Apply isort and black reformatting

Signed-off-by: nithinraok <[email protected]>

nemo/collections/asr/models/hybrid_rnnt_ctc_models.py

@@ -141,7 +141,7 @@ def transcribe(
                 )
 
             decoding_cfg = self.cfg.aux_ctc.decoding if self.cur_decoder == "ctc" else self.cfg.decoding
-            
+
             logging.info(
                 "Timestamps requested, setting decoding timestamps to True. Capture them in Hypothesis object, with output[idx].timestep['word'/'segment'/'char']"
             )

tests/collections/asr/conftest.py

@@ -14,12 +14,13 @@
 
 from dataclasses import dataclass
 from typing import Optional, Type
-from nemo.collections.asr.models import ASRModel
 
 import numpy as np
 import pytest
 import torch
 
+from nemo.collections.asr.models import ASRModel
+
 
 class RNNTTestHelper:
     @staticmethod
@@ -355,14 +356,17 @@ def rnnt_test_helper() -> Type[RNNTTestHelper]:
 def rnn_loss_sample_data() -> Type[RnntLossSampleData]:
     return RnntLossSampleData
 
+
 @pytest.fixture(scope='session')
 def fast_conformer_transducer_model():
     return ASRModel.from_pretrained("stt_en_fastconformer_transducer_large")
 
+
 @pytest.fixture(scope='session')
 def fast_conformer_ctc_model():
     return ASRModel.from_pretrained("stt_en_fastconformer_ctc_large")
 
+
 @pytest.fixture(scope='session')
 def fast_conformer_hybrid_model():
-    return ASRModel.from_pretrained("parakeet-tdt_ctc-110m")
+    return ASRModel.from_pretrained("parakeet-tdt_ctc-110m")

tests/collections/asr/mixins/test_transcription.py

@@ -50,6 +50,7 @@ def audio_files(test_data_dir):
     Returns a list of audio files for testing.
     """
     import soundfile as sf
+
     audio_file1 = os.path.join(test_data_dir, "asr", "train", "an4", "wav", "an46-mmap-b.wav")
     audio_file2 = os.path.join(test_data_dir, "asr", "train", "an4", "wav", "an104-mrcb-b.wav")
 
@@ -310,6 +311,7 @@ class OverrideConfig(TranscribeConfig):
         assert outputs[0][2] == 3.0
 
     pytest.mark.with_downloads()
+
     @pytest.mark.unit
     def test_transcribe_return_hypothesis(self, test_data_dir, fast_conformer_ctc_model):
         audio_file = os.path.join(test_data_dir, "asr", "train", "an4", "wav", "an46-mmap-b.wav")
@@ -327,7 +329,7 @@ def test_transcribe_return_hypothesis(self, test_data_dir, fast_conformer_ctc_mo
     @pytest.mark.with_downloads()
     @pytest.mark.unit
     def test_transcribe_tensor(self, audio_files, fast_conformer_ctc_model):
-        
+
         audio, _ = audio_files
         # Numpy array test
         outputs = fast_conformer_ctc_model.transcribe(audio, batch_size=1)
@@ -353,7 +355,7 @@ def test_transcribe_multiple_tensor(self, audio_files, fast_conformer_ctc_model)
     def test_transcribe_dataloader(self, audio_files, fast_conformer_ctc_model):
 
         audio, audio2 = audio_files
-        
+
         dataset = DummyDataset([audio, audio2])
         collate_fn = lambda x: _speech_collate_fn(x, pad_id=0)
         dataloader = DataLoader(dataset, batch_size=2, shuffle=False, num_workers=0, collate_fn=collate_fn)
@@ -369,43 +371,39 @@ def test_transcribe_dataloader(self, audio_files, fast_conformer_ctc_model):
     def test_timestamps_with_transcribe(self, audio_files, fast_conformer_ctc_model):
         audio1, audio2 = audio_files
 
-        output = fast_conformer_ctc_model.transcribe([audio1,audio2], timestamps=True)
+        output = fast_conformer_ctc_model.transcribe([audio1, audio2], timestamps=True)
 
-        # check len of output 
-        assert len(output)==2
+        # check len of output
+        assert len(output) == 2
 
-        # check hypothesis object 
+        # check hypothesis object
         assert isinstance(output[0], Hypothesis)
-        # check transcript 
+        # check transcript
         assert output[0].text == 'stop'
         assert output[1].text == 'start'
 
         # check timestamp
         assert output[0].timestep['segment'][0]['start'] == pytest.approx(0.4)
         assert output[0].timestep['segment'][0]['end'] == pytest.approx(0.48)
 
-
     @pytest.mark.with_downloads()
     @pytest.mark.unit
     def test_timestamps_with_transcribe_hybrid(self, audio_files, fast_conformer_hybrid_model):
         audio1, audio2 = audio_files
 
-        output = fast_conformer_hybrid_model.transcribe([audio1,audio2], timestamps=True)
+        output = fast_conformer_hybrid_model.transcribe([audio1, audio2], timestamps=True)
 
-        # check len of output 
-        assert len(output)==2
+        # check len of output
+        assert len(output) == 2
 
-        output = output[1] # Transducer returns tuple
+        output = output[1]  # Transducer returns tuple
 
-        # check hypothesis object 
+        # check hypothesis object
         assert isinstance(output[0], Hypothesis)
-        # check transcript 
+        # check transcript
         assert output[0].text == 'Stop?'
         assert output[1].text == 'Start.'
 
         # check timestamp
         assert output[0].timestep['segment'][0]['start'] == pytest.approx(0.48)
         assert output[0].timestep['segment'][0]['end'] == pytest.approx(0.72)
-
-
-