Support modified transducer.

egs/librispeech/ASR/pruned_transducer_stateless_multi_datasets/model.py

@@ -15,6 +15,7 @@
 # limitations under the License.
 
 
+import random
 from typing import Optional
 
 import k2
@@ -119,6 +120,7 @@ class Transducer(nn.Module):
         x_lens: torch.Tensor,
         y: k2.RaggedTensor,
         libri: bool = True,
+        modified_transducer_prob: float = 0.0,
         prune_range: int = 5,
         am_scale: float = 0.0,
         lm_scale: float = 0.0,
@@ -136,6 +138,8 @@ class Transducer(nn.Module):
           libri:
             True to use the decoder and joiner for the LibriSpeech dataset.
             False to use the decoder and joiner for the GigaSpeech dataset.
+          modified_transducer_prob:
+            The probability to use modified transducer loss.
           prune_range:
             The prune range for rnnt loss, it means how many symbols(context)
             we are considering for each frame to compute the loss.
@@ -163,6 +167,16 @@ class Transducer(nn.Module):
         encoder_out, x_lens = self.encoder(x, x_lens)
         assert torch.all(x_lens > 0)
 
+        assert 0 <= modified_transducer_prob <= 1
+
+        if modified_transducer_prob == 0:
+            modified = False
+        elif random.random() < modified_transducer_prob:
+            # random.random() returns a float in the range [0, 1)
+            modified = True
+        else:
+            modified = False
+
         # Now for the decoder, i.e., the prediction network
         row_splits = y.shape.row_splits(1)
         y_lens = row_splits[1:] - row_splits[:-1]
@@ -213,6 +227,7 @@ class Transducer(nn.Module):
             lm_only_scale=lm_scale,
             am_only_scale=am_scale,
             boundary=boundary,
+            modified=modified,
             reduction="sum",
             return_grad=True,
         )
@@ -243,6 +258,7 @@ class Transducer(nn.Module):
             ranges=ranges,
             termination_symbol=blank_id,
             boundary=boundary,
+            modified=modified,
             reduction="sum",
         )
 

egs/librispeech/ASR/pruned_transducer_stateless_multi_datasets/train.py

@@ -180,6 +180,17 @@ def get_parser():
         "2 means tri-gram",
     )
 
+    parser.add_argument(
+        "--modified-transducer-prob",
+        type=float,
+        default=0.25,
+        help="""The probability to use modified transducer loss.
+        In modified transduer, it limits the maximum number of symbols
+        per frame to 1. See also the option --max-sym-per-frame in
+        pruned_transducer_stateless_multi_datasets/decode.py
+        """,
+    )
+
     parser.add_argument(
         "--prune-range",
         type=int,
@@ -498,6 +509,7 @@ def compute_loss(
             x_lens=feature_lens,
             y=y,
             libri=libri,
+            modified_transducer_prob=params.modified_transducer_prob,
             prune_range=params.prune_range,
             am_scale=params.am_scale,
             lm_scale=params.lm_scale,