Fix style

egs/librispeech/ASR/pruned_transducer_stateless/decode.py

@@ -143,7 +143,7 @@ def get_params() -> AttributeDict:
             "num_encoder_layers": 12,
             "vgg_frontend": False,
             # parameters for decoder
-            "embedding_dim" : 512,
+            "embedding_dim": 512,
             "env_info": get_env_info(),
         }
     )

egs/librispeech/ASR/pruned_transducer_stateless/export.py

@@ -28,7 +28,8 @@ Usage:
 
 It will generate a file exp_dir/pretrained.pt
 
-To use the generated file with `pruned_transducer_stateless/decode.py`, you can do:
+To use the generated file with `pruned_transducer_stateless/decode.py`,
+you can do:
 
     cd /path/to/exp_dir
     ln -s pretrained.pt epoch-9999.pt
@@ -48,6 +49,7 @@ from pathlib import Path
 
 import sentencepiece as spm
 import torch
+import torch.nn as nn
 from conformer import Conformer
 from decoder import Decoder
 from joiner import Joiner

egs/librispeech/ASR/pruned_transducer_stateless/model.py

@@ -136,11 +136,14 @@ class Transducer(nn.Module):
             lm_only_scale=self.lm_scale,
             am_only_scale=self.am_scale,
             boundary=boundary,
-            return_grad=True
+            return_grad=True,
         )
 
         ranges = k2.get_rnnt_prune_ranges(
-            px_grad=px_grad, py_grad=py_grad, boundary=boundary, s_range=self.prune_range
+            px_grad=px_grad,
+            py_grad=py_grad,
+            boundary=boundary,
+            s_range=self.prune_range,
         )
 
         am_pruned, lm_pruned = k2.do_rnnt_pruning(
@@ -150,7 +153,11 @@ class Transducer(nn.Module):
         logits = self.joiner(am_pruned, lm_pruned)
 
         pruned_loss = k2.rnnt_loss_pruned(
-            joint=logits, symbols=y_padded, ranges=ranges, termination_symbol=blank_id, boundary=boundary
+            joint=logits,
+            symbols=y_padded,
+            ranges=ranges,
+            termination_symbol=blank_id,
+            boundary=boundary,
         )
 
         return (-torch.sum(simple_loss), -torch.sum(pruned_loss))

egs/librispeech/ASR/pruned_transducer_stateless/pretrained.py

@@ -49,6 +49,7 @@ from typing import List
 import kaldifeat
 import sentencepiece as spm
 import torch
+import torch.nn as nn
 import torchaudio
 from beam_search import beam_search, greedy_search
 from conformer import Conformer
@@ -142,7 +143,7 @@ def get_params() -> AttributeDict:
             "num_encoder_layers": 12,
             "vgg_frontend": False,
             # parameters for decoder
-            "embedding_dim" : 512,
+            "embedding_dim": 512,
             "env_info": get_env_info(),
         }
     )

egs/librispeech/ASR/pruned_transducer_stateless/train.py

@@ -116,7 +116,7 @@ def get_parser():
     parser.add_argument(
         "--exp-dir",
         type=str,
-        default="transducer_stateless/exp",
+        default="pruned_transducer_stateless/exp",
         help="""The experiment dir.
         It specifies the directory where all training related
         files, e.g., checkpoints, log, etc, are saved
@@ -271,7 +271,7 @@ def get_decoder_model(params: AttributeDict) -> nn.Module:
     return decoder
 
 
-def get_joiner_model(params: AttributeDict) -> nn.Module :
+def get_joiner_model(params: AttributeDict) -> nn.Module:
     joiner = Joiner(
         input_dim=params.vocab_size,
         inner_dim=params.embedding_dim,
@@ -280,7 +280,7 @@ def get_joiner_model(params: AttributeDict) -> nn.Module :
     return joiner
 
 
-def get_transducer_model(params: AttributeDict) ->nn.Module:
+def get_transducer_model(params: AttributeDict) -> nn.Module:
     encoder = get_encoder_model(params)
     decoder = get_decoder_model(params)
     joiner = get_joiner_model(params)