Minor fixes

egs/librispeech/ASR/pruned_transducer_stateless2/conformer.py

@@ -15,6 +15,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import logging
 import copy
 import math
 import warnings
@@ -234,7 +235,7 @@ class Conformer(EncoderInterface):
 
         if not simulate_streaming:
             assert (
-                decode_states is not None
+                states is not None
             ), "Require cache when sending data in streaming mode"
 
             assert (
@@ -423,7 +424,7 @@ class ConformerEncoderLayer(nn.Module):
             # src: [chunk_size, N, F] e.g. [8, 41, 512]
             key = torch.cat([states[0, ...], src], dim=0)
             val = key
-            states[0, ...] = key[-left_context, ...]
+            states[0, ...] = key[-left_context:, ...]
         else:
             assert left_context == 0
 
@@ -441,14 +442,15 @@ class ConformerEncoderLayer(nn.Module):
         src = src + self.dropout(src_att)
 
         # convolution module
+        residual = src
         if not self.training and states is not None:
             src = torch.cat([states[1, ...], src], dim=0)
-            states[1, ...] = src[-left_context, ...]
+            states[1, ...] = src[-left_context:, ...]
 
         conv = self.conv_module(src)
-        conv = conv[-src.size(0) :, :, :]  # noqa: E203
+        conv = conv[-residual.size(0) :, :, :]  # noqa: E203
 
-        src = src + self.dropout(conv)
+        src = residual + self.dropout(conv)
 
         # feed forward module
         src = src + self.dropout(self.feed_forward(src))

egs/librispeech/ASR/pruned_transducer_stateless2/decode.py

@@ -70,6 +70,7 @@ Usage:
 
 import argparse
 import logging
+import math
 from collections import defaultdict
 from pathlib import Path
 from typing import Dict, List, Optional, Tuple
@@ -101,6 +102,7 @@ from icefall.utils import (
     write_error_stats,
 )
 
+LOG_EPS = math.log(1e-10)
 
 def get_parser():
     parser = argparse.ArgumentParser(
@@ -324,6 +326,13 @@ def decode_one_batch(
     supervisions = batch["supervisions"]
     feature_lens = supervisions["num_frames"].to(device)
 
+    feature_lens += params.left_context
+    feature = torch.nn.functional.pad(
+        feature,
+        pad=(0, 0, 0, params.left_context),
+        value=LOG_EPS,
+    )
+
     if params.simulate_streaming:
         encoder_out, encoder_out_lens, _ = model.encoder.streaming_forward(
             x=feature,