fix for black

egs/libriheavy/ASR/zipformer_prompt_asr/decoder.py

@@ -17,9 +17,9 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-
 from scaling import Balancer
 
+
 class Decoder(nn.Module):
     """This class modifies the stateless decoder from the following paper:
 
@@ -61,10 +61,15 @@ class Decoder(nn.Module):
         )
         # the balancers are to avoid any drift in the magnitude of the
         # embeddings, which would interact badly with parameter averaging.
-        self.balancer = Balancer(decoder_dim, channel_dim=-1,
-                                 min_positive=0.0, max_positive=1.0,
-                                 min_abs=0.5, max_abs=1.0,
-                                 prob=0.05)
+        self.balancer = Balancer(
+            decoder_dim,
+            channel_dim=-1,
+            min_positive=0.0,
+            max_positive=1.0,
+            min_abs=0.5,
+            max_abs=1.0,
+            prob=0.05,
+        )
 
         self.blank_id = blank_id
 
@@ -78,14 +83,18 @@ class Decoder(nn.Module):
                 out_channels=decoder_dim,
                 kernel_size=context_size,
                 padding=0,
-                groups=decoder_dim//4,  # group size == 4
+                groups=decoder_dim // 4,  # group size == 4
                 bias=False,
             )
-            self.balancer2 = Balancer(decoder_dim, channel_dim=-1,
-                                      min_positive=0.0, max_positive=1.0,
-                                      min_abs=0.5, max_abs=1.0,
-                                      prob=0.05)
-
+            self.balancer2 = Balancer(
+                decoder_dim,
+                channel_dim=-1,
+                min_positive=0.0,
+                max_positive=1.0,
+                min_abs=0.5,
+                max_abs=1.0,
+                prob=0.05,
+            )
 
     def forward(self, y: torch.Tensor, need_pad: bool = True) -> torch.Tensor:
         """
@@ -108,9 +117,7 @@ class Decoder(nn.Module):
         if self.context_size > 1:
             embedding_out = embedding_out.permute(0, 2, 1)
             if need_pad is True:
-                embedding_out = F.pad(
-                    embedding_out, pad=(self.context_size - 1, 0)
-                )
+                embedding_out = F.pad(embedding_out, pad=(self.context_size - 1, 0))
             else:
                 # During inference time, there is no need to do extra padding
                 # as we only need one output

egs/libriheavy/ASR/zipformer_prompt_asr/joiner.py

@@ -16,9 +16,7 @@
 
 import torch
 import torch.nn as nn
-from scaling import (
-    ScaledLinear
-)
+from scaling import ScaledLinear
 
 
 class Joiner(nn.Module):
@@ -28,7 +26,7 @@ class Joiner(nn.Module):
         decoder_dim: int,
         joiner_dim: int,
         vocab_size: int,
-        context_dim: int=512,
+        context_dim: int = 512,
         context_injection: bool = False,
     ):
         super().__init__()
@@ -37,7 +35,9 @@ class Joiner(nn.Module):
         self.decoder_proj = ScaledLinear(decoder_dim, joiner_dim, initial_scale=0.25)
         self.output_linear = nn.Linear(joiner_dim, vocab_size)
         if context_injection:
-            self.context_proj = ScaledLinear(context_dim, joiner_dim, initial_scale=0.25)
+            self.context_proj = ScaledLinear(
+                context_dim, joiner_dim, initial_scale=0.25
+            )
         else:
             self.context_proj = None
 
@@ -68,7 +68,11 @@ class Joiner(nn.Module):
 
         if project_input:
             if context:
-                logit = self.encoder_proj(encoder_out) + self.decoder_proj(decoder_out) + self.context_proj(context)
+                logit = (
+                    self.encoder_proj(encoder_out)
+                    + self.decoder_proj(decoder_out)
+                    + self.context_proj(context)
+                )
             else:
                 logit = self.encoder_proj(encoder_out) + self.decoder_proj(decoder_out)
         else:

egs/libriheavy/ASR/zipformer_prompt_asr/ls_text_normalization.py

@@ -111,7 +111,7 @@ def decoding_normalization(text: str) -> str:
 
     # Only keep all alpha-numeric characters, hypen and apostrophe
     text = text.replace("-", " ")
-    text = re.sub("[^a-zA-Z0-9\s']+", "", text)
+    text = re.sub(r"[^a-zA-Z0-9\s']+", "", text)
     return text
 
 
@@ -130,9 +130,10 @@ def word_normalization(word: str) -> str:
     if word.isnumeric():
         word = num_to_words(int(word))
         return str(word).upper()
-    if word[-2:] == "TH" and word[0].isnumeric():  #  e.g 9TH, 6TH
+    #  e.g 9TH, 6TH
+    if word[-2:] == "TH" and word[0].isnumeric():
         return num_to_ordinal_word(int(word[:-2])).upper()
-    if word[0] == "\'":
+    if word[0] == "'":
         return word[1:]
 
     return word

egs/libriheavy/ASR/zipformer_prompt_asr/subsampling.py

@@ -15,11 +15,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Tuple
 import warnings
+from typing import Tuple
 
 import torch
-from torch import Tensor, nn
 from scaling import (
     Balancer,
     BiasNorm,
@@ -33,6 +32,7 @@ from scaling import (
     SwooshR,
     Whiten,
 )
+from torch import Tensor, nn
 
 
 class ConvNeXt(nn.Module):
@@ -106,9 +106,7 @@ class ConvNeXt(nn.Module):
         if layerdrop_rate != 0.0:
             batch_size = x.shape[0]
             mask = (
-                torch.rand(
-                    (batch_size, 1, 1, 1), dtype=x.dtype, device=x.device
-                )
+                torch.rand((batch_size, 1, 1, 1), dtype=x.dtype, device=x.device)
                 > layerdrop_rate
             )
         else:
@@ -227,9 +225,7 @@ class Conv2dSubsampling(nn.Module):
         # many copies of this extra gradient term.
         self.out_whiten = Whiten(
             num_groups=1,
-            whitening_limit=ScheduledFloat(
-                (0.0, 4.0), (20000.0, 8.0), default=4.0
-            ),
+            whitening_limit=ScheduledFloat((0.0, 4.0), (20000.0, 8.0), default=4.0),
             prob=(0.025, 0.25),
             grad_scale=0.02,
         )

egs/libriheavy/ASR/zipformer_prompt_asr/test_model.py

@@ -23,9 +23,9 @@ To run this file, do:
     python ./pruned_transducer_stateless4/test_model.py
 """
 
-from train_subformer import get_params, get_transducer_model, get_text_encoder
-from zipformer import Zipformer2
 from scaling import ScheduledFloat
+from train_subformer import get_params, get_text_encoder, get_transducer_model
+from zipformer import Zipformer2
 
 
 def test_model_1():
@@ -55,8 +55,8 @@ def test_model_M():
     params.encoder_unmasked_dims = "256,256,256,256,256"
     params.zipformer_downsampling_factors = "1,2,4,8,2"
     params.cnn_module_kernels = "31,31,15,15"
-    
-    params.text_encoder_dim = (192,192,256,384)
+
+    params.text_encoder_dim = (192, 192, 256, 384)
     params.decoder_dim = 512
     params.joiner_dim = 512
     model = Zipformer2(
@@ -83,12 +83,12 @@ def test_model_M():
     )
     num_param = sum([p.numel() for p in model.parameters()])
     print(f"Number of model parameters: {num_param}")
-    
+
     model = Zipformer2(
         output_downsampling_factor=8,
         downsampling_factor=(1, 2, 4, 8),
         num_encoder_layers=(2, 4, 6, 6),
-        encoder_dim=(256,256,384,512),
+        encoder_dim=(256, 256, 384, 512),
         encoder_unmasked_dim=(196, 196, 256, 256),
         query_head_dim=(32, 32, 32, 32),
         pos_head_dim=(4, 4, 4, 4),