format,test=doc

paddlespeech/s2t/io/sampler.py

@@ -51,7 +51,7 @@ def _batch_shuffle(indices, batch_size, epoch, clipped=False):
     """
     rng = np.random.RandomState(epoch)
     shift_len = rng.randint(0, batch_size - 1)
-    batch_indices = list(zip(*[iter(indices[shift_len:])] * batch_size))
+    batch_indices = list(zip(* [iter(indices[shift_len:])] * batch_size))
     rng.shuffle(batch_indices)
     batch_indices = [item for batch in batch_indices for item in batch]
     assert clipped is False

paddlespeech/s2t/models/u2_st/u2_st.py

@@ -33,8 +33,6 @@ from paddlespeech.s2t.modules.decoder import TransformerDecoder
 from paddlespeech.s2t.modules.encoder import ConformerEncoder
 from paddlespeech.s2t.modules.encoder import TransformerEncoder
 from paddlespeech.s2t.modules.loss import LabelSmoothingLoss
-from paddlespeech.s2t.modules.mask import mask_finished_preds
-from paddlespeech.s2t.modules.mask import mask_finished_scores
 from paddlespeech.s2t.modules.mask import subsequent_mask
 from paddlespeech.s2t.utils import checkpoint
 from paddlespeech.s2t.utils import layer_tools
@@ -291,7 +289,7 @@ class U2STBaseModel(nn.Layer):
         device = speech.place
 
         # Let's assume B = batch_size and N = beam_size
-        # 1. Encoder and init hypothesis 
+        # 1. Encoder and init hypothesis
         encoder_out, encoder_mask = self._forward_encoder(
             speech, speech_lengths, decoding_chunk_size,
             num_decoding_left_chunks,

paddlespeech/t2s/modules/transformer/repeat.py

@@ -36,4 +36,4 @@ def repeat(N, fn):
     Returns:
         MultiSequential: Repeated model instance.
     """
-    return MultiSequential(*[fn(n) for n in range(N)])
+    return MultiSequential(* [fn(n) for n in range(N)])

tests/unit/asr/deepspeech2_online_model_test.py

@@ -11,16 +11,17 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import os
+import pickle
 import unittest
 
 import numpy as np
 import paddle
-import pickle
-import os
 from paddle import inference
 
-from paddlespeech.s2t.models.ds2_online import DeepSpeech2ModelOnline
 from paddlespeech.s2t.models.ds2_online import DeepSpeech2InferModelOnline
+from paddlespeech.s2t.models.ds2_online import DeepSpeech2ModelOnline
+
 
 class TestDeepSpeech2ModelOnline(unittest.TestCase):
     def setUp(self):
@@ -185,15 +186,12 @@ class TestDeepSpeech2ModelOnline(unittest.TestCase):
                 paddle.allclose(final_state_c_box, final_state_c_box_chk), True)
 
 
-
-
 class TestDeepSpeech2StaticModelOnline(unittest.TestCase):
-    
     def setUp(self):
         export_prefix = "exp/deepspeech2_online/checkpoints/test_export"
         if not os.path.exists(os.path.dirname(export_prefix)):
             os.makedirs(os.path.dirname(export_prefix), mode=0o755)
-        infer_model =  DeepSpeech2InferModelOnline(
+        infer_model = DeepSpeech2InferModelOnline(
             feat_size=161,
             dict_size=4233,
             num_conv_layers=2,
@@ -207,27 +205,25 @@ class TestDeepSpeech2StaticModelOnline(unittest.TestCase):
 
         with open("test_data/static_ds2online_inputs.pickle", "rb") as f:
             self.data_dict = pickle.load(f)
-        
+
         self.setup_model(export_prefix)
-    
 
     def setup_model(self, export_prefix):
-        deepspeech_config = inference.Config(
+        deepspeech_config = inference.Config(export_prefix + ".pdmodel",
-            export_prefix + ".pdmodel",
+                                             export_prefix + ".pdiparams")
-            export_prefix + ".pdiparams")
+        if ('CUDA_VISIBLE_DEVICES' in os.environ.keys() and
-        if ('CUDA_VISIBLE_DEVICES' in os.environ.keys() and os.environ['CUDA_VISIBLE_DEVICES'].strip() != ''):
+                os.environ['CUDA_VISIBLE_DEVICES'].strip() != ''):
             deepspeech_config.enable_use_gpu(100, 0)
             deepspeech_config.enable_memory_optim()
         deepspeech_predictor = inference.create_predictor(deepspeech_config)
         self.predictor = deepspeech_predictor
-    
+
     def test_unit(self):
         input_names = self.predictor.get_input_names()
         audio_handle = self.predictor.get_input_handle(input_names[0])
         audio_len_handle = self.predictor.get_input_handle(input_names[1])
         h_box_handle = self.predictor.get_input_handle(input_names[2])
         c_box_handle = self.predictor.get_input_handle(input_names[3])
-        
 
         x_chunk = self.data_dict["audio_chunk"]
         x_chunk_lens = self.data_dict["audio_chunk_lens"]
@@ -246,13 +242,9 @@ class TestDeepSpeech2StaticModelOnline(unittest.TestCase):
         c_box_handle.reshape(chunk_state_c_box.shape)
         c_box_handle.copy_from_cpu(chunk_state_c_box)
 
-
-
         output_names = self.predictor.get_output_names()
-        output_handle = self.predictor.get_output_handle(
+        output_handle = self.predictor.get_output_handle(output_names[0])
-            output_names[0])
+        output_lens_handle = self.predictor.get_output_handle(output_names[1])
-        output_lens_handle = self.predictor.get_output_handle(
-            output_names[1])
         output_state_h_handle = self.predictor.get_output_handle(
             output_names[2])
         output_state_c_handle = self.predictor.get_output_handle(
@@ -264,7 +256,7 @@ class TestDeepSpeech2StaticModelOnline(unittest.TestCase):
         chunk_state_h_box = output_state_h_handle.copy_to_cpu()
         chunk_state_c_box = output_state_c_handle.copy_to_cpu()
         return True
-    
+
 
 if __name__ == '__main__':
     unittest.main()