add ctc branch, with delay penalty

egs/librispeech/ASR/pruned_transducer_stateless4_ctc/model.py

@@ -69,6 +69,12 @@ class Transducer(nn.Module):
         self.simple_am_proj = ScaledLinear(encoder_dim, vocab_size, initial_speed=0.5)
         self.simple_lm_proj = ScaledLinear(decoder_dim, vocab_size)
 
+        self.ctc_output = nn.Sequential(
+            nn.Dropout(p=0.1),
+            ScaledLinear(encoder_dim, vocab_size, initial_speed=0.5),
+            nn.LogSoftmax(dim=-1),
+        )
+
     def forward(
         self,
         x: torch.Tensor,
@@ -80,7 +86,7 @@ class Transducer(nn.Module):
         warmup: float = 1.0,
         reduction: str = "sum",
         delay_penalty: float = 0.0,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
         """
         Args:
           x:
@@ -113,8 +119,7 @@ class Transducer(nn.Module):
             See https://github.com/k2-fsa/k2/issues/955 and
             https://arxiv.org/pdf/2211.00490.pdf for more details.
         Returns:
-        Returns:
-          Return the transducer loss.
+          Return a tuple containing simple loss, pruned loss, and ctc-output.
 
         Note:
            Regarding am_scale & lm_scale, it will make the loss-function one of
@@ -132,6 +137,9 @@ class Transducer(nn.Module):
         encoder_out, x_lens = self.encoder(x, x_lens, warmup=warmup)
         assert torch.all(x_lens > 0)
 
+        # compute ctc log-probs
+        ctc_output = self.ctc_output(encoder_out)
+
         # Now for the decoder, i.e., the prediction network
         row_splits = y.shape.row_splits(1)
         y_lens = row_splits[1:] - row_splits[:-1]
@@ -204,4 +212,4 @@ class Transducer(nn.Module):
                 reduction=reduction,
             )
 
-        return (simple_loss, pruned_loss)
+        return (simple_loss, pruned_loss, ctc_output)

egs/librispeech/ASR/pruned_transducer_stateless4_ctc/train.py

@@ -97,6 +97,7 @@ from icefall.utils import (
     AttributeDict,
     MetricsTracker,
     display_and_save_batch,
+    encode_supervisions,
     setup_logger,
     str2bool,
 )
@@ -273,6 +274,13 @@ def get_parser():
         "with this parameter before adding to the final loss.",
     )
 
+    parser.add_argument(
+        "--ctc-loss-scale",
+        type=float,
+        default=0.2,
+        help="Scale for CTC loss.",
+    )
+
     parser.add_argument(
         "--seed",
         type=int,
@@ -341,6 +349,16 @@ def get_parser():
         https://arxiv.org/pdf/2211.00490.pdf for more details.""",
     )
 
+    parser.add_argument(
+        "--ctc-delay-penalty",
+        type=float,
+        default=0.0,
+        help="""A constant value used to penalize symbol delay for CTC loss,
+        to encourage streaming models to emit symbols earlier.
+        See https://github.com/k2-fsa/k2/issues/955 and
+        https://arxiv.org/pdf/2211.00490.pdf for more details.""",
+    )
+
     add_model_arguments(parser)
 
     return parser
@@ -412,6 +430,9 @@ def get_params() -> AttributeDict:
             "decoder_dim": 512,
             # parameters for joiner
             "joiner_dim": 512,
+            # parameters for ctc loss
+            "beam_size": 10,
+            "use_double_scores": True,
             # parameters for Noam
             "model_warm_step": 3000,  # arg given to model, not for lrate
             "env_info": get_env_info(),
@@ -627,11 +648,11 @@ def compute_loss(
     feature_lens = supervisions["num_frames"].to(device)
 
     texts = batch["supervisions"]["text"]
-    y = sp.encode(texts, out_type=int)
-    y = k2.RaggedTensor(y).to(device)
+    token_ids = sp.encode(texts, out_type=int)
+    y = k2.RaggedTensor(token_ids).to(device)
 
     with torch.set_grad_enabled(is_training):
-        simple_loss, pruned_loss = model(
+        simple_loss, pruned_loss, ctc_output = model(
             x=feature,
             x_lens=feature_lens,
             y=y,
@@ -640,7 +661,7 @@ def compute_loss(
             lm_scale=params.lm_scale,
             warmup=warmup,
             reduction="none",
-            delay_penalty=params.delay_penalty if warmup >= 2.0 else 0,
+            delay_penalty=params.delay_penalty if warmup >= 1.0 else 0,
         )
         simple_loss_is_finite = torch.isfinite(simple_loss)
         pruned_loss_is_finite = torch.isfinite(pruned_loss)
@@ -674,6 +695,38 @@ def compute_loss(
         )
         loss = params.simple_loss_scale * simple_loss + pruned_loss_scale * pruned_loss
 
+    # Compute ctc loss
+
+    # NOTE: We need `encode_supervisions` to sort sequences with
+    # different duration in decreasing order, required by
+    # `k2.intersect_dense` called in `k2.ctc_loss`
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        supervision_segments, token_ids = encode_supervisions(
+            supervisions,
+            subsampling_factor=params.subsampling_factor,
+            token_ids=token_ids,
+        )
+
+    # Works with a BPE model
+    decoding_graph = k2.ctc_graph(token_ids, modified=False, device=device)
+    dense_fsa_vec = k2.DenseFsaVec(
+        ctc_output,
+        supervision_segments,
+        allow_truncate=params.subsampling_factor - 1,
+    )
+
+    ctc_loss = k2.ctc_loss(
+        decoding_graph=decoding_graph,
+        dense_fsa_vec=dense_fsa_vec,
+        output_beam=params.beam_size,
+        delay_penalty=params.ctc_delay_penalty if warmup >= 1.0 else 0.0,
+        reduction="sum",
+        use_double_scores=params.use_double_scores,
+    )
+    assert ctc_loss.requires_grad == is_training
+    loss += params.ctc_loss_scale * ctc_loss
+
     assert loss.requires_grad == is_training
 
     info = MetricsTracker()
@@ -698,6 +751,7 @@ def compute_loss(
     info["loss"] = loss.detach().cpu().item()
     info["simple_loss"] = simple_loss.detach().cpu().item()
     info["pruned_loss"] = pruned_loss.detach().cpu().item()
+    info["ctc_loss"] = ctc_loss.detach().cpu().item()
 
     return loss, info