     return parser
 
+
 def post_processing(
     results: List[Tuple[str, List[str], List[str]]],
 ) -> List[Tuple[str, List[str], List[str]]]:
@@ -192,6 +192,7 @@ def post_processing(
         new_results.append((key, new_ref, new_hyp))
     return new_results
 
+
 def decode_one_batch(
     model: OnnxModel, token_table: SymbolTable, batch: dict
 ) -> List[List[str]]:

egs/librispeech/ASR/zipformer_adapter/train.py

@@ -137,14 +137,14 @@ def add_finetune_arguments(parser: argparse.ArgumentParser):
         "--use-adapters",
         type=str2bool,
         default=True,
-        help="If use adapter to finetune the model"
+        help="If use adapter to finetune the model",
     )
 
     parser.add_argument(
         "--adapter-dim",
         type=int,
         default=16,
-        help="The bottleneck dimension of the adapter"
+        help="The bottleneck dimension of the adapter",
     )
 
     parser.add_argument(
@@ -1273,7 +1273,11 @@ def run(rank, world_size, args):
         else:
             p.requires_grad = False
 
-    logging.info("A total of {} trainable parameters ({:.3f}% of the whole model)".format(num_trainable, num_trainable/num_param * 100))
+    logging.info(
+        "A total of {} trainable parameters ({:.3f}% of the whole model)".format(
+            num_trainable, num_trainable / num_param * 100
+        )
+    )
 
     model.to(device)
     if world_size > 1:

egs/librispeech/ASR/zipformer_adapter/zipformer.py

@@ -40,13 +40,13 @@ from scaling import (
     Dropout2,
     FloatLike,
     ScheduledFloat,
+    SwooshL,
+    SwooshR,
     Whiten,
     convert_num_channels,
     limit_param_value,
     penalize_abs_values_gt,
     softmax,
-    SwooshL,
-    SwooshR,
 )
 from torch import Tensor, nn
 
@@ -601,8 +601,8 @@ class Zipformer2EncoderLayer(nn.Module):
         bypass_skip_rate: FloatLike = ScheduledFloat(
             (0.0, 0.5), (4000.0, 0.02), default=0
         ),
-        use_adapters: bool=False,
-        adapter_dim: int=16,
+        use_adapters: bool = False,
+        adapter_dim: int = 16,
     ) -> None:
         super(Zipformer2EncoderLayer, self).__init__()
         self.embed_dim = embed_dim
@@ -2488,8 +2488,8 @@ def _test_zipformer_main(causal: bool = False):
 class AdapterModule(nn.Module):
     def __init__(
         self,
-        embed_dim: int=384,
-        bottleneck_dim: int=16,
+        embed_dim: int = 384,
+        bottleneck_dim: int = 16,
     ):
         # The simplest adapter
         super(AdapterModule, self).__init__()

egs/must_c/ST/local/get_text.py

@@ -5,9 +5,9 @@ This file prints the text field of supervisions from cutset to the console
 """
 
 import argparse
+from pathlib import Path
 
 from lhotse import load_manifest_lazy
-from pathlib import Path
 
 
 def get_args():

egs/must_c/ST/local/get_words.py

@@ -5,7 +5,6 @@ This file generates words.txt from the given transcript file.
 """
 
 import argparse
-
 from pathlib import Path
 
 

egs/swbd/ASR/conformer_ctc/decode.py

@@ -29,7 +29,6 @@ import torch
 import torch.nn as nn
 from asr_datamodule import SwitchBoardAsrDataModule
 from conformer import Conformer
-
 from sclite_scoring import asr_text_post_processing
 
 from icefall.bpe_graph_compiler import BpeCtcTrainingGraphCompiler

egs/swbd/ASR/local/filter_empty_text.py

@@ -16,8 +16,8 @@
 # limitations under the License.
 
 import argparse
-from pathlib import Path
 import logging
+from pathlib import Path
 from typing import List
 
 

egs/tal_csasr/ASR/pruned_transducer_stateless7_bbpe/jit_pretrained.py

@@ -45,6 +45,7 @@ import sentencepiece as spm
 import torch
 import torchaudio
 from torch.nn.utils.rnn import pad_sequence
+
 from icefall import smart_byte_decode
 
 

egs/tedlium3/ASR/zipformer/model.py

@@ -19,9 +19,9 @@ import k2
 import torch
 import torch.nn as nn
 from encoder_interface import EncoderInterface
+from scaling import ScaledLinear
 
 from icefall.utils import add_sos, make_pad_mask
-from scaling import ScaledLinear
 
 
 class Transducer(nn.Module):

egs/wenetspeech/ASR/local/prepare_dataset_from_kaldi_dir.py

@@ -17,10 +17,10 @@
 
 import argparse
 import logging
-
-import torch
-import lhotse
 from pathlib import Path
+
+import lhotse
+import torch
 from lhotse import (
     CutSet,
     Fbank,
@@ -29,6 +29,7 @@ from lhotse import (
     fix_manifests,
     validate_recordings_and_supervisions,
 )
+
 from icefall.utils import get_executor, str2bool
 
 # Torch's multithreaded behavior needs to be disabled or

egs/wenetspeech/ASR/local/prepare_pinyin.py

@@ -41,6 +41,7 @@ from prepare_lang import (
     write_lexicon,
     write_mapping,
 )
+
 from icefall.utils import text_to_pinyin
 
 

egs/wenetspeech/ASR/pruned_transducer_stateless5/onnx_check.py

@@ -74,10 +74,10 @@ It will generate the following 3 files inside $repo/exp:
 import argparse
 import logging
 
-from icefall import is_module_available
+import torch
 from onnx_pretrained import OnnxModel
 
-import torch
+from icefall import is_module_available
 
 
 def get_parser():

egs/wenetspeech/KWS/zipformer/decode.py

@@ -30,9 +30,7 @@ import k2
 import torch
 import torch.nn as nn
 from asr_datamodule import WenetSpeechAsrDataModule
-from beam_search import (
-    keywords_search,
-)
+from beam_search import keywords_search
 from lhotse.cut import Cut
 from train import add_model_arguments, get_model, get_params
 

egs/wenetspeech/KWS/zipformer/finetune.py

@@ -87,6 +87,19 @@ from torch import Tensor
 from torch.cuda.amp import GradScaler
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.tensorboard import SummaryWriter
+from train import (
+    add_model_arguments,
+    add_training_arguments,
+    compute_validation_loss,
+    display_and_save_batch,
+    get_adjusted_batch_count,
+    get_model,
+    get_params,
+    load_checkpoint_if_available,
+    save_checkpoint,
+    scan_pessimistic_batches_for_oom,
+    set_batch_count,
+)
 
 from icefall import diagnostics
 from icefall.char_graph_compiler import CharCtcTrainingGraphCompiler
@@ -109,21 +122,6 @@ from icefall.utils import (
     text_to_pinyin,
 )
 
-from train import (
-    add_model_arguments,
-    add_training_arguments,
-    compute_validation_loss,
-    display_and_save_batch,
-    get_adjusted_batch_count,
-    get_model,
-    get_params,
-    load_checkpoint_if_available,
-    save_checkpoint,
-    scan_pessimistic_batches_for_oom,
-    set_batch_count,
-)
-
-
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
 
 

egs/wenetspeech/KWS/zipformer/train.py

@@ -99,7 +99,6 @@ from icefall.utils import (
     text_to_pinyin,
 )
 
-
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
 
 

egs/yesno/ASR/tdnn/jit_pretrained.py

@@ -18,9 +18,8 @@ you can use ./export.py --jit 1
 
 import argparse
 import logging
-from typing import List
 import math
-
+from typing import List
 
 import k2
 import kaldifeat

icefall/byte_utils.py

@@ -8,7 +8,6 @@
 import re
 import unicodedata
 
-
 WHITESPACE_NORMALIZER = re.compile(r"\s+")
 SPACE = chr(32)
 SPACE_ESCAPE = chr(9601)

icefall/ctc/prepare_lang.py

@@ -8,12 +8,12 @@ The lang_dir should contain the following files:
 """
 
 import math
+import re
 from collections import defaultdict
 from pathlib import Path
 from typing import List, Tuple
 
 import kaldifst
-import re
 
 
 class Lexicon:

icefall/diagnostics.py

@@ -18,7 +18,7 @@
 
 import random
 from dataclasses import dataclass
-from typing import Optional, Tuple, List
+from typing import List, Optional, Tuple
 
 import torch
 from torch import Tensor, nn

icefall/profiler.py

@@ -5,14 +5,15 @@
 
 # This is modified from https://github.com/microsoft/DeepSpeed/blob/master/deepspeed/profiling/flops_profiler/profiler.py
 
+from collections import OrderedDict
+from functools import partial
+from typing import List, Optional
+
 import k2
+import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from functools import partial
-from typing import List, Optional
-from collections import OrderedDict
-import numpy as np
 
 Tensor = torch.Tensor
 

icefall/rnn_lm/export-onnx.py

@@ -5,16 +5,16 @@
 import argparse
 import logging
 from pathlib import Path
+from typing import Dict
 
 import onnx
 import torch
 from model import RnnLmModel
 from onnxruntime.quantization import QuantType, quantize_dynamic
+from train import get_params
 
 from icefall.checkpoint import average_checkpoints, find_checkpoints, load_checkpoint
 from icefall.utils import AttributeDict, str2bool
-from typing import Dict
-from train import get_params
 
 
 def add_meta_data(filename: str, meta_data: Dict[str, str]):

icefall/utils.py

@@ -28,8 +28,6 @@ from contextlib import contextmanager
 from dataclasses import dataclass
 from datetime import datetime
 from pathlib import Path
-from pypinyin import pinyin, lazy_pinyin
-from pypinyin.contrib.tone_convert import to_initials, to_finals_tone, to_finals
 from shutil import copyfile
 from typing import Dict, Iterable, List, Optional, TextIO, Tuple, Union
 
@@ -40,6 +38,8 @@ import sentencepiece as spm
 import torch
 import torch.distributed as dist
 import torch.nn as nn
+from pypinyin import lazy_pinyin, pinyin
+from pypinyin.contrib.tone_convert import to_finals, to_finals_tone, to_initials
 from torch.utils.tensorboard import SummaryWriter
 
 from icefall.checkpoint import average_checkpoints