from local

egs/librispeech/ASR/incremental_transf/model.py

@@ -243,55 +243,7 @@ class Interformer(nn.Module):
         x: torch.Tensor,
         x_lens: torch.Tensor,
     ):
-        """
+        encoder_out, x_lens = self.pt_encoder(x, x_lens, warmup=warmup)
-        Args:
-          x:
-            A 3-D tensor of shape (N, T, C).
-          x_lens:
-            A 1-D tensor of shape (N,). It contains the number of frames in `x`
-            before padding.
-          y:
-            A ragged tensor with 2 axes [utt][label]. It contains labels of each
-            utterance.
-          prune_range:
-            The prune range for rnnt loss, it means how many symbols(context)
-            we are considering for each frame to compute the loss.
-          am_scale:
-            The scale to smooth the loss with am (output of encoder network)
-            part
-          lm_scale:
-            The scale to smooth the loss with lm (output of predictor network)
-            part
-          warmup:
-            A value warmup >= 0 that determines which modules are active, values
-            warmup > 1 "are fully warmed up" and all modules will be active.
-          reduction:
-            "sum" to sum the losses over all utterances in the batch.
-            "none" to return the loss in a 1-D tensor for each utterance
-            in the batch.
-          delay_penalty:
-            A constant value used to penalize symbol delay, 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.
-        Returns:
-        Returns:
-          Return the transducer loss.
-
-        Note:
-           Regarding am_scale & lm_scale, it will make the loss-function one of
-           the form:
-              lm_scale * lm_probs + am_scale * am_probs +
-              (1-lm_scale-am_scale) * combined_probs
-        """
-        assert reduction in ("sum", "none"), reduction
-        assert x.ndim == 3, x.shape
-        assert x_lens.ndim == 1, x_lens.shape
-        assert y.num_axes == 2, y.num_axes
-
-        assert x.size(0) == x_lens.size(0) == y.dim0
-
-        encoder_out, x_lens = self.encoder(x, x_lens, warmup=warmup)
         assert torch.all(x_lens > 0)
 
         # Now for the decoder, i.e., the prediction network