Remove sos ID.

egs/librispeech/ASR/transducer/beam_search.py

@@ -111,7 +111,6 @@ def beam_search(
     # support only batch_size == 1 for now
     assert encoder_out.size(0) == 1, encoder_out.size(0)
     blank_id = model.decoder.blank_id
-    sos_id = model.decoder.sos_id
     device = model.device
 
     sos = torch.tensor([blank_id], device=device).reshape(1, 1)
@@ -192,7 +191,7 @@ def beam_search(
 
             # Second, choose other labels
             for i, v in enumerate(log_prob.tolist()):
-                if i in (blank_id, sos_id):
+                if i == blank_id:
                     continue
                 new_ys = y_star.ys + [i]
                 new_log_prob = y_star.log_prob + v

egs/librispeech/ASR/transducer/decode.py

@@ -159,7 +159,6 @@ def get_decoder_model(params: AttributeDict):
         vocab_size=params.vocab_size,
         embedding_dim=params.decoder_embedding_dim,
         blank_id=params.blank_id,
-        sos_id=params.sos_id,
         num_layers=params.num_decoder_layers,
         hidden_dim=params.decoder_hidden_dim,
         output_dim=params.encoder_out_dim,
@@ -399,7 +398,6 @@ def main():
 
     # <blk> and <sos/eos> are defined in local/train_bpe_model.py
     params.blank_id = sp.piece_to_id("<blk>")
-    params.sos_id = sp.piece_to_id("<sos/eos>")
     params.vocab_size = sp.get_piece_size()
 
     logging.info(params)

egs/librispeech/ASR/transducer/decoder.py

@@ -27,7 +27,6 @@ class Decoder(nn.Module):
         vocab_size: int,
         embedding_dim: int,
         blank_id: int,
-        sos_id: int,
         num_layers: int,
         hidden_dim: int,
         output_dim: int,
@@ -42,8 +41,6 @@ class Decoder(nn.Module):
             Dimension of the input embedding.
           blank_id:
             The ID of the blank symbol.
-          sos_id:
-            The ID of the SOS symbol.
           num_layers:
             Number of LSTM layers.
           hidden_dim:
@@ -71,7 +68,6 @@ class Decoder(nn.Module):
             dropout=rnn_dropout,
         )
         self.blank_id = blank_id
-        self.sos_id = sos_id
         self.output_linear = nn.Linear(hidden_dim, output_dim)
 
     def forward(

egs/librispeech/ASR/transducer/export.py

@@ -148,7 +148,6 @@ def get_decoder_model(params: AttributeDict):
         vocab_size=params.vocab_size,
         embedding_dim=params.decoder_embedding_dim,
         blank_id=params.blank_id,
-        sos_id=params.sos_id,
         num_layers=params.num_decoder_layers,
         hidden_dim=params.decoder_hidden_dim,
         output_dim=params.encoder_out_dim,
@@ -197,7 +196,6 @@ def main():
 
     # <blk> and <sos/eos> are defined in local/train_bpe_model.py
     params.blank_id = sp.piece_to_id("<blk>")
-    params.sos_id = sp.piece_to_id("<sos/eos>")
     params.vocab_size = sp.get_piece_size()
 
     logging.info(params)

egs/librispeech/ASR/transducer/model.py

@@ -54,7 +54,7 @@ class Transducer(nn.Module):
           decoder:
             It is the prediction network in the paper. Its input shape
             is (N, U) and its output shape is (N, U, C). It should contain
-            two attributes: `blank_id` and `sos_id`.
+            one attribute: `blank_id`.
           joiner:
             It has two inputs with shapes: (N, T, C) and (N, U, C). Its
             output shape is (N, T, U, C). Note that its output contains
@@ -63,7 +63,6 @@ class Transducer(nn.Module):
         super().__init__()
         assert isinstance(encoder, EncoderInterface)
         assert hasattr(decoder, "blank_id")
-        assert hasattr(decoder, "sos_id")
 
         self.encoder = encoder
         self.decoder = decoder
@@ -102,8 +101,7 @@ class Transducer(nn.Module):
         y_lens = row_splits[1:] - row_splits[:-1]
 
         blank_id = self.decoder.blank_id
-        sos_id = self.decoder.sos_id
+        sos_y = add_sos(y, sos_id=blank_id)
-        sos_y = add_sos(y, sos_id=sos_id)
 
         sos_y_padded = sos_y.pad(mode="constant", padding_value=blank_id)
 

egs/librispeech/ASR/transducer/pretrained.py

@@ -145,7 +145,6 @@ def get_decoder_model(params: AttributeDict):
         vocab_size=params.vocab_size,
         embedding_dim=params.decoder_embedding_dim,
         blank_id=params.blank_id,
-        sos_id=params.sos_id,
         num_layers=params.num_decoder_layers,
         hidden_dim=params.decoder_hidden_dim,
         output_dim=params.encoder_out_dim,
@@ -211,7 +210,6 @@ def main():
 
     # <blk> and <sos/eos> are defined in local/train_bpe_model.py
     params.blank_id = sp.piece_to_id("<blk>")
-    params.sos_id = sp.piece_to_id("<sos/eos>")
     params.vocab_size = sp.get_piece_size()
 
     logging.info(f"{params}")

egs/librispeech/ASR/transducer/test_conformer.py

@@ -36,7 +36,6 @@ def test_conformer():
         nhead=8,
         dim_feedforward=2048,
         num_encoder_layers=12,
-        use_feat_batchnorm=True,
     )
     N = 3
     T = 100

egs/librispeech/ASR/transducer/test_decoder.py

@@ -29,7 +29,6 @@ from decoder import Decoder
 def test_decoder():
     vocab_size = 3
     blank_id = 0
-    sos_id = 2
     embedding_dim = 128
     num_layers = 2
     hidden_dim = 6
@@ -41,7 +40,6 @@ def test_decoder():
         vocab_size=vocab_size,
         embedding_dim=embedding_dim,
         blank_id=blank_id,
-        sos_id=sos_id,
         num_layers=num_layers,
         hidden_dim=hidden_dim,
         output_dim=output_dim,

egs/librispeech/ASR/transducer/test_transducer.py

@@ -39,7 +39,6 @@ def test_transducer():
     # decoder params
     vocab_size = 3
     blank_id = 0
-    sos_id = 2
     embedding_dim = 128
     num_layers = 2
 
@@ -51,14 +50,12 @@ def test_transducer():
         nhead=8,
         dim_feedforward=2048,
         num_encoder_layers=12,
-        use_feat_batchnorm=True,
     )
 
     decoder = Decoder(
         vocab_size=vocab_size,
         embedding_dim=embedding_dim,
         blank_id=blank_id,
-        sos_id=sos_id,
         num_layers=num_layers,
         hidden_dim=output_dim,
         output_dim=output_dim,

egs/librispeech/ASR/transducer/test_transformer.py

@@ -36,7 +36,6 @@ def test_transformer():
         nhead=8,
         dim_feedforward=2048,
         num_encoder_layers=12,
-        use_feat_batchnorm=True,
     )
     N = 3
     T = 100

egs/librispeech/ASR/transducer/train.py

@@ -229,7 +229,6 @@ def get_decoder_model(params: AttributeDict):
         vocab_size=params.vocab_size,
         embedding_dim=params.decoder_embedding_dim,
         blank_id=params.blank_id,
-        sos_id=params.sos_id,
         num_layers=params.num_decoder_layers,
         hidden_dim=params.decoder_hidden_dim,
         output_dim=params.encoder_out_dim,
@@ -567,7 +566,6 @@ def run(rank, world_size, args):
 
     # <blk> and <sos/eos> are defined in local/train_bpe_model.py
     params.blank_id = sp.piece_to_id("<blk>")
-    params.sos_id = sp.piece_to_id("<sos/eos>")
     params.vocab_size = sp.get_piece_size()
 
     logging.info(params)