diff --git a/src/wrappers/dilated.jl b/src/wrappers/dilated.jl
new file mode 100644
index 00000000..c7ca3a43
--- /dev/null
+++ b/src/wrappers/dilated.jl
@@ -0,0 +1,92 @@
+using Flux
+
+"""
+    Dilated(cell, inputs, rate)
+
+Dilated RNN layer with a single recurrent cell. 
+
+* `cell` is any Flux recurrent cell (e.g. `MGUCell`, `LSTMCell`, etc.).
+* `inputs` is a Vector of length `n_steps`, where each element is the data at one time step.
+  For instance, `inputs[t]` might be a `(input_dim, batch_size)` matrix.
+* `rate` is the dilation rate.
+
+"""
+using Flux
+
+struct Dilated{L,D,S,R}
+    layers::L
+    dropout::D
+    states::S
+    rates::R
+end
+
+@layer Dilated trainable=(layers)
+
+function Dilated(rlayer::Function, (input_size, hidden_size)::Pair{<:Int,<:Int};
+        num_layers::Int=1, rates::Union{Int,AbstractVector{<:Int}}=1, dropout::Real=0.0,
+        dims=:, active::Union{Bool,Nothing}=nothing, rng=Flux.default_rng(), kwargs...)
+    if rates isa Int
+        rates = fill(rates, num_layers)
+    end
+    layers = Vector{Any}(undef, num_layers)
+    for i in 1:num_layers
+        in_size = (i == 1) ? input_size : hidden_size
+        layers[i] = rlayer(in_size => hidden_size; kwargs...)
+    end
+    drop = Dropout(dropout; dims=dims, active=active, rng=rng)
+    states = [initialstates(layers[i]) for i in 1:num_layers]
+    Dilated(layers, drop, states, rates)
+end
+
+function _dilated_forward(cell::AbstractRecurrentCell, state, inputs::Vector{<:AbstractMatrix}, rate::Int)
+    n_steps = length(inputs)
+    even = (n_steps % rate) == 0
+    original_n_steps = n_steps
+    if !even
+        dilated_n_steps = div(n_steps, rate) + 1
+        pad_len = dilated_n_steps * rate - n_steps
+        zero_tensor = fill(0.0, size(inputs[1]))
+        for _ in 1:pad_len
+            push!(inputs, zero_tensor)
+        end
+    else
+        dilated_n_steps = div(n_steps, rate)
+    end
+    new_inputs = Vector{AbstractMatrix}(undef, dilated_n_steps)
+    idx = 1
+    for i in 1:dilated_n_steps
+        chunk = inputs[idx : idx + rate - 1]
+        idx += rate
+        new_inputs[i] = vcat(chunk...)
+    end
+    d_outputs = Vector{AbstractMatrix}(undef, dilated_n_steps)
+    hidden_state = state
+    for i in 1:dilated_n_steps
+        inp_t = new_inputs[i]'
+        out_t, hidden_state = cell(inp_t, hidden_state)
+        d_outputs[i] = out_t'
+    end
+    splitted = map(o -> [
+        o[(j-1)*size(inputs[1],1)+1 : j*size(inputs[1],1), :]
+        for j in 1:rate
+    ], d_outputs)
+    splitted_flat = reduce(vcat, splitted)
+    final_outputs = splitted_flat[1:original_n_steps]
+    return final_outputs, hidden_state
+end
+
+function (drnn::DilatedRNN)(inp::Vector{<:AbstractMatrix})
+    x = inp
+    for i in 1:length(drnn.layers)
+        cell   = drnn.layers[i]
+        state  = drnn.states[i]
+        rate   = drnn.rates[i]
+        out, new_state = _dilated_forward(cell, state, x, rate)
+        drnn.states[i] = new_state
+        if i < length(drnn.layers)
+            out = drnn.dropout(out)
+        end
+        x = out
+    end
+    x
+end