From c6b71fc222069bfeed609492755c29ca3e074bc6 Mon Sep 17 00:00:00 2001
From: dohe0342 <kimdohe1070@gmail.com>
Date: Mon, 9 Jan 2023 19:31:07 +0900
Subject: [PATCH] from local

---
 .../ASR/incremental_transf/.model.py.swp      | Bin 24576 -> 24576 bytes
 .../ASR/incremental_transf/model.py           |  26 ------------------
 2 files changed, 26 deletions(-)

diff --git a/egs/librispeech/ASR/incremental_transf/.model.py.swp b/egs/librispeech/ASR/incremental_transf/.model.py.swp
index b0d71b1df1e880f20d493652d01267b95ff3d208..508ef2ac884cdebb996ec171eb1bd9085236591e 100644
GIT binary patch
delta 508
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delta 396
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diff --git a/egs/librispeech/ASR/incremental_transf/model.py b/egs/librispeech/ASR/incremental_transf/model.py
index 7c0138405..cc5fac241 100644
--- a/egs/librispeech/ASR/incremental_transf/model.py
+++ b/egs/librispeech/ASR/incremental_transf/model.py
@@ -248,32 +248,6 @@ class Interformer(nn.Module):
                                                              warmup=warmup, 
                                                              get_layer_output=True
                                             )
-        assert torch.all(x_lens > 0)
-
-        # Now for the decoder, i.e., the prediction network
-        row_splits = y.shape.row_splits(1)
-        y_lens = row_splits[1:] - row_splits[:-1]
-
-        blank_id = self.decoder.blank_id
-        sos_y = add_sos(y, sos_id=blank_id)
-
-        # sos_y_padded: [B, S + 1], start with SOS.
-        sos_y_padded = sos_y.pad(mode="constant", padding_value=blank_id)
-
-        # decoder_out: [B, S + 1, decoder_dim]
-        decoder_out = self.decoder(sos_y_padded)
-
-        # Note: y does not start with SOS
-        # y_padded : [B, S]
-        y_padded = y.pad(mode="constant", padding_value=0)
-
-        y_padded = y_padded.to(torch.int64)
-        boundary = torch.zeros((x.size(0), 4), dtype=torch.int64, device=x.device)
-        boundary[:, 2] = y_lens
-        boundary[:, 3] = x_lens
-
-        lm = self.simple_lm_proj(decoder_out)
-        am = self.simple_am_proj(encoder_out)
 
         with torch.cuda.amp.autocast(enabled=False):
             simple_loss, (px_grad, py_grad) = k2.rnnt_loss_smoothed(