diff --git a/asteroid/masknn/convolutional.py b/asteroid/masknn/convolutional.py
index 333ce5383..1ec58df5d 100644
--- a/asteroid/masknn/convolutional.py
+++ b/asteroid/masknn/convolutional.py
@@ -486,7 +486,20 @@ class DCUMaskNet(BaseDCUMaskNet):
     Input shape is expected to be $(batch, nfreqs, time)$, with $nfreqs - 1$ divisible
     by $f_0 * f_1 * ... * f_N$ where $f_k$ are the frequency strides of the encoders,
     and $time - 1$ is divisible by $t_0 * t_1 * ... * t_N$ where $t_N$ are the time
-    strides of the encoders.
+    strides of the encoders. If `fix_length_mode` is not `None`, the time dimension
+    may is automatically padded or trimmed to a valid size before running it through
+    the network.
+
+    .. note::
+       If using `fix_length_mode="trim"`, the network's output will be all-zero at the
+       trimmed time-steps. You might want to ignore those time-steps in your loss function.
+
+       The time-domain length of the network's internal working size (the trimmed size)
+       can be retrieved using :meth:`~asteroid.models.BaseDCUNet.get_masker_working_size`::
+
+          >>> dcu16 = DCUNet("DCUNet-16", fix_length_mode="trim")
+          >>> dcu16.get_masker_working_size(3 * 16000)
+          45568
 
     References
         [1] : "Phase-aware Speech Enhancement with Deep Complex U-Net",
diff --git a/asteroid/models/base_models.py b/asteroid/models/base_models.py
index 35f4b11e9..30684c370 100644
--- a/asteroid/models/base_models.py
+++ b/asteroid/models/base_models.py
@@ -239,6 +239,20 @@ def forward(self, wav):
         reconstructed = pad_x_to_y(decoded, wav)
         return _shape_reconstructed(reconstructed, shape)
 
+    def get_masker_working_size(self, n_samples: int) -> int:
+        """Get the masker's internal working size for an input of `n_samples`.
+
+        Generally, if `fix_length_mode = "pad"`, the internal working size is
+        `>= n_samples`, otherwise it is `<= n_samples`.
+        """
+        x = torch.zeros(1, 1, n_samples)
+        tf_rep = self.forward_encoder(x)
+        tf_rep = self.masker.fix_input_dims(tf_rep)
+        masked = self.apply_masks(tf_rep, 1)
+        decoded = self.forward_decoder(masked)
+        return decoded.shape[-1]
+
+
     def forward_encoder(self, wav: torch.Tensor) -> torch.Tensor:
         """Computes time-frequency representation of `wav`.
 
diff --git a/asteroid/models/dcunet.py b/asteroid/models/dcunet.py
index a83759f82..965914f04 100644
--- a/asteroid/models/dcunet.py
+++ b/asteroid/models/dcunet.py
@@ -1,3 +1,4 @@
+import torch
 from asteroid_filterbanks import make_enc_dec
 from asteroid_filterbanks.transforms import from_torch_complex, to_torch_complex
 from ..masknn.convolutional import DCUMaskNet
diff --git a/tests/models/models_test.py b/tests/models/models_test.py
index 985b60890..0480da78e 100644
--- a/tests/models/models_test.py
+++ b/tests/models/models_test.py
@@ -175,6 +175,16 @@ def test_dcunet():
         DCUNet("mini").masker(torch.zeros((1, 9, 16), dtype=torch.complex64))
 
 
+def test_masker_working_size():
+    dcu_mini_trim = DCUNet("mini", fix_length_mode="trim")
+    dcu_mini_pad = DCUNet("mini", fix_length_mode="pad")
+    dccrn_mini = DCCRNet("mini")
+    inp_size = 3 * 16000
+    assert dcu_mini_trim.get_masker_working_size(inp_size) == 47616
+    assert dcu_mini_pad.get_masker_working_size(inp_size) == 49664
+    assert dccrn_mini.get_masker_working_size(inp_size) == 47872
+
+
 def test_dccrnet():
     _, istft = make_enc_dec("stft", 512, 512)
     input_samples = istft(torch.zeros((514, 16))).shape[0]