Improve RNNT Loss docstrings (#1620)

Author: Caroline Chen

torchaudio/prototype/rnnt_loss.py

@@ -24,12 +24,13 @@ def rnnt_loss(
     dependencies.
 
     Args:
-        logits (Tensor): Tensor of dimension (batch, time, target, class) containing output from joiner
+        logits (Tensor): Tensor of dimension (batch, max seq length, max target length + 1, class)
+            containing output from joiner
         targets (Tensor): Tensor of dimension (batch, max target length) containing targets with zero padded
         logit_lengths (Tensor): Tensor of dimension (batch) containing lengths of each sequence from encoder
         target_lengths (Tensor): Tensor of dimension (batch) containing lengths of targets for each sequence
-        blank (int, opt): blank label (Default: ``-1``)
-        clamp (float): clamp for gradients (Default: ``-1``)
+        blank (int, optional): blank label (Default: ``-1``)
+        clamp (float, optional): clamp for gradients (Default: ``-1``)
         reduction (string, optional): Specifies the reduction to apply to the output:
             ``'none'`` | ``'mean'`` | ``'sum'``. (Default: ``'mean'``)
 
@@ -69,8 +70,8 @@ class RNNTLoss(torch.nn.Module):
     dependencies.
 
     Args:
-        blank (int, opt): blank label (Default: ``-1``)
-        clamp (float): clamp for gradients (Default: ``-1``)
+        blank (int, optional): blank label (Default: ``-1``)
+        clamp (float, optional): clamp for gradients (Default: ``-1``)
         reduction (string, optional): Specifies the reduction to apply to the output:
             ``'none'`` | ``'mean'`` | ``'sum'``. (Default: ``'mean'``)
     """
@@ -95,7 +96,8 @@ def forward(
     ):
         """
         Args:
-            logits (Tensor): Tensor of dimension (batch, time, target, class) containing output from joiner
+            logits (Tensor): Tensor of dimension (batch, max seq length, max target length + 1, class)
+                containing output from joiner
             targets (Tensor): Tensor of dimension (batch, max target length) containing targets with zero padded
             logit_lengths (Tensor): Tensor of dimension (batch) containing lengths of each sequence from encoder
             target_lengths (Tensor): Tensor of dimension (batch) containing lengths of targets for each sequence