diff --git a/egs/yesno/ASR/tdnn/train.py b/egs/yesno/ASR/tdnn/train.py
index 582f3e822..f8e8538ca 100644
--- a/egs/yesno/ASR/tdnn/train.py
+++ b/egs/yesno/ASR/tdnn/train.py
@@ -33,10 +33,7 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--world-size",
-        type=int,
-        default=1,
-        help="Number of GPUs for DDP training.",
+        "--world-size", type=int, default=1, help="Number of GPUs for DDP training.",
     )
 
     parser.add_argument(
@@ -54,10 +51,7 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--num-epochs",
-        type=int,
-        default=15,
-        help="Number of epochs to train.",
+        "--num-epochs", type=int, default=15, help="Number of epochs to train.",
     )
 
     parser.add_argument(
@@ -187,10 +181,7 @@ def load_checkpoint_if_available(
 
     filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt"
     saved_params = load_checkpoint(
-        filename,
-        model=model,
-        optimizer=optimizer,
-        scheduler=scheduler,
+        filename, model=model, optimizer=optimizer, scheduler=scheduler,
     )
 
     keys = [
@@ -287,16 +278,12 @@ def compute_loss(
 
     batch_size = nnet_output.shape[0]
     supervision_segments = torch.tensor(
-        [[i, 0, nnet_output.shape[1]] for i in range(batch_size)],
-        dtype=torch.int32,
+        [[i, 0, nnet_output.shape[1]] for i in range(batch_size)], dtype=torch.int32,
     )
 
     decoding_graph = graph_compiler.compile(texts)
 
-    dense_fsa_vec = k2.DenseFsaVec(
-        nnet_output,
-        supervision_segments,
-    )
+    dense_fsa_vec = k2.DenseFsaVec(nnet_output, supervision_segments,)
 
     loss = k2.ctc_loss(
         decoding_graph=decoding_graph,
@@ -309,8 +296,8 @@ def compute_loss(
     assert loss.requires_grad == is_training
 
     info = LossRecord()
-    info['frames'] = supervision_segments[:, 2].sum().item()
-    info['loss'] = loss.detach().cpu().item()
+    info["frames"] = supervision_segments[:, 2].sum().item()
+    info["loss"] = loss.detach().cpu().item()
 
     return loss, info
 
@@ -344,7 +331,7 @@ def compute_validation_loss(
     if world_size > 1:
         tot_loss.reduce(loss.device)
 
-    loss_value = tot_loss['loss'] / tot_loss['frames']
+    loss_value = tot_loss["loss"] / tot_loss["frames"]
 
     if loss_value < params.best_valid_loss:
         params.best_valid_epoch = params.cur_epoch
@@ -420,15 +407,9 @@ def train_one_epoch(
 
             if tb_writer is not None:
                 loss_info.write_summary(
-                    tb_writer,
-                    "train/current_",
-                    params.batch_idx_train
-                )
-                tot_loss.write_summary(
-                    tb_writer,
-                    "train/tot_",
-                    params.batch_idx_train
+                    tb_writer, "train/current_", params.batch_idx_train
                 )
+                tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train)
 
         if batch_idx > 0 and batch_idx % params.valid_interval == 0:
             valid_info = compute_validation_loss(
@@ -439,17 +420,13 @@ def train_one_epoch(
                 world_size=world_size,
             )
             model.train()
-            logging.info(
-                f"Epoch {params.cur_epoch}, validation {valid_info}"
-                )
+            logging.info(f"Epoch {params.cur_epoch}, validation {valid_info}")
             if tb_writer is not None:
                 valid_info.write_summary(
-                    tb_writer,
-                    "train/valid_",
-                    params.batch_idx_train,
+                    tb_writer, "train/valid_", params.batch_idx_train,
                 )
 
-    loss_value = tot_loss['loss'] / tot_loss['frames']
+    loss_value = tot_loss["loss"] / tot_loss["frames"]
     params.train_loss = loss_value
 
     if params.train_loss < params.best_train_loss:
@@ -506,9 +483,7 @@ def run(rank, world_size, args):
         model = DDP(model, device_ids=[rank])
 
     optimizer = optim.SGD(
-        model.parameters(),
-        lr=params.lr,
-        weight_decay=params.weight_decay,
+        model.parameters(), lr=params.lr, weight_decay=params.weight_decay,
     )
 
     if checkpoints:
@@ -542,11 +517,7 @@ def run(rank, world_size, args):
         )
 
         save_checkpoint(
-            params=params,
-            model=model,
-            optimizer=optimizer,
-            scheduler=None,
-            rank=rank,
+            params=params, model=model, optimizer=optimizer, scheduler=None, rank=rank,
         )
 
     logging.info("Done!")