diff --git a/candle-core/src/backend.rs b/candle-core/src/backend.rs
index 67a08714..03a07434 100644
--- a/candle-core/src/backend.rs
+++ b/candle-core/src/backend.rs
@@ -57,6 +57,7 @@ pub trait BackendStorage: Sized {
 
     fn avg_pool2d(&self, _: &Layout, _: (usize, usize), _: (usize, usize)) -> Result<Self>;
     fn max_pool2d(&self, _: &Layout, _: (usize, usize), _: (usize, usize)) -> Result<Self>;
+    fn upsample_nearest1d(&self, _: &Layout, _: usize) -> Result<Self>;
     fn upsample_nearest2d(&self, _: &Layout, _: usize, _: usize) -> Result<Self>;
 
     fn gather(&self, _: &Layout, _: &Self, _: &Layout, _: usize) -> Result<Self>;
diff --git a/candle-core/src/backprop.rs b/candle-core/src/backprop.rs
index d2099df7..b930a9f4 100644
--- a/candle-core/src/backprop.rs
+++ b/candle-core/src/backprop.rs
@@ -91,6 +91,7 @@ impl Tensor {
                         }
                     }
                     Op::Reshape(node)
+                    | Op::UpsampleNearest1D(node)
                     | Op::UpsampleNearest2D(node)
                     | Op::AvgPool2D { arg: node, .. }
                     | Op::MaxPool2D { arg: node, .. }
@@ -262,6 +263,9 @@ impl Tensor {
                         let sum_grad = grads.or_insert(arg)?;
                         *sum_grad = sum_grad.add(&grad_arg)?;
                     }
+                    Op::UpsampleNearest1D { .. } => Err(Error::BackwardNotSupported {
+                        op: "upsample-nearest1d",
+                    })?,
                     Op::UpsampleNearest2D { .. } => Err(Error::BackwardNotSupported {
                         op: "upsample-nearest2d",
                     })?,
diff --git a/candle-core/src/cpu_backend.rs b/candle-core/src/cpu_backend.rs
index 544ce32d..4e808b34 100644
--- a/candle-core/src/cpu_backend.rs
+++ b/candle-core/src/cpu_backend.rs
@@ -727,6 +727,36 @@ impl Map1 for MaxPool2D {
     }
 }
 
+struct UpsampleNearest1D(usize);
+
+impl Map1 for UpsampleNearest1D {
+    fn f<T: WithDType>(&self, src: &[T], layout: &Layout) -> Result<Vec<T>> {
+        // TODO: Specialized implementation for the case 2*sz?
+        let dst_sz = self.0;
+        let (b_sz, c, src_sz) = layout.shape().dims3()?;
+        let stride = layout.stride();
+        let stride_sz = stride[2];
+        let src_index = layout.start_offset();
+        let scale_sz = src_sz as f64 / dst_sz as f64;
+        let mut dst = vec![T::zero(); b_sz * c * dst_sz];
+        let src_idxs = (0..dst_sz)
+            .map(|idx| usize::min(src_sz - 1, (idx as f64 * scale_sz) as usize))
+            .collect::<Vec<_>>();
+        for b_idx in 0..b_sz {
+            let dst = &mut dst[b_idx * c * dst_sz..];
+            let src_index = src_index + b_idx * stride[0];
+            for c_idx in 0..c {
+                let dst = &mut dst[c_idx * dst_sz..];
+                let src_index = src_index + c_idx * stride[1];
+                for (idx, src_idx) in src_idxs.iter().enumerate() {
+                    dst[idx] = src[src_index + src_idx * stride_sz]
+                }
+            }
+        }
+        Ok(dst)
+    }
+}
+
 struct UpsampleNearest2D(usize, usize);
 
 impl Map1 for UpsampleNearest2D {
@@ -2137,6 +2167,10 @@ impl BackendStorage for CpuStorage {
         MaxPool2D(kernel_size, stride).map(self, layout)
     }
 
+    fn upsample_nearest1d(&self, layout: &Layout, sz: usize) -> Result<Self> {
+        UpsampleNearest1D(sz).map(self, layout)
+    }
+
     fn upsample_nearest2d(&self, layout: &Layout, h: usize, w: usize) -> Result<Self> {
         UpsampleNearest2D(h, w).map(self, layout)
     }
diff --git a/candle-core/src/cuda_backend.rs b/candle-core/src/cuda_backend.rs
index 4ec41b87..00fd1d04 100644
--- a/candle-core/src/cuda_backend.rs
+++ b/candle-core/src/cuda_backend.rs
@@ -1954,6 +1954,10 @@ impl BackendStorage for CudaStorage {
         Ok(Self { slice, device })
     }
 
+    fn upsample_nearest1d(&self, _: &Layout, _out_sz: usize) -> Result<Self> {
+        crate::bail!("upsample-nearest1d is not supported on cuda")
+    }
+
     fn upsample_nearest2d(&self, l: &Layout, out_w: usize, out_h: usize) -> Result<Self> {
         let device = self.device().clone();
         let slice = UpsampleNearest2D(out_w, out_h).map(&self.slice, &device, l)?;
diff --git a/candle-core/src/dummy_cuda_backend.rs b/candle-core/src/dummy_cuda_backend.rs
index 6c896653..5cc9c6d8 100644
--- a/candle-core/src/dummy_cuda_backend.rs
+++ b/candle-core/src/dummy_cuda_backend.rs
@@ -152,6 +152,10 @@ impl crate::backend::BackendStorage for CudaStorage {
         Err(Error::NotCompiledWithCudaSupport)
     }
 
+    fn upsample_nearest1d(&self, _: &Layout, _: usize) -> Result<Self> {
+        Err(Error::NotCompiledWithCudaSupport)
+    }
+
     fn upsample_nearest2d(&self, _: &Layout, _: usize, _: usize) -> Result<Self> {
         Err(Error::NotCompiledWithCudaSupport)
     }
diff --git a/candle-core/src/op.rs b/candle-core/src/op.rs
index 9382b217..7940739c 100644
--- a/candle-core/src/op.rs
+++ b/candle-core/src/op.rs
@@ -116,6 +116,7 @@ pub enum Op {
         stride: (usize, usize),
     },
 
+    UpsampleNearest1D(Tensor),
     UpsampleNearest2D(Tensor),
 
     Cat(Vec<Tensor>, usize),
diff --git a/candle-core/src/storage.rs b/candle-core/src/storage.rs
index 8bd14ea9..9bd1fed6 100644
--- a/candle-core/src/storage.rs
+++ b/candle-core/src/storage.rs
@@ -369,6 +369,19 @@ impl Storage {
         }
     }
 
+    pub(crate) fn upsample_nearest1d(&self, layout: &Layout, sz: usize) -> Result<Self> {
+        match self {
+            Storage::Cpu(storage) => {
+                let storage = storage.upsample_nearest1d(layout, sz)?;
+                Ok(Self::Cpu(storage))
+            }
+            Self::Cuda(storage) => {
+                let storage = storage.upsample_nearest1d(layout, sz)?;
+                Ok(Self::Cuda(storage))
+            }
+        }
+    }
+
     pub(crate) fn upsample_nearest2d(&self, layout: &Layout, h: usize, w: usize) -> Result<Self> {
         match self {
             Storage::Cpu(storage) => {
diff --git a/candle-core/src/tensor.rs b/candle-core/src/tensor.rs
index 59a23c39..4388bf77 100644
--- a/candle-core/src/tensor.rs
+++ b/candle-core/src/tensor.rs
@@ -854,12 +854,30 @@ impl Tensor {
         self.maximum(min)?.minimum(max)
     }
 
-    /// Upsample the input tensor to the `(target_h, target_w)` size, taking the value of the
+    /// Interpolate the input tensor to the `target_size` size, taking the value of the nearest element.
+    ///
+    /// The input tensor should have three dimensions, `(batch, channels, l)`, the returned
+    /// tensor also has three dimensions, `(batch, channels, target_size)`.
+    pub fn interpolate1d(&self, target_size: usize) -> Result<Self> {
+        let (n, c, _l) = self.dims3()?;
+        let op = BackpropOp::new1(self, Op::UpsampleNearest1D);
+        let storage = self
+            .storage()
+            .upsample_nearest1d(self.layout(), target_size)?;
+        Ok(from_storage(storage, (n, c, target_size), op, false))
+    }
+
+    /// Alias for `interpolate1d`.
+    pub fn upsample_nearest1d(&self, target_size: usize) -> Result<Self> {
+        self.interpolate1d(target_size)
+    }
+
+    /// Interpolate the input tensor to the `(target_h, target_w)` size, taking the value of the
     /// nearest element.
     ///
     /// The input tensor should have four dimensions, `(batch, channels, h, w)`, the returned
     /// tensor also has four dimensions, `(batch, channels, target_h, target_w)`.
-    pub fn upsample_nearest2d(&self, target_h: usize, target_w: usize) -> Result<Self> {
+    pub fn interpolate2d(&self, target_h: usize, target_w: usize) -> Result<Self> {
         let (n, c, _h, _w) = self.dims4()?;
         let op = BackpropOp::new1(self, Op::UpsampleNearest2D);
         let storage = self
@@ -868,6 +886,11 @@ impl Tensor {
         Ok(from_storage(storage, (n, c, target_h, target_w), op, false))
     }
 
+    /// Alias for `interpolate2d`.
+    pub fn upsample_nearest2d(&self, target_h: usize, target_w: usize) -> Result<Self> {
+        self.interpolate2d(target_h, target_w)
+    }
+
     /// 2D average pooling over an input tensor with multiple channels.
     ///
     /// The input tensor should have four dimensions, `(batch, channels, h, w)`, the returned