Apply isort and black reformatting

Signed-off-by: anteju <anteju@users.noreply.github.com>
NVIDIA · May 21, 2024 · c29b35e · c29b35e
1 parent 0f8bcfc
commit c29b35e
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Showing 23 changed files with 247 additions and 218 deletions.
diff --git a/examples/audio_tasks/audio_to_audio_eval.py b/examples/audio_tasks/audio_to_audio_eval.py
@@ -107,8 +107,7 @@ class AudioEvaluationConfig(process_audio.ProcessConfig):
 
 
 def get_evaluation_dataloader(config):
-    """Prepare a dataloader for evaluation.
-    """
+    """Prepare a dataloader for evaluation."""
     if config.get("use_lhotse", False):
         return get_lhotse_dataloader_from_config(
             config, global_rank=0, world_size=1, dataset=LhotseAudioToTargetDataset()
@@ -128,8 +127,7 @@ def get_evaluation_dataloader(config):
 
 
 def get_metrics(cfg: AudioEvaluationConfig):
-    """Prepare a dictionary with metrics.
-    """
+    """Prepare a dictionary with metrics."""
     available_metrics = ['sdr', 'sisdr', 'stoi', 'estoi', 'pesq']
 
     metrics = dict()
@@ -203,9 +201,10 @@ def main(cfg: AudioEvaluationConfig):
 
         num_files = 0
 
-        with open(process_cfg.output_filename, 'r') as f_processed, open(
-            temporary_manifest_filepath, 'w', encoding='utf-8'
-        ) as f_tmp:
+        with (
+            open(process_cfg.output_filename, 'r') as f_processed,
+            open(temporary_manifest_filepath, 'w', encoding='utf-8') as f_tmp,
+        ):
             for line_processed in f_processed:
                 data_processed = json.loads(line_processed)
 

diff --git a/examples/audio_tasks/speech_enhancement.py b/examples/audio_tasks/speech_enhancement.py
@@ -43,17 +43,15 @@
 
 
 class ModelType(str, Enum):
-    """Enumeration with the available model types.
-    """
+    """Enumeration with the available model types."""
 
     MaskBased = 'mask_based'
     Predictive = 'predictive'
     ScoreBased = 'score_based'
 
 
 def get_model_class(model_type: ModelType):
-    """Get model class for a given model type.
-    """
+    """Get model class for a given model type."""
     if model_type == ModelType.MaskBased:
         return EncMaskDecAudioToAudioModel
     elif model_type == ModelType.Predictive:

diff --git a/nemo/collections/asr/data/audio_to_text.py b/nemo/collections/asr/data/audio_to_text.py
@@ -256,8 +256,7 @@ def cache_datastore_manifests(
     if num_datastore_manifests > 0:
         # Local utility function
         def cache_data(manifest_filepaths, cache_audio, num_workers, max_num_workers):
-            """Cache manifests and audio data from object store.
-            """
+            """Cache manifests and audio data from object store."""
             # Determine the number of workers to use
             if num_workers is None:
                 num_workers = os.cpu_count() - 1
@@ -421,8 +420,7 @@ class _AudioTextDataset(Dataset):
 
     @property
     def output_types(self) -> Optional[Dict[str, NeuralType]]:
-        """Returns definitions of module output ports.
-               """
+        """Returns definitions of module output ports."""
         return {
             'audio_signal': NeuralType(('B', 'T'), AudioSignal()),
             'a_sig_length': NeuralType(tuple('B'), LengthsType()),
@@ -546,8 +544,7 @@ class AudioToCharDataset(_AudioTextDataset):
 
     @property
     def output_types(self) -> Optional[Dict[str, NeuralType]]:
-        """Returns definitions of module output ports.
-               """
+        """Returns definitions of module output ports."""
         return {
             'audio_signal': NeuralType(('B', 'T'), AudioSignal()),
             'a_sig_length': NeuralType(tuple('B'), LengthsType()),
@@ -640,8 +637,7 @@ class AudioToBPEDataset(_AudioTextDataset):
 
     @property
     def output_types(self) -> Optional[Dict[str, NeuralType]]:
-        """Returns definitions of module output ports.
-               """
+        """Returns definitions of module output ports."""
         return {
             'audio_signal': NeuralType(('B', 'T'), AudioSignal()),
             'a_sig_length': NeuralType(tuple('B'), LengthsType()),
@@ -910,8 +906,7 @@ def __next__(self):
         return TarredAudioFilter(self.manifest_processor.collection)
 
     def _loop_offsets(self, iterator):
-        """This function is used to iterate through utterances with different offsets for each file.
-        """
+        """This function is used to iterate through utterances with different offsets for each file."""
 
         class TarredAudioLoopOffsets:
             def __init__(self, collection):
@@ -944,8 +939,7 @@ def _collate_fn(self, batch):
         return _speech_collate_fn(batch, self.pad_id)
 
     def _build_sample(self, tup):
-        """Builds the training sample by combining the data from the WebDataset with the manifest info.
-        """
+        """Builds the training sample by combining the data from the WebDataset with the manifest info."""
         audio_bytes, audio_filename, offset_id = tup
 
         # Grab manifest entry from self.manifest_preprocessor.collection
@@ -1316,7 +1310,9 @@ class BucketingDataset(IterableDataset):
     """
 
     def __init__(
-        self, dataset: IterableDataset, bucketing_batch_size: int,
+        self,
+        dataset: IterableDataset,
+        bucketing_batch_size: int,
     ):
         self.wrapped_dataset = dataset
         self.bucketing_batch_size = bucketing_batch_size