Reformat by black

egs/ksponspeech/ASR/local/compute_fbank_ksponspeech.py

@@ -63,7 +63,7 @@ def get_args():
     parser.add_argument(
         "--data-dir",
         type=str,
-        default='data',
+        default="data",
         help="""Path of data directory""",
     )
 
@@ -74,10 +74,10 @@ def compute_fbank_speechtools(
     bpe_model: Optional[str] = None,
     dataset: Optional[str] = None,
     perturb_speed: Optional[bool] = False,
-    data_dir: Optional[str] = 'data',
+    data_dir: Optional[str] = "data",
 ):
     src_dir = Path(data_dir) / "manifests"
-    output_dir = Path(data_dir ) / "fbank"
+    output_dir = Path(data_dir) / "fbank"
     num_jobs = min(4, os.cpu_count())
     num_mel_bins = 80
 
@@ -116,11 +116,11 @@ def compute_fbank_speechtools(
 
     if torch.cuda.is_available():
         # Use cuda for fbank compute
-        device = 'cuda'
+        device = "cuda"
     else:
-        device = 'cpu'
+        device = "cpu"
     logging.info(f"Device: {device}")
-    
+
     extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins, device=device))
 
     with get_executor() as ex:  # Initialize the executor only once.
@@ -135,9 +135,11 @@ def compute_fbank_speechtools(
                 recordings=m["recordings"],
                 supervisions=m["supervisions"],
             )
-            
+
             # Filter duration
-            cut_set = cut_set.filter(lambda x: x.duration > 1 and x.sampling_rate == 16000)
+            cut_set = cut_set.filter(
+                lambda x: x.duration > 1 and x.sampling_rate == 16000
+            )
 
             if "train" in partition:
                 if bpe_model:
@@ -150,7 +152,7 @@ def compute_fbank_speechtools(
                         + cut_set.perturb_speed(1.1)
                     )
             logging.info(f"Compute & Store features...")
-            if device == 'cuda':
+            if device == "cuda":
                 cut_set = cut_set.compute_and_store_features_batch(
                     extractor=extractor,
                     storage_path=f"{output_dir}/{prefix}_feats_{partition}",

egs/ksponspeech/ASR/local/compute_fbank_musan.py

@@ -53,8 +53,8 @@ def is_cut_long(c: MonoCut) -> bool:
 def compute_fbank_musan(
     src_dir: str = "data/manifests",
     num_mel_bins: int = 80,
-    whisper_fbank: bool = False, 
-    output_dir: str = "data/fbank"
+    whisper_fbank: bool = False,
+    output_dir: str = "data/fbank",
 ):
     src_dir = Path(src_dir)
     output_dir = Path(output_dir)

egs/ksponspeech/ASR/pruned_transducer_stateless7_streaming/asr_datamodule.py

@@ -399,14 +399,14 @@ class KsponSpeechAsrDataModule:
         return load_manifest_lazy(
             self.args.manifest_dir / "ksponspeech_cuts_dev.jsonl.gz"
         )
-        
+
     @lru_cache()
     def eval_clean_cuts(self) -> CutSet:
         logging.info("About to get eval_clean cuts")
         return load_manifest_lazy(
             self.args.manifest_dir / "ksponspeech_cuts_eval_clean.jsonl.gz"
         )
-        
+
     @lru_cache()
     def eval_other_cuts(self) -> CutSet:
         logging.info("About to get eval_other cuts")

egs/ksponspeech/ASR/pruned_transducer_stateless7_streaming/decode.py

@@ -693,7 +693,11 @@ def save_results(
         errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
         with open(errs_filename, "w") as f:
             cer = write_error_stats(
-                f, f"{test_set_name}-{key}", results, enable_log=True, compute_CER=True,
+                f,
+                f"{test_set_name}-{key}",
+                results,
+                enable_log=True,
+                compute_CER=True,
             )
             test_set_cers[key] = cer
 

egs/ksponspeech/ASR/pruned_transducer_stateless7_streaming/streaming_decode.py

@@ -321,7 +321,6 @@ def decode_dataset(
         num_mel_bins=80,
         high_freq=-400.0,
     )
-    
 
     log_interval = 50
 
@@ -426,7 +425,11 @@ def save_results(
         errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
         with open(errs_filename, "w") as f:
             cer = write_error_stats(
-                f, f"{test_set_name}-{key}", results, enable_log=True, compute_CER=True,
+                f,
+                f"{test_set_name}-{key}",
+                results,
+                enable_log=True,
+                compute_CER=True,
             )
             test_set_cers[key] = cer
 

egs/ksponspeech/ASR/pruned_transducer_stateless7_streaming/train.py

@@ -1031,7 +1031,7 @@ def run(rank, world_size, args):
     ksponspeech = KsponSpeechAsrDataModule(args)
 
     train_cuts = ksponspeech.train_cuts()
-    
+
     def remove_short_and_long_utt(c: Cut):
         # Keep only utterances with duration between 1 second and 20 seconds
         #
@@ -1083,7 +1083,7 @@ def run(rank, world_size, args):
     )
 
     valid_cuts = ksponspeech.dev_cuts()
-    
+
     # valid_cuts = valid_cuts.filter(remove_short_and_long_utt)
     valid_dl = ksponspeech.valid_dataloaders(valid_cuts)