diff --git a/lerobot/common/datasets/utils.py b/lerobot/common/datasets/utils.py
index b20b63fe..ae8fa001 100644
--- a/lerobot/common/datasets/utils.py
+++ b/lerobot/common/datasets/utils.py
@@ -80,6 +80,7 @@ def hf_transform_to_torch(items_dict: dict[torch.Tensor | None]):
         if isinstance(first_item, PILImage.Image):
             to_tensor = transforms.ToTensor()
             items_dict[key] = [to_tensor(img) for img in items_dict[key]]
+        # TODO(aliberts): remove this part as we'll be using task_index
         elif isinstance(first_item, str):
             # TODO (michel-aractingi): add str2embedding via language tokenizer
             # For now we leave this part up to the user to choose how to address
@@ -96,13 +97,13 @@ def hf_transform_to_torch(items_dict: dict[torch.Tensor | None]):
 
 
 @cache
-def get_hub_safe_version(repo_id: str, version: str) -> str:
+def get_hub_safe_version(repo_id: str, version: str, enforce_v2: bool = True) -> str:
     num_version = float(version.strip("v"))
-    if num_version < 2:
+    if num_version < 2 and enforce_v2:
         raise ValueError(
             f"""The dataset you requested ({repo_id}) is in {version} format. We introduced a new
             format with v2.0 that is not backward compatible. Please use our conversion script
-            first (convert_dataset_16_to_20.py) to convert your dataset to this new format."""
+            first (convert_dataset_v1_to_v2.py) to convert your dataset to this new format."""
         )
     api = HfApi()
     dataset_info = api.list_repo_refs(repo_id, repo_type="dataset")
@@ -192,7 +193,9 @@ def load_tasks(repo_id: str, version: str, local_dir: Path) -> dict:
         repo_id, filename="meta/tasks.json", local_dir=local_dir, repo_type="dataset", revision=version
     )
     with open(fpath) as f:
-        return json.load(f)
+        tasks = json.load(f)
+
+    return {item["task_index"]: item["task"] for item in sorted(tasks, key=lambda x: x["task_index"])}
 
 
 def get_episode_data_index(episodes: list, episode_dicts: list[dict]) -> dict[str, torch.Tensor]:
diff --git a/convert_dataset_v1_to_v2.py b/lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py
similarity index 96%
rename from convert_dataset_v1_to_v2.py
rename to lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py
index ede8905f..cecab0df 100644
--- a/convert_dataset_v1_to_v2.py
+++ b/lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py
@@ -3,13 +3,18 @@ This script will help you convert any LeRobot dataset already pushed to the hub
 2.0. You will be required to provide the 'tasks', which is a short but accurate description in plain English
 for each of the task performed in the dataset. This will allow to easily train models with task-conditionning.
 
-We support 3 different scenarios for these tasks:
+We support 3 different scenarios for these tasks (see instructions below):
     1. Single task dataset: all episodes of your dataset have the same single task.
     2. Single task episodes: the episodes of your dataset each contain a single task but they can differ from
       one episode to the next.
     3. Multi task episodes: episodes of your dataset may each contain several different tasks.
 
 
+Can you can also provide a robot config .yaml file (not mandatory) to this script via the option
+'--robot-config' so that it writes information about the robot (robot type, motors names) this dataset was
+recorded with. For now, only Aloha/Koch type robots are supported with this option.
+
+
 # 1. Single task dataset
 If your dataset contains a single task, you can simply provide it directly via the CLI with the
 '--single-task' option.
@@ -17,7 +22,7 @@ If your dataset contains a single task, you can simply provide it directly via t
 Examples:
 
 ```bash
-python convert_dataset_v1_to_v2.py \
+python lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py \
     --repo-id lerobot/aloha_sim_insertion_human_image \
     --single-task "Insert the peg into the socket." \
     --robot-config lerobot/configs/robot/aloha.yaml \
@@ -25,7 +30,7 @@ python convert_dataset_v1_to_v2.py \
 ```
 
 ```bash
-python convert_dataset_v1_to_v2.py \
+python lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py \
     --repo-id aliberts/koch_tutorial \
     --single-task "Pick the Lego block and drop it in the box on the right." \
     --robot-config lerobot/configs/robot/koch.yaml \
@@ -42,7 +47,7 @@ If your dataset is a multi-task dataset, you have two options to provide the tas
     Example:
 
     ```bash
-    python convert_dataset_v1_to_v2.py \
+    python lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py \
         --repo-id lerobot/stanford_kuka_multimodal_dataset \
         --tasks-col "language_instruction" \
         --local-dir data
@@ -71,7 +76,7 @@ parquet file, and you must provide this column's name with the '--tasks-col' arg
 Example:
 
 ```bash
-python convert_dataset_v1_to_v2.py \
+python lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py \
     --repo-id lerobot/stanford_kuka_multimodal_dataset \
     --tasks-col "language_instruction" \
     --local-dir data
@@ -321,6 +326,7 @@ def get_videos_info(repo_id: str, local_dir: Path, video_keys: list[str]) -> dic
     hub_api = HfApi()
     videos_info_dict = {"videos_path": VIDEO_PATH}
     for vid_key in video_keys:
+        # Assumes first episode
         video_path = VIDEO_PATH.format(video_key=vid_key, episode_index=0)
         video_path = hub_api.hf_hub_download(
             repo_id=repo_id, repo_type="dataset", local_dir=local_dir, filename=video_path
@@ -437,7 +443,7 @@ def convert_dataset(
     episode_lengths = split_parquet_by_episodes(dataset, keys, total_episodes, episode_indices, v20_dir)
 
     # Shapes
-    sequence_shapes = {key: len(dataset[key][0]) for key in keys["sequence"]}
+    sequence_shapes = {key: dataset.features[key].length for key in keys["sequence"]}
     image_shapes = get_image_shapes(dataset, keys["image"]) if len(keys["image"]) > 0 else {}
     if len(keys["video"]) > 0:
         assert metadata_v1.get("video", False)
@@ -479,6 +485,7 @@ def convert_dataset(
         "data_path": PARQUET_PATH,
         "robot_type": robot_type,
         "total_episodes": total_episodes,
+        "total_frames": len(dataset),
         "total_tasks": len(tasks),
         "fps": metadata_v1["fps"],
         "splits": {"train": f"0:{total_episodes}"},
@@ -512,13 +519,6 @@ def convert_dataset(
         repo_type="dataset",
         revision="main",
     )
-    hub_api.upload_folder(
-        repo_id=repo_id,
-        path_in_repo="videos",
-        folder_path=v1x_dir / "videos",
-        repo_type="dataset",
-        revision="main",
-    )
     hub_api.upload_folder(
         repo_id=repo_id,
         path_in_repo="meta",