diff --git a/src/libtorchaudio/forced_align/compute.cpp b/src/libtorchaudio/forced_align/compute.cpp
index 40cbcc8dd7..b1e90603e5 100644
--- a/src/libtorchaudio/forced_align/compute.cpp
+++ b/src/libtorchaudio/forced_align/compute.cpp
@@ -1,19 +1,10 @@
-#include <libtorchaudio/forced_align/compute.h>
-#include <torch/script.h>
+#include <torch/csrc/stable/library.h>
 
-std::tuple<torch::Tensor, torch::Tensor> forced_align(
-    const torch::Tensor& logProbs,
-    const torch::Tensor& targets,
-    const torch::Tensor& inputLengths,
-    const torch::Tensor& targetLengths,
-    const int64_t blank) {
-  static auto op = torch::Dispatcher::singleton()
-                       .findSchemaOrThrow("torchaudio::forced_align", "")
-                       .typed<decltype(forced_align)>();
-  return op.call(logProbs, targets, inputLengths, targetLengths, blank);
-}
-
-TORCH_LIBRARY_FRAGMENT(torchaudio, m) {
+STABLE_TORCH_LIBRARY_FRAGMENT(torchaudio, m) {
   m.def(
-      "forced_align(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank) -> (Tensor, Tensor)");
+      "forced_align(Tensor log_probs,"
+      "Tensor targets,"
+      "Tensor input_lengths,"
+      "Tensor target_lengths,"
+      "int blank) -> (Tensor, Tensor)");
 }
diff --git a/src/libtorchaudio/forced_align/compute.h b/src/libtorchaudio/forced_align/compute.h
index aaae9b086c..6f70f09bee 100644
--- a/src/libtorchaudio/forced_align/compute.h
+++ b/src/libtorchaudio/forced_align/compute.h
@@ -1,10 +1 @@
 #pragma once
-
-#include <torch/script.h>
-
-std::tuple<torch::Tensor, torch::Tensor> forced_align(
-    const torch::Tensor& logProbs,
-    const torch::Tensor& targets,
-    const torch::Tensor& inputLengths,
-    const torch::Tensor& targetLengths,
-    const int64_t blank);
diff --git a/src/libtorchaudio/forced_align/cpu/compute.cpp b/src/libtorchaudio/forced_align/cpu/compute.cpp
index fc702dc3b7..5adb822f66 100644
--- a/src/libtorchaudio/forced_align/cpu/compute.cpp
+++ b/src/libtorchaudio/forced_align/cpu/compute.cpp
@@ -38,9 +38,9 @@ void forced_align_impl(
   for (int i = 0; i < T * S; i++) {
     backPtr_a[i] = -1;
   }
-  auto logProbs_a = torchaudio::stable::accessor<scalar_t, 3>(logProbs);
-  auto targets_a = torchaudio::stable::accessor<target_t, 2>(targets);
-  auto paths_a = torchaudio::stable::accessor<target_t, 2>(paths);
+  auto logProbs_a = torchaudio::accessor<scalar_t, 3>(logProbs);
+  auto targets_a = torchaudio::accessor<target_t, 2>(targets);
+  auto paths_a = torchaudio::accessor<target_t, 2>(paths);
   auto R = 0;
   for (auto i = 1; i < L; i++) {
     if (targets_a[batchIndex][i] == targets_a[batchIndex][i - 1]) {
@@ -147,10 +147,10 @@ template <typename scalar_t>
 const auto forced_align_int_impl = forced_align_impl<scalar_t, ScalarType::Int>;
 
 std::tuple<Tensor, Tensor> compute(
-    const Tensor& logProbs,
-    const Tensor& targets,
-    const Tensor& inputLengths,
-    const Tensor& targetLengths,
+    Tensor logProbs,
+    Tensor targets,
+    Tensor inputLengths,
+    Tensor targetLengths,
     const int64_t blank) {
   STD_TORCH_CHECK(logProbs.is_cpu(), "log_probs must be a CPU tensor");
   STD_TORCH_CHECK(targets.is_cpu(), "targets must be a CPU tensor");
@@ -224,24 +224,8 @@ std::tuple<Tensor, Tensor> compute(
   return std::make_tuple(paths, logProbs);
 }
 
-void boxed_forced_align_cpu(
-    StableIValue* stack,
-    uint64_t num_args,
-    uint64_t num_outputs) {
-  STD_TORCH_CHECK(num_args == 5, "num_args must be 5");
-  STD_TORCH_CHECK(num_outputs == 2, "num_outputs must be 2");
-  std::tuple<Tensor, Tensor> res = compute(
-      /*logProbs*/ torch::stable::detail::to<Tensor>(stack[0]),
-      /*targets*/ torch::stable::detail::to<Tensor>(stack[1]),
-      /*logit_lengths*/ torch::stable::detail::to<Tensor>(stack[2]),
-      /*target_lengths*/ torch::stable::detail::to<Tensor>(stack[3]),
-      /*blank*/ float(torch::stable::detail::to<int64_t>(stack[4])));
-  stack[0] = torch::stable::detail::from(std::get<0>(res));
-  stack[1] = torch::stable::detail::from(std::get<1>(res));
-}
-
 STABLE_TORCH_LIBRARY_IMPL(torchaudio, CPU, m) {
-  m.impl("forced_align", &boxed_forced_align_cpu);
+  m.impl("forced_align", TORCH_BOX(&compute));
 }
 
 } // namespace cpu
diff --git a/src/libtorchaudio/forced_align/gpu/compute.cu b/src/libtorchaudio/forced_align/gpu/compute.cu
index e7430b93df..41f86914a6 100644
--- a/src/libtorchaudio/forced_align/gpu/compute.cu
+++ b/src/libtorchaudio/forced_align/gpu/compute.cu
@@ -1,5 +1,4 @@
 #include <libtorchaudio/utils.h>
-#include <libtorchaudio/stable/TensorAccessor.h>
 #include <torch/csrc/stable/library.h>
 #include <torch/headeronly/core/Dispatch_v2.h>
 #include <torch/headeronly/core/ScalarType.h>
@@ -23,9 +22,9 @@ using torch::headeronly::ScalarType;
 
 template <typename scalar_t, typename target_t>
 __global__ void falign_cuda_step_kernel(
-    const torchaudio::stable::PackedTensorAccessor32<scalar_t, 3, torchaudio::stable::RestrictPtrTraits>
+    const torchaudio::PackedTensorAccessor32<scalar_t, 3>
         logProbs_a,
-    const torchaudio::stable::PackedTensorAccessor32<target_t, 2, torchaudio::stable::RestrictPtrTraits>
+    const torchaudio::PackedTensorAccessor32<target_t, 2>
         targets_a,
     const int T,
     const int L,
@@ -36,9 +35,9 @@ __global__ void falign_cuda_step_kernel(
     int start,
     int end,
     int backPtrBufferLen,
-    torchaudio::stable::PackedTensorAccessor32<scalar_t, 2, torchaudio::stable::RestrictPtrTraits>
+    torchaudio::PackedTensorAccessor32<scalar_t, 2>
         alphas_a,
-    torchaudio::stable::PackedTensorAccessor32<int8_t, 2, torchaudio::stable::RestrictPtrTraits>
+    torchaudio::PackedTensorAccessor32<int8_t, 2>
         backPtrBuffer_a) {
   scalar_t kNegInfinity = -std::numeric_limits<scalar_t>::infinity();
   const int batchIndex =
@@ -125,7 +124,7 @@ void forced_align_impl(
   const scalar_t kNegInfinity = -std::numeric_limits<scalar_t>::infinity();
   using target_t = typename std::
       conditional<target_scalar_type == ScalarType::Int, int, int64_t>::type;
-  auto paths_a = torchaudio::stable::accessor<target_t, 2>(paths);
+  auto paths_a = torchaudio::accessor<target_t, 2>(paths);
   const int batchIndex =
       0; // TODO: support batch version and use the real batch index
   const int T = logProbs.size(1); // num frames
@@ -150,8 +149,8 @@ void forced_align_impl(
   torch::stable::fill_(alphas, kNegInfinity);
 
   // CPU accessors
-  auto targetsCpu_a = torchaudio::stable::accessor<target_t, 2>(targetsCpu);
-  auto backPtrCpu_a = torchaudio::stable::accessor<int8_t, 2>(backPtrCpu);
+  auto targetsCpu_a = torchaudio::accessor<target_t, 2>(targetsCpu);
+  auto backPtrCpu_a = torchaudio::accessor<int8_t, 2>(backPtrCpu);
   // count the number of repeats in label
   int R = 0;
   for (int i = 1; i < L; ++i) {
@@ -192,8 +191,8 @@ void forced_align_impl(
     }
     falign_cuda_step_kernel<scalar_t, target_t>
         <<<1, kNumThreads, 0, defaultStream>>>(
-            torchaudio::stable::packed_accessor32<scalar_t, 3, torchaudio::stable::RestrictPtrTraits>(logProbs),
-            torchaudio::stable::packed_accessor32<target_t, 2, torchaudio::stable::RestrictPtrTraits>(targets),
+            torchaudio::packed_accessor32<scalar_t, 3>(logProbs),
+            torchaudio::packed_accessor32<target_t, 2>(targets),
             T,
             L,
             N,
@@ -203,8 +202,8 @@ void forced_align_impl(
             start,
             end,
             backPtrBufferLen,
-            torchaudio::stable::packed_accessor32<scalar_t, 2, torchaudio::stable::RestrictPtrTraits>(alphas),
-            torchaudio::stable::packed_accessor32<int8_t, 2, torchaudio::stable::RestrictPtrTraits>(backPtrBuffer));
+            torchaudio::packed_accessor32<scalar_t, 2>(alphas),
+            torchaudio::packed_accessor32<int8_t, 2>(backPtrBuffer));
     C10_CUDA_KERNEL_LAUNCH_CHECK();
     ++backPtrBufferLen;
     if (backPtrBufferLen == kBackPtrBufferSize || t == T - 1) {
@@ -228,9 +227,8 @@ void forced_align_impl(
     }
   }
   cpuDataTranferStream.synchronize();
-
   auto alphasCpu = torchaudio::stable::cpu(alphas);
-  auto alphasCpu_a = torchaudio::stable::accessor<scalar_t, 2>(alphasCpu);
+  auto alphasCpu_a = torchaudio::accessor<scalar_t, 2>(alphasCpu);
   int curIdxOffset = ((T - 1) % 2);
   int ltrIdx =
       alphasCpu_a[curIdxOffset][S - 1] > alphasCpu_a[curIdxOffset][S - 2]
@@ -244,18 +242,11 @@ void forced_align_impl(
   }
 }
 
-template <typename scalar_t>
-const auto forced_align_long_impl =
-    forced_align_impl<scalar_t, ScalarType::Long>;
-
-template <typename scalar_t>
-const auto forced_align_int_impl = forced_align_impl<scalar_t, ScalarType::Int>;
-
 std::tuple<Tensor, Tensor> compute(
-    const Tensor& logProbs,
-    const Tensor& targets,
-    const Tensor& inputLengths,
-    const Tensor& targetLengths,
+    Tensor logProbs,
+    Tensor targets,
+    Tensor inputLengths,
+    Tensor targetLengths,
     const int64_t blank) {
 
   STD_TORCH_CHECK(logProbs.is_cuda(), "log_probs must be a CUDA tensor");
@@ -307,31 +298,17 @@ std::tuple<Tensor, Tensor> compute(
 
   THO_DISPATCH_V2(logProbs.scalar_type(), "forced_align_impl", AT_WRAP([&] {
         if (targets.scalar_type() == ScalarType::Long) {
-          forced_align_long_impl<scalar_t>(logProbs, targets, blank, paths);
+          (forced_align_impl<scalar_t, ScalarType::Long>(logProbs, targets, blank, paths));
         } else {
-          forced_align_int_impl<scalar_t>(logProbs, targets, blank, paths);
-        }
+          (forced_align_impl<scalar_t, ScalarType::Int>(logProbs, targets, blank, paths));
+          }
       }), AT_EXPAND(AT_FLOATING_TYPES), ScalarType::Half);
-
   Tensor pathsCuda = torchaudio::stable::cuda(paths, logProbs.get_device_index());
   return std::make_tuple(pathsCuda, logProbs);
 }
 
-void boxed_forced_align_gpu(StableIValue* stack, uint64_t num_args, uint64_t num_outputs) {
-  STD_TORCH_CHECK(num_args == 5, "num_args must be 5");
-  STD_TORCH_CHECK(num_outputs == 2, "num_outputs must be 2");
-  std::tuple<Tensor, Tensor> res = compute(
-      /*logProbs*/torch::stable::detail::to<Tensor>(stack[0]),
-      /*targets*/torch::stable::detail::to<Tensor>(stack[1]),
-      /*logit_lengths*/torch::stable::detail::to<Tensor>(stack[2]),
-      /*target_lengths*/torch::stable::detail::to<Tensor>(stack[3]),
-      /*blank*/float(torch::stable::detail::to<int64_t>(stack[4])));
-  stack[0] = torch::stable::detail::from(std::get<0>(res));
-  stack[1] = torch::stable::detail::from(std::get<1>(res));
-}
-
 STABLE_TORCH_LIBRARY_IMPL(torchaudio, CUDA, m) {
-  m.impl("forced_align", &boxed_forced_align_gpu);
+  m.impl("forced_align", TORCH_BOX(&compute));
 }
 
 } // namespace gpu
diff --git a/src/libtorchaudio/overdrive.cpp b/src/libtorchaudio/overdrive.cpp
index 4e3c8bf8c9..ab527ace4e 100644
--- a/src/libtorchaudio/overdrive.cpp
+++ b/src/libtorchaudio/overdrive.cpp
@@ -1,3 +1,4 @@
+#include <libtorchaudio/utils.h>
 #include <torch/csrc/stable/library.h>
 #include <torch/csrc/stable/ops.h>
 #include <torch/csrc/stable/tensor.h>
@@ -5,27 +6,15 @@
 #include <torch/headeronly/core/TensorAccessor.h>
 
 namespace {
-
 using torch::stable::Tensor;
 
-template <typename T, size_t N>
-using TensorAccessor = torch::headeronly::HeaderOnlyTensorAccessor<T, N>;
-
-// TODO: eliminate accessor<T, N>(t) in favor of t.accessor<T, N>
-// after Tensor::accessor is supported in stable ABI
-template <typename T, size_t N>
-inline TensorAccessor<T, N> accessor(Tensor t) {
-  return TensorAccessor<T, N>(
-      reinterpret_cast<T*>(t.data_ptr()), t.sizes().data(), t.strides().data());
-}
-
 template <typename scalar_t>
 void overdrive_cpu_kernel(
-    TensorAccessor<scalar_t, 2> waveform_accessor,
-    TensorAccessor<scalar_t, 2> temp_accessor,
-    TensorAccessor<scalar_t, 1> last_in_accessor,
-    TensorAccessor<scalar_t, 1> last_out_accessor,
-    TensorAccessor<scalar_t, 2> output_waveform_accessor) {
+    torchaudio::TensorAccessor<scalar_t, 2> waveform_accessor,
+    torchaudio::TensorAccessor<scalar_t, 2> temp_accessor,
+    torchaudio::TensorAccessor<scalar_t, 1> last_in_accessor,
+    torchaudio::TensorAccessor<scalar_t, 1> last_out_accessor,
+    torchaudio::TensorAccessor<scalar_t, 2> output_waveform_accessor) {
   int64_t n_frames = waveform_accessor.size(1);
   int64_t n_channels = waveform_accessor.size(0);
 
@@ -56,11 +45,11 @@ std::tuple<Tensor, Tensor, Tensor> overdrive_core_loop_cpu(
       "overdrive_cpu",
       AT_WRAP([&] {
         overdrive_cpu_kernel<scalar_t>(
-            accessor<scalar_t, 2>(waveform),
-            accessor<scalar_t, 2>(temp),
-            accessor<scalar_t, 1>(last_in),
-            accessor<scalar_t, 1>(last_out),
-            accessor<scalar_t, 2>(output_waveform));
+            torchaudio::accessor<scalar_t, 2>(waveform),
+            torchaudio::accessor<scalar_t, 2>(temp),
+            torchaudio::accessor<scalar_t, 1>(last_in),
+            torchaudio::accessor<scalar_t, 1>(last_out),
+            torchaudio::accessor<scalar_t, 2>(output_waveform));
       }),
       AT_FLOATING_TYPES);
   return std::make_tuple(last_in, last_out, output_waveform);
@@ -70,7 +59,11 @@ std::tuple<Tensor, Tensor, Tensor> overdrive_core_loop_cpu(
 
 STABLE_TORCH_LIBRARY_FRAGMENT(torchaudio, m) {
   m.def(
-      "_overdrive_core_loop(Tensor waveform, Tensor temp, Tensor(a!) last_in, Tensor(b!) last_out, Tensor(c!) output_waveform) -> (Tensor(a!), Tensor(b!), Tensor(c!))");
+      "_overdrive_core_loop(Tensor waveform,"
+      "Tensor temp,"
+      "Tensor(a!) last_in,"
+      "Tensor(b!) last_out,"
+      "Tensor(c!) output_waveform) -> (Tensor(a!), Tensor(b!), Tensor(c!))");
 }
 
 STABLE_TORCH_LIBRARY_IMPL(torchaudio, CPU, m) {
diff --git a/src/libtorchaudio/rnnt/cpu/compute.cpp b/src/libtorchaudio/rnnt/cpu/compute.cpp
index 811833c96f..0449b8afcc 100644
--- a/src/libtorchaudio/rnnt/cpu/compute.cpp
+++ b/src/libtorchaudio/rnnt/cpu/compute.cpp
@@ -2,6 +2,7 @@
 #include <torch/csrc/stable/library.h>
 #include <torch/csrc/stable/ops.h>
 #include <torch/csrc/stable/tensor.h>
+#include <torch/headeronly/core/Dispatch_v2.h>
 
 namespace torchaudio {
 namespace rnnt {
@@ -12,10 +13,10 @@ using torch::stable::Tensor;
 
 // Entry point into RNNT Loss
 std::tuple<Tensor, Tensor> compute(
-    const Tensor& logits,
-    const Tensor& targets,
-    const Tensor& logit_lengths,
-    const Tensor& target_lengths,
+    Tensor logits,
+    Tensor targets,
+    Tensor logit_lengths,
+    Tensor target_lengths,
     int64_t blank,
     double clamp,
     bool fused_log_softmax = true) {
@@ -109,6 +110,8 @@ std::tuple<Tensor, Tensor> compute(
       {DtypeWorkspace<float>::ComputeSizeFromOptions(options)},
       ScalarType::Float);
 
+  // TODO: use t.mutable_data_ptr<..>() instead of reinterpret_cast
+  // when stable ABI Tensor supports mutable_data_ptr templates.
   Workspace<float> workspace(
       /*options=*/options,
       /*dtype_data=*/reinterpret_cast<float*>(float_workspace.data_ptr()),
@@ -116,57 +119,27 @@ std::tuple<Tensor, Tensor> compute(
       /*int_data=*/reinterpret_cast<int*>(int_workspace.data_ptr()),
       /*int_size=*/int_workspace.numel());
 
-  switch (logits.scalar_type()) {
-    case ScalarType::Float: {
-      Compute</*DTYPE=*/float, /*CAST_DTYPE=*/float>(
-          /*workspace=*/workspace,
-          /*logits=*/reinterpret_cast<float*>(logits.data_ptr()),
-          /*targets=*/reinterpret_cast<int*>(targets.data_ptr()),
-          /*srcLengths=*/reinterpret_cast<int*>(logit_lengths.data_ptr()),
-          /*tgtLengths=*/reinterpret_cast<int*>(target_lengths.data_ptr()),
-          /*costs=*/reinterpret_cast<float*>(costs.data_ptr()),
-          /*gradients=*/reinterpret_cast<float*>(gradients.data_ptr()));
-      break;
-    }
-    case ScalarType::Half: {
-      Compute</*DTYPE=*/c10::Half, /*CAST_DTYPE=*/float>(
-          /*workspace=*/workspace,
-          /*logits=*/reinterpret_cast<c10::Half*>(logits.data_ptr()),
-          /*targets=*/reinterpret_cast<int*>(targets.data_ptr()),
-          /*srcLengths=*/reinterpret_cast<int*>(logit_lengths.data_ptr()),
-          /*tgtLengths=*/reinterpret_cast<int*>(target_lengths.data_ptr()),
-          /*costs=*/reinterpret_cast<c10::Half*>(costs.data_ptr()),
-          /*gradients=*/reinterpret_cast<c10::Half*>(gradients.data_ptr()));
-      break;
-    }
-    default: {
-      STD_TORCH_CHECK(false, "unreachable");
-    }
-  };
+  THO_DISPATCH_V2(
+      logits.scalar_type(),
+      "rnnt:compute",
+      AT_WRAP([&] {
+        (Compute</*DTYPE=*/scalar_t, /*CAST_DTYPE=*/float>(
+            /*workspace=*/workspace,
+            /*logits=*/reinterpret_cast<scalar_t*>(logits.data_ptr()),
+            /*targets=*/reinterpret_cast<int*>(targets.data_ptr()),
+            /*srcLengths=*/reinterpret_cast<int*>(logit_lengths.data_ptr()),
+            /*tgtLengths=*/reinterpret_cast<int*>(target_lengths.data_ptr()),
+            /*costs=*/reinterpret_cast<scalar_t*>(costs.data_ptr()),
+            /*gradients=*/reinterpret_cast<scalar_t*>(gradients.data_ptr())));
+      }),
+      ScalarType::Float,
+      ScalarType::Half);
 
   return std::make_tuple(costs, gradients);
 }
 
-void boxed_rnnt_loss(
-    StableIValue* stack,
-    uint64_t num_args,
-    uint64_t num_outputs) {
-  STD_TORCH_CHECK(num_args == 7, "num_args must be 7");
-  STD_TORCH_CHECK(num_outputs == 2, "num_outputs must be 2");
-  std::tuple<Tensor, Tensor> res = compute(
-      /*logits*/ torch::stable::detail::to<Tensor>(stack[0]),
-      /*targets*/ torch::stable::detail::to<Tensor>(stack[1]),
-      /*logit_lengths*/ torch::stable::detail::to<Tensor>(stack[2]),
-      /*target_lengths*/ torch::stable::detail::to<Tensor>(stack[3]),
-      /*blank*/ float(torch::stable::detail::to<int64_t>(stack[4])),
-      /*clamp*/ torch::stable::detail::to<double>(stack[5]),
-      /*fused_log_softmax*/ torch::stable::detail::to<bool>(stack[6]));
-  stack[0] = torch::stable::detail::from(std::get<0>(res));
-  stack[1] = torch::stable::detail::from(std::get<1>(res));
-}
-
 STABLE_TORCH_LIBRARY_IMPL(torchaudio, CPU, m) {
-  m.impl("rnnt_loss_forward", &boxed_rnnt_loss);
+  m.impl("rnnt_loss_forward", TORCH_BOX(&compute));
 }
 
 } // namespace cpu
diff --git a/src/libtorchaudio/rnnt/gpu/compute.cu b/src/libtorchaudio/rnnt/gpu/compute.cu
index 77c6a0e268..800f029121 100644
--- a/src/libtorchaudio/rnnt/gpu/compute.cu
+++ b/src/libtorchaudio/rnnt/gpu/compute.cu
@@ -148,11 +148,6 @@ std::tuple<Tensor, Tensor> compute(
   return std::make_tuple(costs, gradients);
 }
 
-STABLE_TORCH_LIBRARY_FRAGMENT(torchaudio, m) {
-  m.def(
-      "rnnt_loss_forward(Tensor logits, Tensor targets, Tensor logit_lengths, Tensor target_lengths, int blank, double clamp, bool fused_log_softmax) -> (Tensor, Tensor)");
-}
-
 STABLE_TORCH_LIBRARY_IMPL(torchaudio, CUDA, m) {
   m.impl("rnnt_loss_forward", TORCH_BOX(&compute));
 }
diff --git a/src/libtorchaudio/stable/TensorAccessor.h b/src/libtorchaudio/stable/TensorAccessor.h
deleted file mode 100644
index db786dafa6..0000000000
--- a/src/libtorchaudio/stable/TensorAccessor.h
+++ /dev/null
@@ -1,275 +0,0 @@
-#pragma once
-/*
-  This header files provides torchaudio::stable::TensorAccessor
-  templates that are torch::stable::Tensor-compatible analogus of
-  at::TensorAccessor defined in ATen/core/TensorAccessor.h.
-
-  TODO: remove this header file when torch::stable provides all
-  features implemented here.
-*/
-
-// #include <libtorchaudio/stable/Device.h>
-
-#include <torch/headeronly/macros/Macros.h>
-#include <type_traits>
-
-namespace torchaudio::stable {
-
-template <typename T>
-struct DefaultPtrTraits {
-  typedef T* PtrType;
-};
-
-#if defined(__CUDACC__) || defined(__HIPCC__)
-template <typename T>
-struct RestrictPtrTraits {
-  typedef T* __restrict__ PtrType;
-};
-#endif
-
-template <
-    typename T,
-    size_t N,
-    template <typename U> class PtrTraits = DefaultPtrTraits,
-    typename index_t = int64_t>
-class TensorAccessorBase {
- public:
-  typedef typename PtrTraits<T>::PtrType PtrType;
-
-  C10_HOST_DEVICE TensorAccessorBase(
-      PtrType data_,
-      const index_t* sizes_,
-      const index_t* strides_)
-      : data_(data_) /*, sizes_(sizes_), strides_(strides_)*/ {
-    // Originally, TensorAccessor is a view of sizes and strides as
-    // these are ArrayRef instances. Until torch::stable supports
-    // ArrayRef-like features, we store copies of sizes and strides:
-    for (size_t i = 0; i < N; ++i) {
-      this->sizes_[i] = sizes_[i];
-      this->strides_[i] = strides_[i];
-    }
-  }
-
-  C10_HOST_DEVICE PtrType data() {
-    return data_;
-  }
-  C10_HOST_DEVICE const PtrType data() const {
-    return data_;
-  }
-
- protected:
-  PtrType data_;
-  /*
-    const index_t* sizes_;
-    const index_t* strides_;
-  */
-  // NOLINTNEXTLINE(*c-arrays*)
-  index_t sizes_[N];
-  // NOLINTNEXTLINE(*c-arrays*)
-  index_t strides_[N];
-};
-
-template <
-    typename T,
-    size_t N,
-    template <typename U> class PtrTraits = DefaultPtrTraits,
-    typename index_t = int64_t>
-class TensorAccessor : public TensorAccessorBase<T, N, PtrTraits, index_t> {
- public:
-  typedef typename PtrTraits<T>::PtrType PtrType;
-
-  C10_HOST_DEVICE TensorAccessor(
-      PtrType data_,
-      const index_t* sizes_,
-      const index_t* strides_)
-      : TensorAccessorBase<T, N, PtrTraits, index_t>(data_, sizes_, strides_) {}
-
-  C10_HOST_DEVICE TensorAccessor<T, N - 1, PtrTraits, index_t> operator[](
-      index_t i) {
-    return TensorAccessor<T, N - 1, PtrTraits, index_t>(
-        this->data_ + this->strides_[0] * i,
-        this->sizes_ + 1,
-        this->strides_ + 1);
-  }
-
-  C10_HOST_DEVICE const TensorAccessor<T, N - 1, PtrTraits, index_t> operator[](
-      index_t i) const {
-    return TensorAccessor<T, N - 1, PtrTraits, index_t>(
-        this->data_ + this->strides_[0] * i,
-        this->sizes_ + 1,
-        this->strides_ + 1);
-  }
-};
-
-template <typename T, template <typename U> class PtrTraits, typename index_t>
-class TensorAccessor<T, 1, PtrTraits, index_t>
-    : public TensorAccessorBase<T, 1, PtrTraits, index_t> {
- public:
-  typedef typename PtrTraits<T>::PtrType PtrType;
-
-  C10_HOST_DEVICE TensorAccessor(
-      PtrType data_,
-      const index_t* sizes_,
-      const index_t* strides_)
-      : TensorAccessorBase<T, 1, PtrTraits, index_t>(data_, sizes_, strides_) {}
-  C10_HOST_DEVICE T& operator[](index_t i) {
-    // NOLINTNEXTLINE(clang-analyzer-core.NullDereference)
-    return this->data_[this->strides_[0] * i];
-  }
-  C10_HOST_DEVICE const T& operator[](index_t i) const {
-    return this->data_[this->strides_[0] * i];
-  }
-};
-
-template <
-    typename T,
-    size_t N,
-    template <typename U> class PtrTraits = DefaultPtrTraits,
-    typename index_t = int64_t>
-class GenericPackedTensorAccessorBase {
- public:
-  typedef typename PtrTraits<T>::PtrType PtrType;
-  C10_HOST GenericPackedTensorAccessorBase(
-      PtrType data_,
-      const index_t* sizes_,
-      const index_t* strides_)
-      : data_(data_) {
-    std::copy(sizes_, sizes_ + N, std::begin(this->sizes_));
-    std::copy(strides_, strides_ + N, std::begin(this->strides_));
-  }
-
-  template <
-      typename source_index_t,
-      class = std::enable_if_t<std::is_same_v<source_index_t, int64_t>>>
-  C10_HOST GenericPackedTensorAccessorBase(
-      PtrType data_,
-      const source_index_t* sizes_,
-      const source_index_t* strides_)
-      : data_(data_) {
-    for (size_t i = 0; i < N; ++i) {
-      this->sizes_[i] = sizes_[i];
-      this->strides_[i] = strides_[i];
-    }
-  }
-
-  C10_HOST_DEVICE PtrType data() {
-    return data_;
-  }
-  C10_HOST_DEVICE const PtrType data() const {
-    return data_;
-  }
-
- protected:
-  PtrType data_;
-  // NOLINTNEXTLINE(*c-arrays*)
-  index_t sizes_[N];
-  // NOLINTNEXTLINE(*c-arrays*)
-  index_t strides_[N];
-  C10_HOST void bounds_check_(index_t i) const {
-    STD_TORCH_CHECK(
-        0 <= i && i < index_t{N},
-        "Index ",
-        i,
-        " is not within bounds of a tensor of dimension ",
-        N);
-  }
-};
-
-template <
-    typename T,
-    size_t N,
-    template <typename U> class PtrTraits = DefaultPtrTraits,
-    typename index_t = int64_t>
-class GenericPackedTensorAccessor
-    : public GenericPackedTensorAccessorBase<T, N, PtrTraits, index_t> {
- public:
-  typedef typename PtrTraits<T>::PtrType PtrType;
-
-  C10_HOST GenericPackedTensorAccessor(
-      PtrType data_,
-      const index_t* sizes_,
-      const index_t* strides_)
-      : GenericPackedTensorAccessorBase<T, N, PtrTraits, index_t>(
-            data_,
-            sizes_,
-            strides_) {}
-
-  // if index_t is not int64_t, we want to have an int64_t constructor
-  template <
-      typename source_index_t,
-      class = std::enable_if_t<std::is_same_v<source_index_t, int64_t>>>
-  C10_HOST GenericPackedTensorAccessor(
-      PtrType data_,
-      const source_index_t* sizes_,
-      const source_index_t* strides_)
-      : GenericPackedTensorAccessorBase<T, N, PtrTraits, index_t>(
-            data_,
-            sizes_,
-            strides_) {}
-
-  C10_DEVICE TensorAccessor<T, N - 1, PtrTraits, index_t> operator[](
-      index_t i) {
-    index_t* new_sizes = this->sizes_ + 1;
-    index_t* new_strides = this->strides_ + 1;
-    return TensorAccessor<T, N - 1, PtrTraits, index_t>(
-        this->data_ + this->strides_[0] * i, new_sizes, new_strides);
-  }
-
-  C10_DEVICE const TensorAccessor<T, N - 1, PtrTraits, index_t> operator[](
-      index_t i) const {
-    const index_t* new_sizes = this->sizes_ + 1;
-    const index_t* new_strides = this->strides_ + 1;
-    return TensorAccessor<T, N - 1, PtrTraits, index_t>(
-        this->data_ + this->strides_[0] * i, new_sizes, new_strides);
-  }
-};
-
-template <typename T, template <typename U> class PtrTraits, typename index_t>
-class GenericPackedTensorAccessor<T, 1, PtrTraits, index_t>
-    : public GenericPackedTensorAccessorBase<T, 1, PtrTraits, index_t> {
- public:
-  typedef typename PtrTraits<T>::PtrType PtrType;
-  C10_HOST GenericPackedTensorAccessor(
-      PtrType data_,
-      const index_t* sizes_,
-      const index_t* strides_)
-      : GenericPackedTensorAccessorBase<T, 1, PtrTraits, index_t>(
-            data_,
-            sizes_,
-            strides_) {}
-
-  template <
-      typename source_index_t,
-      class = std::enable_if_t<std::is_same_v<source_index_t, int64_t>>>
-  C10_HOST GenericPackedTensorAccessor(
-      PtrType data_,
-      const source_index_t* sizes_,
-      const source_index_t* strides_)
-      : GenericPackedTensorAccessorBase<T, 1, PtrTraits, index_t>(
-            data_,
-            sizes_,
-            strides_) {}
-
-  C10_DEVICE T& operator[](index_t i) {
-    return this->data_[this->strides_[0] * i];
-  }
-  C10_DEVICE const T& operator[](index_t i) const {
-    return this->data_[this->strides_[0] * i];
-  }
-};
-
-template <
-    typename T,
-    size_t N,
-    template <typename U> class PtrTraits = DefaultPtrTraits>
-using PackedTensorAccessor32 =
-    GenericPackedTensorAccessor<T, N, PtrTraits, int32_t>;
-
-template <
-    typename T,
-    size_t N,
-    template <typename U> class PtrTraits = DefaultPtrTraits>
-using PackedTensorAccessor64 =
-    GenericPackedTensorAccessor<T, N, PtrTraits, int64_t>;
-
-} // namespace torchaudio::stable
diff --git a/src/libtorchaudio/stable/ops.h b/src/libtorchaudio/stable/ops.h
index 529dbccd3b..fa5a89345d 100644
--- a/src/libtorchaudio/stable/ops.h
+++ b/src/libtorchaudio/stable/ops.h
@@ -10,7 +10,6 @@
 */
 
 #include <libtorchaudio/stable/Device.h>
-#include <libtorchaudio/stable/TensorAccessor.h>
 #include <torch/csrc/stable/ops.h>
 
 #ifdef USE_CUDA
@@ -59,87 +58,6 @@ const T* const_data_ptr(const Tensor& t) {
   return reinterpret_cast<const T*>(data_ptr);
 }
 
-// TODO: When accessor is implemented in torch::stable, eliminate
-// accessor template below.
-template <typename T, size_t N>
-torchaudio::stable::TensorAccessor<T, N> accessor(const Tensor& t) {
-  static_assert(
-      N > 0,
-      "accessor is used for indexing tensor, for scalars use *data_ptr<T>()");
-  STD_TORCH_CHECK(
-      t.dim() == N,
-      "TensorAccessor expected ",
-      N,
-      " dims but tensor has ",
-      t.dim());
-  T* ptr = nullptr;
-  if constexpr (std::is_const_v<T>) {
-    ptr = const_data_ptr<T>(t);
-  } else {
-    ptr = mutable_data_ptr<T>(t);
-  }
-  auto sizes_ = sizes(t);
-  auto strides_ = strides(t);
-  return torchaudio::stable::TensorAccessor<T, N>(
-      ptr, sizes_.data(), strides_.data());
-}
-
-// TODO: move to TensorAccessor.h?
-template <
-    typename T,
-    size_t N,
-    template <typename U>
-    class PtrTraits = torchaudio::stable::DefaultPtrTraits,
-    typename index_t = int64_t>
-torchaudio::stable::GenericPackedTensorAccessor<T, N, PtrTraits, index_t>
-generic_packed_accessor(const Tensor& t) {
-  static_assert(
-      N > 0,
-      "accessor is used for indexing tensor, for scalars use *data_ptr<T>()");
-  STD_TORCH_CHECK(
-      t.dim() == N,
-      "TensorAccessor expected ",
-      N,
-      " dims but tensor has ",
-      t.dim());
-  T* ptr = nullptr;
-  if constexpr (std::is_const_v<T>) {
-    ptr = const_data_ptr<T>(t);
-  } else {
-    ptr = mutable_data_ptr<T>(t);
-  }
-  auto sizes_ = sizes(t);
-  auto strides_ = strides(t);
-  return torchaudio::stable::
-      GenericPackedTensorAccessor<T, N, PtrTraits, index_t>(
-          static_cast<typename PtrTraits<T>::PtrType>(ptr),
-          sizes_.data(),
-          strides_.data());
-}
-
-template <
-    typename T,
-    size_t N,
-    template <typename U>
-    class PtrTraits = torchaudio::stable::DefaultPtrTraits>
-torchaudio::stable::PackedTensorAccessor32<T, N, PtrTraits> packed_accessor32(
-    const Tensor& t) {
-  STD_TORCH_CHECK(
-      t.numel() <= static_cast<int64_t>(std::numeric_limits<int32_t>::max()),
-      "numel needs to be smaller than int32_t max; otherwise, please use packed_accessor64");
-  return generic_packed_accessor<T, N, PtrTraits, int32_t>(t);
-}
-
-template <
-    typename T,
-    size_t N,
-    template <typename U>
-    class PtrTraits = torchaudio::stable::DefaultPtrTraits>
-torchaudio::stable::PackedTensorAccessor64<T, N, PtrTraits> packed_accessor64(
-    const Tensor& t) {
-  return generic_packed_accessor<T, N, PtrTraits, int64_t>();
-}
-
 // TODO: When cpu is implemented in torch::stable, eliminate
 // cpu function below.
 inline Tensor cpu(const Tensor& self) {
diff --git a/src/libtorchaudio/utils.h b/src/libtorchaudio/utils.h
index 4693f7e91c..a700e30efa 100644
--- a/src/libtorchaudio/utils.h
+++ b/src/libtorchaudio/utils.h
@@ -1,5 +1,7 @@
 #pragma once
 
+#include <torch/headeronly/core/TensorAccessor.h>
+
 // TODO: replace the include libtorchaudio/stable/ops.h with
 // torch/stable/ops.h when torch::stable provides all required
 // features (torch::stable::item<T> or similar):
@@ -17,4 +19,35 @@ T max(const torch::stable::Tensor& t) {
 bool is_align_available();
 std::optional<int64_t> cuda_version();
 
+template <typename T, size_t N>
+using TensorAccessor = torch::headeronly::HeaderOnlyTensorAccessor<T, N>;
+
+// TODO: eliminate accessor<T, N>(t) in favor of t.accessor<T, N>
+// after Tensor::accessor is supported in stable ABI
+template <typename T, size_t N>
+inline TensorAccessor<T, N> accessor(Tensor t) {
+  return TensorAccessor<T, N>(
+      reinterpret_cast<T*>(t.data_ptr()), t.sizes().data(), t.strides().data());
+}
+
+#if defined(__CUDACC__) || defined(__HIPCC__)
+template <typename T, size_t N>
+using PackedTensorAccessor32 =
+    torch::headeronly::HeaderOnlyGenericPackedTensorAccessor<
+        T,
+        N,
+        torch::headeronly::RestrictPtrTraits,
+        int32_t>;
+
+// TODO: eliminate accessor<T, N>(t) in favor of t.accessor<T, N>
+// after Tensor::accessor is supported in stable ABI
+template <typename T, size_t N>
+inline PackedTensorAccessor32<T, N> packed_accessor32(Tensor t) {
+  return PackedTensorAccessor32<T, N>(
+      static_cast<typename PackedTensorAccessor32<T, N>::PtrType>(t.data_ptr()),
+      t.sizes().data(),
+      t.strides().data());
+}
+#endif
+
 } // namespace torchaudio