Remove padding in the model to make the results reproducible.

.github/workflows/run-yesno-recipe.yml

@@ -74,8 +74,5 @@ jobs:
           cd egs/yesno/ASR
           ./prepare.sh
           python3 ./tdnn/train.py
-          python3 ./tdnn/decode.py --avg 2
-          python3 ./tdnn/decode.py --avg 3
-          python3 ./tdnn/decode.py --avg 4
-          python3 ./tdnn/decode.py --avg 5
+          python3 ./tdnn/decode.py
           # TODO: Check that the WER is less than some value

egs/yesno/ASR/tdnn/decode.py

@@ -32,14 +32,14 @@ def get_parser():
     parser.add_argument(
         "--epoch",
         type=int,
-        default=9,
+        default=14,
         help="It specifies the checkpoint to use for decoding."
         "Note: Epoch counts from 0.",
     )
     parser.add_argument(
         "--avg",
         type=int,
-        default=4,
+        default=2,
         help="Number of checkpoints to average. Automatically select "
         "consecutive checkpoints before the checkpoint specified by "
         "'--epoch'. ",
@@ -104,16 +104,11 @@ def decode_one_batch(
     nnet_output = model(feature)
     # nnet_output is [N, T, C]
 
-    supervisions = batch["supervisions"]
-
-    supervision_segments = torch.stack(
-        (
-            supervisions["sequence_idx"],
-            supervisions["start_frame"],
-            supervisions["num_frames"],
-        ),
-        1,
-    ).to(torch.int32)
+    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,
+    )
 
     lattice = get_lattice(
         nnet_output=nnet_output,

egs/yesno/ASR/tdnn/model.py

@@ -23,7 +23,6 @@ class Tdnn(nn.Module):
                 in_channels=num_features,
                 out_channels=32,
                 kernel_size=3,
-                padding=1,
             ),
             nn.ReLU(inplace=True),
             nn.BatchNorm1d(num_features=32, affine=False),
@@ -31,7 +30,6 @@ class Tdnn(nn.Module):
                 in_channels=32,
                 out_channels=32,
                 kernel_size=5,
-                padding=4,
                 dilation=2,
             ),
             nn.ReLU(inplace=True),
@@ -40,7 +38,6 @@ class Tdnn(nn.Module):
                 in_channels=32,
                 out_channels=32,
                 kernel_size=5,
-                padding=8,
                 dilation=4,
             ),
             nn.ReLU(inplace=True),

egs/yesno/ASR/tdnn/train.py

@@ -24,12 +24,7 @@ from icefall.checkpoint import save_checkpoint as save_checkpoint_impl
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.graph_compiler import CtcTrainingGraphCompiler
 from icefall.lexicon import Lexicon
-from icefall.utils import (
-    AttributeDict,
-    encode_supervisions,
-    setup_logger,
-    str2bool,
-)
+from icefall.utils import AttributeDict, setup_logger, str2bool
 
 
 def get_parser():
@@ -61,7 +56,7 @@ def get_parser():
     parser.add_argument(
         "--num-epochs",
         type=int,
-        default=10,
+        default=15,
         help="Number of epochs to train.",
     )
 
@@ -129,7 +124,7 @@ def get_params() -> AttributeDict:
         {
             "exp_dir": Path("tdnn/exp"),
             "lang_dir": Path("data/lang_phone"),
-            "lr": 1e-1,
+            "lr": 1e-2,
             "feature_dim": 23,
             "weight_decay": 1e-6,
             "start_epoch": 0,
@@ -277,9 +272,14 @@ def compute_loss(
     # different duration in decreasing order, required by
     # `k2.intersect_dense` called in `k2.ctc_loss`
     supervisions = batch["supervisions"]
-    supervision_segments, texts = encode_supervisions(
-        supervisions, subsampling_factor=1
+    texts = supervisions["text"]
+
+    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,
     )
+
     decoding_graph = graph_compiler.compile(texts)
 
     dense_fsa_vec = k2.DenseFsaVec(