diff --git a/lerobot/common/datasets/transforms.py b/lerobot/common/datasets/transforms.py
new file mode 100644
index 00000000..b53215aa
--- /dev/null
+++ b/lerobot/common/datasets/transforms.py
@@ -0,0 +1,224 @@
+from pathlib import Path
+from typing import Sequence
+
+import torch
+from tensordict import TensorDictBase
+from tensordict.nn import dispatch
+from tensordict.utils import NestedKey
+from torchaudio.io import StreamReader
+from torchrl.envs.transforms import Transform
+
+
+class ViewSliceHorizonTransform(Transform):
+    invertible = False
+
+    def __init__(self, num_slices, horizon):
+        super().__init__()
+        self.num_slices = num_slices
+        self.horizon = horizon
+
+    def _reset(self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase) -> TensorDictBase:
+        # _reset is called once when the environment reset to normalize the first observation
+        tensordict_reset = self._call(tensordict_reset)
+        return tensordict_reset
+
+    @dispatch(source="in_keys", dest="out_keys")
+    def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
+        return self._call(tensordict)
+
+    def _call(self, td: TensorDictBase) -> TensorDictBase:
+        td = td.view(self.num_slices, self.horizon)
+        return td
+
+
+class KeepFrames(Transform):
+    invertible = False
+
+    def __init__(
+        self,
+        positions,
+        in_keys: Sequence[NestedKey],
+        out_keys: Sequence[NestedKey] = None,
+    ):
+        if isinstance(positions, list):
+            assert isinstance(positions[0], int)
+        # TODO(rcadene)L add support for `isinstance(positions, int)`?
+
+        self.positions = positions
+        if out_keys is None:
+            out_keys = in_keys
+        super().__init__(in_keys=in_keys, out_keys=out_keys)
+
+    def _reset(self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase) -> TensorDictBase:
+        # _reset is called once when the environment reset to normalize the first observation
+        tensordict_reset = self._call(tensordict_reset)
+        return tensordict_reset
+
+    @dispatch(source="in_keys", dest="out_keys")
+    def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
+        return self._call(tensordict)
+
+    def _call(self, td: TensorDictBase) -> TensorDictBase:
+        # we need set batch_size=[] before assigning a different shape to td[outkey]
+        td.batch_size = []
+
+        for inkey, outkey in zip(self.in_keys, self.out_keys, strict=False):
+            # TODO(rcadene): don't know how to do `inkey not in td`
+            if td.get(inkey, None) is None:
+                continue
+            td[outkey] = td[inkey][:, self.positions]
+        return td
+
+
+class DecodeVideoTransform(Transform):
+    invertible = False
+
+    def __init__(
+        self,
+        data_dir: Path | str,
+        device="cpu",
+        # format options are None=yuv420p (usually), rgb24, bgr24, etc.
+        format: str | None = None,
+        frame_rate: int | None = None,
+        width: int | None = None,
+        height: int | None = None,
+        in_keys: Sequence[NestedKey] = None,
+        out_keys: Sequence[NestedKey] = None,
+        in_keys_inv: Sequence[NestedKey] | None = None,
+        out_keys_inv: Sequence[NestedKey] | None = None,
+    ):
+        self.data_dir = Path(data_dir)
+        self.device = device
+        self.format = format
+        self.frame_rate = frame_rate
+        self.width = width
+        self.height = height
+        self.video_id_to_path = None
+        if out_keys is None:
+            out_keys = in_keys
+        if in_keys_inv is None:
+            in_keys_inv = out_keys
+        if out_keys_inv is None:
+            out_keys_inv = in_keys
+        super().__init__(
+            in_keys=in_keys, out_keys=out_keys, in_keys_inv=in_keys_inv, out_keys_inv=out_keys_inv
+        )
+
+    def set_video_id_to_path(self, video_id_to_path):
+        self.video_id_to_path = video_id_to_path
+
+    def _reset(self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase) -> TensorDictBase:
+        # _reset is called once when the environment reset to normalize the first observation
+        tensordict_reset = self._call(tensordict_reset)
+        return tensordict_reset
+
+    @dispatch(source="in_keys", dest="out_keys")
+    def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
+        return self._call(tensordict)
+
+    def _call(self, td: TensorDictBase) -> TensorDictBase:
+        assert (
+            self.video_id_to_path is not None
+        ), "Setting a video_id_to_path dictionary with `self.set_video_id_to_path(video_id_to_path)` is required."
+
+        for inkey, outkey in zip(self.in_keys, self.out_keys, strict=False):
+            # TODO(rcadene): don't know how to do `inkey not in td`
+            if td.get(inkey, None) is None:
+                continue
+
+            bsize = len(td[inkey])  # num episodes in the batch
+            b_frames = []
+            for i in range(bsize):
+                assert (
+                    td["observation", "frame", "video_id"].ndim == 3
+                    and td["observation", "frame", "video_id"].shape[2] == 1
+                ), "We expect 2 dims. Respectively, number of episodes in the batch, number of observations, 1"
+
+                ep_video_ids = td[inkey]["video_id"][i]
+                timestamps = td[inkey]["timestamp"][i]
+                frame_ids = td["frame_id"][i]
+
+                unique_video_id = (ep_video_ids.min() == ep_video_ids.max()).item()
+                assert unique_video_id
+
+                is_ascending = torch.all(timestamps[:-1] <= timestamps[1:]).item()
+                assert is_ascending
+
+                is_contiguous = ((frame_ids[1:] - frame_ids[:-1]) == 1).all().item()
+                assert is_contiguous
+
+                FIRST_FRAME = 0  # noqa: N806
+                video_id = ep_video_ids[FIRST_FRAME].squeeze(0).item()
+                video_path = self.data_dir / self.video_id_to_path[video_id]
+                first_frame_ts = timestamps[FIRST_FRAME].squeeze(0).item()
+                num_contiguous_frames = len(timestamps)
+
+                filter_desc = []
+                video_stream_kwgs = {
+                    "frames_per_chunk": num_contiguous_frames,
+                    "buffer_chunk_size": num_contiguous_frames,
+                }
+
+                # choice of decoder
+                if self.device == "cuda":
+                    video_stream_kwgs["hw_accel"] = "cuda"
+                    video_stream_kwgs["decoder"] = "h264_cuvid"
+                    # video_stream_kwgs["decoder"] = "hevc_cuvid"
+                    # video_stream_kwgs["decoder"] = "av1_cuvid"
+                    # video_stream_kwgs["decoder"] = "ffv1_cuvid"
+                else:
+                    video_stream_kwgs["decoder"] = "h264"
+                    # video_stream_kwgs["decoder"] = "hevc"
+                    # video_stream_kwgs["decoder"] = "av1"
+                    # video_stream_kwgs["decoder"] = "ffv1"
+
+                # resize
+                resize_width = self.width is not None
+                resize_height = self.height is not None
+                if resize_width or resize_height:
+                    if self.device == "cuda":
+                        assert resize_width and resize_height
+                        video_stream_kwgs["decoder_option"] = {"resize": f"{self.width}x{self.height}"}
+                    else:
+                        scales = []
+                        if resize_width:
+                            scales.append(f"width={self.width}")
+                        if resize_height:
+                            scales.append(f"height={self.height}")
+                        filter_desc.append(f"scale={':'.join(scales)}")
+
+                # choice of format
+                if self.format is not None:
+                    if self.device == "cuda":
+                        # TODO(rcadene): rebuild ffmpeg with --enable-cuda-nvcc, --enable-cuvid, and --enable-libnpp
+                        raise NotImplementedError()
+                        # filter_desc = f"scale=format={self.format}"
+                        # filter_desc = f"scale_cuda=format={self.format}"
+                        # filter_desc = f"scale_npp=format={self.format}"
+                    else:
+                        filter_desc.append(f"format=pix_fmts={self.format}")
+
+                # choice of frame rate
+                if self.frame_rate is not None:
+                    filter_desc.append(f"fps={self.frame_rate}")
+
+                if len(filter_desc) > 0:
+                    video_stream_kwgs["filter_desc"] = ",".join(filter_desc)
+
+                # create a stream and load a certain number of frame at a certain frame rate
+                # TODO(rcadene): make sure it's the most optimal way to do it
+                # s = StreamReader(str(video_path).replace('.mp4','.mkv'))
+                s = StreamReader(str(video_path))
+                s.seek(first_frame_ts)
+                s.add_video_stream(**video_stream_kwgs)
+                s.fill_buffer()
+                (frames,) = s.pop_chunks()
+
+                b_frames.append(frames)
+
+            td[outkey] = torch.stack(b_frames)
+
+        if self.device == "cuda":
+            # make sure we return a cuda tensor, since the frames can be unwillingly sent to cpu
+            assert "cuda" in str(td[outkey].device), f"{td[outkey].device} instead of cuda"
+        return td