Update model.py to use torchaudio's RNN-T loss.

egs/librispeech/ASR/transducer_stateless2/model.py

@@ -13,12 +13,18 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+"""
+Note we use `rnnt_loss` from torchaudio, which exists only in
+torchaudio >= v0.10.0. It also means you have to use torch >= v1.10.0
+"""
 
 import random
 
 import k2
 import torch
 import torch.nn as nn
+import torchaudio
+import torchaudio.functional
 from encoder_interface import EncoderInterface
 
 from icefall.utils import add_sos
@@ -102,42 +108,27 @@ class Transducer(nn.Module):
 
         decoder_out = self.decoder(sos_y_padded)
 
-        # +1 here since a blank is prepended to each utterance.
         logits = self.joiner(
             encoder_out=encoder_out,
             decoder_out=decoder_out,
-            encoder_out_len=x_lens,
-            decoder_out_len=y_lens + 1,
         )
 
         # rnnt_loss requires 0 padded targets
         # Note: y does not start with SOS
         y_padded = y.pad(mode="constant", padding_value=0)
 
-        # We don't put this `import` at the beginning of the file
-        # as it is required only in the training, not during the
-        # reference stage
-        import optimized_transducer
+        assert hasattr(torchaudio.functional, "rnnt_loss"), (
+            f"Current torchaudio version: {torchaudio.__version__}\n"
+            "Please install a version >= 0.10.0"
+        )
 
-        assert 0 <= modified_transducer_prob <= 1
-
-        if modified_transducer_prob == 0:
-            one_sym_per_frame = False
-        elif random.random() < modified_transducer_prob:
-            # random.random() returns a float in the range [0, 1)
-            one_sym_per_frame = True
-        else:
-            one_sym_per_frame = False
-
-        loss = optimized_transducer.transducer_loss(
+        loss = torchaudio.functional.rnnt_loss(
             logits=logits,
             targets=y_padded,
             logit_lengths=x_lens,
             target_lengths=y_lens,
             blank=blank_id,
             reduction="sum",
-            one_sym_per_frame=one_sym_per_frame,
-            from_log_softmax=False,
         )
 
         return loss