Rename Jointer to Joiner.

.gitignore

@@ -6,3 +6,5 @@ exp
 exp*/
 *.pt
 download
+*.bak
+*-bak

egs/yesno/ASR/transducer/beam_search.py

@@ -49,7 +49,7 @@ def greedy_search(model: Transducer, encoder_out: torch.Tensor) -> List[int]:
         # fmt: off
         current_encoder_out = encoder_out[:, t:t+1, :]
         # fmt: on
-        logits = model.jointer(current_encoder_out, decoder_out)
+        logits = model.joiner(current_encoder_out, decoder_out)
 
         log_prob = logits.log_softmax(dim=-1)
         # log_prob is (N, 1, 1)

egs/yesno/ASR/transducer/decode.py

@@ -26,7 +26,7 @@ from asr_datamodule import YesNoAsrDataModule
 from transducer.beam_search import greedy_search
 from transducer.decoder import Decoder
 from transducer.encoder import Tdnn
-from transducer.jointer import Jointer
+from transducer.joiner import Joiner
 from transducer.model import Transducer
 
 from icefall.checkpoint import average_checkpoints, load_checkpoint
@@ -248,8 +248,8 @@ def get_transducer_model(params: AttributeDict):
         embedding_dropout=0.4,
         rnn_dropout=0.4,
     )
-    jointer = Jointer(input_dim=params.hidden_dim, output_dim=params.vocab_size)
-    transducer = Transducer(encoder=encoder, decoder=decoder, jointer=jointer)
+    joiner = Joiner(input_dim=params.hidden_dim, output_dim=params.vocab_size)
+    transducer = Transducer(encoder=encoder, decoder=decoder, joiner=joiner)
     return transducer
 
 

egs/yesno/ASR/transducer/joiner.py

@@ -19,7 +19,7 @@ import torch.nn as nn
 import torch.nn.functional as F
 
 
-class Jointer(nn.Module):
+class Joiner(nn.Module):
     def __init__(self, input_dim: int, output_dim: int):
         super().__init__()
 

egs/yesno/ASR/transducer/model.py

@@ -41,7 +41,7 @@ class Transducer(nn.Module):
         self,
         encoder: nn.Module,
         decoder: nn.Module,
-        jointer: nn.Module,
+        joiner: nn.Module,
     ):
         """
         Args:
@@ -54,7 +54,7 @@ class Transducer(nn.Module):
             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
             one attribute: `blank_id`.
-          jointer:
+          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
             unnormalized probs, i.e., not processed by log-softmax.
@@ -62,7 +62,7 @@ class Transducer(nn.Module):
         super().__init__()
         self.encoder = encoder
         self.decoder = decoder
-        self.jointer = jointer
+        self.joiner = joiner
 
     def forward(
         self,
@@ -103,7 +103,7 @@ class Transducer(nn.Module):
 
         decoder_out, _ = self.decoder(sos_y_padded)
 
-        logits = self.jointer(encoder_out, decoder_out)
+        logits = self.joiner(encoder_out, decoder_out)
 
         # rnnt_loss requires 0 padded targets
         y_padded = y.pad(mode="constant", padding_value=0)

egs/yesno/ASR/transducer/test_joiner.py

@@ -19,31 +19,31 @@
 To run this file, do:
 
     cd icefall/egs/yesno/ASR
-    python ./transducer/test_jointer.py
+    python ./transducer/test_joiner.py
 """
 
 
 import torch
-from transducer.jointer import Jointer
+from transducer.joiner import Joiner
 
 
-def test_jointer():
+def test_joiner():
     N = 2
     T = 3
     C = 4
     U = 5
 
-    jointer = Jointer(C, 10)
+    joiner = Joiner(C, 10)
 
     encoder_out = torch.rand(N, T, C)
     decoder_out = torch.rand(N, U, C)
 
-    joint = jointer(encoder_out, decoder_out)
+    joint = joiner(encoder_out, decoder_out)
     assert joint.shape == (N, T, U, 10)
 
 
 def main():
-    test_jointer()
+    test_joiner()
 
 
 if __name__ == "__main__":

egs/yesno/ASR/transducer/test_transducer.py

@@ -27,7 +27,7 @@ import k2
 import torch
 from transducer.decoder import Decoder
 from transducer.encoder import Tdnn
-from transducer.jointer import Jointer
+from transducer.joiner import Joiner
 from transducer.model import Transducer
 
 
@@ -54,8 +54,8 @@ def test_transducer():
         rnn_dropout=0.0,
     )
 
-    jointer = Jointer(output_dim, vocab_size)
-    transducer = Transducer(encoder=encoder, decoder=decoder, jointer=jointer)
+    joiner = Joiner(output_dim, vocab_size)
+    transducer = Transducer(encoder=encoder, decoder=decoder, joiner=joiner)
 
     y = k2.RaggedTensor([[1, 2, 1], [1, 1, 1, 2, 1]])
     N = y.dim0

egs/yesno/ASR/transducer/train.py

@@ -34,7 +34,7 @@ from torch.nn.utils import clip_grad_norm_
 from torch.utils.tensorboard import SummaryWriter
 from transducer.decoder import Decoder
 from transducer.encoder import Tdnn
-from transducer.jointer import Jointer
+from transducer.joiner import Joiner
 from transducer.model import Transducer
 
 from icefall.checkpoint import load_checkpoint
@@ -465,8 +465,8 @@ def get_transducer_model(params: AttributeDict):
         embedding_dropout=0.4,
         rnn_dropout=0.4,
     )
-    jointer = Jointer(input_dim=params.hidden_dim, output_dim=params.vocab_size)
-    transducer = Transducer(encoder=encoder, decoder=decoder, jointer=jointer)
+    joiner = Joiner(input_dim=params.hidden_dim, output_dim=params.vocab_size)
+    transducer = Transducer(encoder=encoder, decoder=decoder, joiner=joiner)
 
     return transducer