fix lint

egs/multi_zh-hans/ASR/whisper/decode.py

@@ -57,8 +57,8 @@ from lhotse.cut import Cut
 from multi_dataset import MultiDataset
 from tn.chinese.normalizer import Normalizer
 from whisper.normalizers import BasicTextNormalizer
-from whisper_encoder_forward_monkey_patch import replace_whisper_encoder_forward
 from whisper_decoder_forward_monkey_patch import replace_whisper_decoder_forward
+from whisper_encoder_forward_monkey_patch import replace_whisper_encoder_forward
 from zhconv import convert
 
 from icefall.checkpoint import average_checkpoints_with_averaged_model, load_checkpoint
@@ -297,6 +297,7 @@ def decode_one_batch(
     print(hyps)
     return {"beam-search": hyps}
 
+
 def decode_dataset(
     dl: torch.utils.data.DataLoader,
     params: AttributeDict,
@@ -314,6 +315,7 @@ def decode_dataset(
     Returns:
         Return a dict, whose key may be "beam-search".
     """
+
     def normalize_text_alimeeting(text: str, normalize: str = "m2met") -> str:
         """
         Text normalization similar to M2MeT challenge baseline.
@@ -323,6 +325,7 @@ def decode_dataset(
             return text
         elif normalize == "m2met":
             import re
+
             text = text.replace(" ", "")
             text = text.replace("<sil>", "")
             text = text.replace("<%>", "")
@@ -348,6 +351,7 @@ def decode_dataset(
             text = text.replace("、", "")
             text = text.replace("？", "")
             return text
+
     results = []
 
     num_cuts = 0

egs/multi_zh-hans/ASR/whisper/multi_dataset.py

@@ -244,4 +244,4 @@ class MultiDataset:
             # "kespeech-asr_dev_phase2": kespeech_dev_phase2_cuts,
             # "wenetspeech-net_test": wenetspeech_test_net_cuts,
             # "wenetspeech_dev": wenetspeech_dev_cuts,
-        }
+        }

egs/multi_zh-hans/ASR/whisper/train.py

@@ -65,8 +65,8 @@ from torch.cuda.amp import GradScaler
 from torch.nn.functional import pad as pad_tensor
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.tensorboard import SummaryWriter
-from whisper_encoder_forward_monkey_patch import replace_whisper_encoder_forward
 from whisper_decoder_forward_monkey_patch import replace_whisper_decoder_forward
+from whisper_encoder_forward_monkey_patch import replace_whisper_encoder_forward
 
 from icefall import diagnostics
 from icefall.checkpoint import load_checkpoint, remove_checkpoints
@@ -458,6 +458,7 @@ def compute_loss(
             return text
         elif normalize == "m2met":
             import re
+
             text = text.replace(" ", "")
             text = text.replace("<sil>", "")
             text = text.replace("<%>", "")

egs/multi_zh-hans/ASR/whisper/whisper_decoder_forward_monkey_patch.py

@@ -1,11 +1,12 @@
+from typing import Dict, Iterable, Optional
+
+import numpy as np
 import torch
 import torch.nn.functional as F
 import whisper
-from torch import Tensor
+from torch import Tensor, nn
-from torch import nn
+from whisper.model import LayerNorm, ResidualAttentionBlock
-from typing import Dict, Iterable, Optional
+
-from whisper.model import ResidualAttentionBlock, LayerNorm
-import numpy as np
 
 def forward(self, x: Tensor, xa: Tensor, kv_cache: Optional[dict] = None):
     """
@@ -19,10 +20,7 @@ def forward(self, x: Tensor, xa: Tensor, kv_cache: Optional[dict] = None):
         self.token_embedding(x)
         + self.positional_embedding[offset : offset + x.shape[-1]]
     )
-    x = (
+    x = x + self.positional_embedding[offset : offset + x.shape[1]]
-        x
-        + self.positional_embedding[offset : offset + x.shape[1]]
-    )
     x = x.to(xa.dtype)
 
     # for block in self.blocks:
@@ -39,6 +37,7 @@ def forward(self, x: Tensor, xa: Tensor, kv_cache: Optional[dict] = None):
 
     return logits
 
+
 def replace_whisper_decoder_forward():
     """
     This function monkey patches the forward method of the whisper encoder.

egs/speechio/ASR/RESULTS.md

@@ -20,7 +20,7 @@
 | 10 | **whisper-large-ft-v0**  | **6.34%** | 2023.03 |
 | 11 | baidu_pro_api_zh | 7.29% | 2023.12 |
 
-Note: Above API results are from [SPEECHIO](https://github.com/SpeechColab/Leaderboard). All results used the default [normalize method.](https://github.com/SpeechColab/Leaderboard/blob/master/utils/benchmark.sh#L67) 
+Note: Above API results are from [SPEECHIO](https://github.com/SpeechColab/Leaderboard). All results used the default [normalize method.](https://github.com/SpeechColab/Leaderboard/blob/master/utils/benchmark.sh#L67)
 
 <details><summary> Detail all models </summary><p>
 

egs/speechio/ASR/local/normalize_results.py

@@ -29,9 +29,10 @@ from pathlib import Path
 from typing import Dict, List, Optional, Tuple
 
 import kaldialign
+from speechio_norm import TextNorm
 
 from icefall.utils import store_transcripts, write_error_stats
-from speechio_norm import TextNorm
+
 
 def get_parser():
     parser = argparse.ArgumentParser(
@@ -140,6 +141,7 @@ def get_filenames(
         results.append(whisper_filename)
     return results
 
+
 def main():
     parser = get_parser()
     args = parser.parse_args()

egs/wenetspeech/ASR/local/fix_manifest.py

@@ -14,10 +14,12 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-import logging
 import argparse
+import logging
+
 from lhotse import CutSet, load_manifest_lazy
 
+
 def get_parser():
     parser = argparse.ArgumentParser(
         formatter_class=argparse.ArgumentDefaultsHelpFormatter
@@ -49,6 +51,7 @@ def get_parser():
 
     return parser
 
+
 def load_fixed_text(fixed_text_path):
     """
     fixed text format
@@ -57,32 +60,38 @@ def load_fixed_text(fixed_text_path):
     load into a dict
     """
     fixed_text_dict = {}
-    with open(fixed_text_path, 'r') as f:
+    with open(fixed_text_path, "r") as f:
         for line in f:
-            cut_id, text = line.strip().split(' ', 1)
+            cut_id, text = line.strip().split(" ", 1)
             fixed_text_dict[cut_id] = text
     return fixed_text_dict
 
+
 def fix_manifest(manifest, fixed_text_dict, fixed_manifest_path):
     with CutSet.open_writer(fixed_manifest_path) as manifest_writer:
         fixed_item = 0
         for i, cut in enumerate(manifest):
             if i % 10000 == 0:
-                logging.info(f'Processing cut {i}, fixed {fixed_item}')
+                logging.info(f"Processing cut {i}, fixed {fixed_item}")
             cut_id_orgin = cut.id
-            if cut_id_orgin.endswith('_sp0.9'):
+            if cut_id_orgin.endswith("_sp0.9"):
                 cut_id = cut_id_orgin[:-6]
-            elif cut_id_orgin.endswith('_sp1.1'):
+            elif cut_id_orgin.endswith("_sp1.1"):
                 cut_id = cut_id_orgin[:-6]
             else:
                 cut_id = cut_id_orgin
             if cut_id in fixed_text_dict:
-                assert len(cut.supervisions) == 1, f'cut {cut_id} has {len(cut.supervisions)} supervisions'
+                assert (
+                    len(cut.supervisions) == 1
+                ), f"cut {cut_id} has {len(cut.supervisions)} supervisions"
                 if cut.supervisions[0].text != fixed_text_dict[cut_id]:
-                    logging.info(f'Fixed text for cut {cut_id_orgin} from {cut.supervisions[0].text} to {fixed_text_dict[cut_id]}')
+                    logging.info(
+                        f"Fixed text for cut {cut_id_orgin} from {cut.supervisions[0].text} to {fixed_text_dict[cut_id]}"
+                    )
                     cut.supervisions[0].text = fixed_text_dict[cut_id]
                 fixed_item += 1
-                manifest_writer.write(cut)     
+                manifest_writer.write(cut)
+
 
 def main():
     formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
@@ -92,23 +101,26 @@ def main():
     args = parser.parse_args()
     logging.info(vars(args))
 
-    fixed_text_path = args.manifest_dir + 'text.fix'
+    fixed_text_path = args.manifest_dir + "text.fix"
     fixed_text_dict = load_fixed_text(fixed_text_path)
-    logging.info(f'Loaded {len(fixed_text_dict)} fixed texts')
+    logging.info(f"Loaded {len(fixed_text_dict)} fixed texts")
 
-    dev_manifest_path = args.manifest_dir + 'cuts_DEV.jsonl.gz'
+    dev_manifest_path = args.manifest_dir + "cuts_DEV.jsonl.gz"
-    fixed_dev_manifest_path = args.manifest_dir + 'cuts_DEV_fixed.jsonl.gz'
+    fixed_dev_manifest_path = args.manifest_dir + "cuts_DEV_fixed.jsonl.gz"
-    logging.info(f'Loading dev manifest from {dev_manifest_path}')
+    logging.info(f"Loading dev manifest from {dev_manifest_path}")
     cuts_dev_manifest = load_manifest_lazy(dev_manifest_path)
     fix_manifest(cuts_dev_manifest, fixed_text_dict, fixed_dev_manifest_path)
-    logging.info(f'Fixed dev manifest saved to {fixed_dev_manifest_path}')
+    logging.info(f"Fixed dev manifest saved to {fixed_dev_manifest_path}")
 
-    manifest_path = args.manifest_dir + f'cuts_{args.training_subset}.jsonl.gz'
+    manifest_path = args.manifest_dir + f"cuts_{args.training_subset}.jsonl.gz"
-    fixed_manifest_path = args.manifest_dir + f'cuts_{args.training_subset}_fixed.jsonl.gz'
+    fixed_manifest_path = (
-    logging.info(f'Loading manifest from {manifest_path}')
+        args.manifest_dir + f"cuts_{args.training_subset}_fixed.jsonl.gz"
+    )
+    logging.info(f"Loading manifest from {manifest_path}")
     cuts_manifest = load_manifest_lazy(manifest_path)
     fix_manifest(cuts_manifest, fixed_text_dict, fixed_manifest_path)
-    logging.info(f'Fixed training manifest saved to {fixed_manifest_path}')
+    logging.info(f"Fixed training manifest saved to {fixed_manifest_path}")
+
 
 if __name__ == "__main__":
-    main()
+    main()

egs/wenetspeech/ASR/prepare.sh

@@ -424,4 +424,4 @@ if [ $stage -le 23 ] && [ $stop_stage -ge 23 ]; then
   python local/fix_manifest.py \
     --fixed-transcript-path data/fbank/text.fix \
     --training-subset L
-fi
+fi

egs/wenetspeech/ASR/whisper/train.py

@@ -38,13 +38,13 @@ torchrun --nproc_per_node 8 ./whisper/train.py \
 import argparse
 import copy
 import logging
+import os
 import random
 import warnings
 from pathlib import Path
 from shutil import copyfile
 from typing import Any, Dict, List, Optional, Tuple, Union
 
-import os
 import deepspeed
 import k2
 import optim

egs/wenetspeech/KWS/run.sh

@@ -25,7 +25,7 @@ if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
     log "You need to run the prepare.sh first."
     exit -1
   fi
-  
+
   python ./zipformer/train.py \
       --world-size 4 \
       --exp-dir zipformer/exp \
@@ -105,11 +105,11 @@ if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
     --encoder-dim 128,128,128,128,128,128 \
     --encoder-unmasked-dim 128,128,128,128,128,128 \
     --causal 1
-fi 
+fi
 
 if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
   log "Stage 2: Finetune the model"
-  
+
   # The following configuration of lr schedule should work well
   # You may also tune the following parameters to adjust learning rate schedule
   base_lr=0.0005
@@ -201,4 +201,4 @@ if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
     --encoder-dim 128,128,128,128,128,128 \
     --encoder-unmasked-dim 128,128,128,128,128,128 \
     --causal 1
-fi 
+fi