format

deepspeech/frontend/normalizer.py

@@ -179,7 +179,8 @@ class FeatureNormalizer(object):
                 wav_number += batch_size
 
                 if wav_number % 1000 == 0:
-                    logger.info(f'process {wav_number} wavs,{all_number} frames.')
+                    logger.info(
+                        f'process {wav_number} wavs,{all_number} frames.')
 
         self.cmvn_info = {
             'mean_stat': list(all_mean_stat.tolist()),

doc/src/benchmark.md

@@ -14,4 +14,3 @@ We compare the training time with 1, 2, 4, 8 Tesla V100 GPUs (with a subset of L
 | 8         | 6.95 X |
 
 `utils/profile.sh` provides such a demo profiling tool, you can change it as need.
-

doc/src/praat_textgrid.md

@@ -256,4 +256,3 @@ pip3 install textgrid
 
 * https://zh.wikipedia.org/wiki/Praat%E8%AF%AD%E9%9F%B3%E5%AD%A6%E8%BD%AF%E4%BB%B6
 * https://blog.csdn.net/duxin_csdn/article/details/88966295
-

doc/src/tools.md

@@ -1,4 +1,3 @@
 # Useful Tools
 
 * [正则可视化和常用正则表达式](https://wangwl.net/static/projects/visualRegex/#)
-

requirements.txt

@@ -1,5 +1,6 @@
 coverage
 pre-commit
+pybind11
 resampy==0.2.2
 scipy==1.2.1
 sentencepiece
@@ -7,7 +8,6 @@ snakeviz
 SoundFile==0.9.0.post1
 sox
 tensorboardX
+textgrid
 typeguard
 yacs
-pybind11
-textgrid