Reformatted streaming_decode.py with flake8

egs/reazonspeech/ASR/zipformer/streaming_decode.py

@@ -22,13 +22,14 @@ Usage:
 
 """
 
-import pdb
 import argparse
 import logging
 import math
+import os
+import pdb
+import subprocess as sp
 from pathlib import Path
 from typing import Dict, List, Optional, Tuple
-from tokenizer import Tokenizer
 
 import k2
 import numpy as np
@@ -42,6 +43,7 @@ from streaming_beam_search import (
     greedy_search,
     modified_beam_search,
 )
+from tokenizer import Tokenizer
 from torch import Tensor, nn
 from torch.nn.utils.rnn import pad_sequence
 from train import add_model_arguments, get_model, get_params
@@ -61,9 +63,6 @@ from icefall.utils import (
     write_error_stats,
 )
 
-import subprocess as sp
-import os
-
 LOG_EPS = math.log(1e-10)
 
 
@@ -528,7 +527,7 @@ def decode_dataset(
     cuts: CutSet,
     params: AttributeDict,
     model: nn.Module,
-    sp: Tokenizer,
+    tokenizer: Tokenizer,
     decoding_graph: Optional[k2.Fsa] = None,
 ) -> Dict[str, List[Tuple[List[str], List[str]]]]:
     """Decode dataset.
@@ -540,7 +539,7 @@ def decode_dataset(
         It is returned by :func:`get_params`.
       model:
         The neural model.
-      sp:
+      tokenizer:
         The BPE model.
       decoding_graph:
         The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
@@ -608,7 +607,7 @@ def decode_dataset(
                     (
                         decode_streams[i].id,
                         decode_streams[i].ground_truth.split(),
-                        sp.decode(decode_streams[i].decoding_result()).split(),
+                        tokenizer.decode(decode_streams[i].decoding_result()).split(),
                     )
                 )
                 del decode_streams[i]
@@ -633,14 +632,13 @@ def decode_dataset(
             print("No finished streams, breaking the loop")
             break
 
-    
         for i in sorted(finished_streams, reverse=True):
             try:
                 decode_results.append(
                     (
                         decode_streams[i].id,
                         decode_streams[i].ground_truth.split(),
-                        sp.decode(decode_streams[i].decoding_result()).split(),
+                        tokenizer.decode(decode_streams[i].decoding_result()).split(),
                     )
                 )
                 del decode_streams[i]
@@ -755,12 +753,12 @@ def main():
 
     logging.info(f"Device: {device}")
 
-    sp = Tokenizer.load(params.lang, params.lang_type)
+    sp_token = Tokenizer.load(params.lang, params.lang_type)
 
     # <blk> and <unk> is defined in local/train_bpe_model.py
-    params.blank_id = sp.piece_to_id("<blk>")
-    params.unk_id = sp.piece_to_id("<unk>")
-    params.vocab_size = sp.get_piece_size()
+    params.blank_id = sp_token.piece_to_id("<blk>")
+    params.unk_id = sp_token.piece_to_id("<unk>")
+    params.vocab_size = sp_token.get_piece_size()
 
     logging.info(params)
 
@@ -870,7 +868,7 @@ def main():
             cuts=test_cut,
             params=params,
             model=model,
-            sp=sp,
+            tokenizer=sp_token,
             decoding_graph=decoding_graph,
         )
         save_results(