Remove SOS from decoder.

egs/librispeech/ASR/transducer_lstm/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_lstm/decode.py

@@ -155,7 +155,6 @@ 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,
@@ -393,9 +392,8 @@ def main():
     sp = spm.SentencePieceProcessor()
     sp.load(params.bpe_model)
 
-    # <blk> and <sos/eos> are defined in local/train_bpe_model.py
+    # <blk> is 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_lstm/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_lstm/model.py

@@ -49,7 +49,7 @@ 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 attributes `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
@@ -58,7 +58,6 @@ 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
@@ -97,8 +96,7 @@ 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=sos_id)
+        sos_y = add_sos(y, sos_id=blank_id)
 
         sos_y_padded = sos_y.pad(mode="constant", padding_value=blank_id)
 

egs/librispeech/ASR/transducer_lstm/train.py

@@ -179,8 +179,6 @@ def get_params() -> AttributeDict:
 
         - num_decoder_layers: Number of decoder layer of transformer decoder.
 
-        - weight_decay:  The weight_decay for the optimizer.
-
         - warm_step: The warm_step for Noam optimizer.
     """
     params = AttributeDict(
@@ -206,7 +204,6 @@ def get_params() -> AttributeDict:
             "num_decoder_layers": 4,
             "decoder_hidden_dim": 512,
             # parameters for Noam
-            "weight_decay": 1e-6,
             "warm_step": 80000,  # For the 100h subset, use 8k
             "env_info": get_env_info(),
         }
@@ -232,7 +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,
@@ -568,9 +564,8 @@ def run(rank, world_size, args):
     sp = spm.SentencePieceProcessor()
     sp.load(params.bpe_model)
 
-    # <blk> and <sos/eos> are defined in local/train_bpe_model.py
+    # <blk> is 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)
@@ -594,7 +589,6 @@ def run(rank, world_size, args):
         model_size=params.encoder_hidden_size,
         factor=params.lr_factor,
         warm_step=params.warm_step,
-        weight_decay=params.weight_decay,
     )
 
     if checkpoints and "optimizer" in checkpoints: