diff --git a/requirements-test.txt b/requirements-test.txt
index f5a93928..99f5c608 100644
--- a/requirements-test.txt
+++ b/requirements-test.txt
@@ -1,5 +1,7 @@
 black==22.3.0
-mypy==0.942
+mypy==1.6.0
+types-requests==2.31.0.8
+types-PyYAML==6.0.12.12
 pylint==2.13.4
 pytest-cov==3.0.0
 pytest-xdist==2.5.0
diff --git a/video2dataset/dataloader/custom_wds.py b/video2dataset/dataloader/custom_wds.py
index 6e0e3285..f1dd6ae5 100644
--- a/video2dataset/dataloader/custom_wds.py
+++ b/video2dataset/dataloader/custom_wds.py
@@ -458,10 +458,10 @@ class TorchDataWebdataset(DataPipeline, FluidInterfaceWithChangedDecode):
     def __init__(
         self,
         urls: Union[List[str], str],
-        repeat: int = None,
+        repeat: Optional[int] = None,
         shardshuffle: int = 10000,
         sample_shuffle: int = 0,
-        buffer_size: int = None,
+        buffer_size: Optional[int] = None,
         resample_prefixes: bool = False,
         prefix_probs: Optional[List[float]] = None,
         drop_last: bool = False,
diff --git a/video2dataset/distributor.py b/video2dataset/distributor.py
index 279a2332..4dc24236 100644
--- a/video2dataset/distributor.py
+++ b/video2dataset/distributor.py
@@ -226,6 +226,7 @@ def _make_sbatch(self):
     def _make_launch_cpu(
         self,
     ):
+        """Create cpu launcher"""
 
         venv = os.environ.get("VIRTUAL_ENV")
         if venv:
@@ -236,7 +237,7 @@ def _make_launch_cpu(
                 venv_activate = f"conda activate {conda_env}"
             else:
                 raise ValueError("You need to specify either a virtual environment or a conda environment.")
-        
+
         cdir = os.path.abspath(os.path.dirname(__file__))
         script = os.path.join(cdir, "slurm_executor.py")
         project_root = os.path.abspath(os.path.join(cdir, ".."))
diff --git a/video2dataset/main.py b/video2dataset/main.py
index d69d1bf5..610b4ec8 100644
--- a/video2dataset/main.py
+++ b/video2dataset/main.py
@@ -42,7 +42,7 @@ def video2dataset(
     output_folder: str = "dataset",
     output_format: str = "files",
     input_format: str = "csv",
-    encode_formats: dict = None,
+    encode_formats: Optional[dict] = None,
     stage: str = "download",
     url_col: str = "url",
     caption_col: Optional[str] = None,
diff --git a/video2dataset/subsamplers/audio_rate_subsampler.py b/video2dataset/subsamplers/audio_rate_subsampler.py
index 04efd6e4..48d94038 100644
--- a/video2dataset/subsamplers/audio_rate_subsampler.py
+++ b/video2dataset/subsamplers/audio_rate_subsampler.py
@@ -22,7 +22,7 @@ def __init__(self, sample_rate, encode_formats, n_audio_channels=None):
 
     def __call__(self, streams, metadata=None):
         audio_bytes = streams.pop("audio")
-        subsampled_bytes, subsampled_metas = [], []
+        subsampled_bytes = []
         for aud_bytes in audio_bytes:
             with tempfile.TemporaryDirectory() as tmpdir:
                 with open(os.path.join(tmpdir, "input.m4a"), "wb") as f:
diff --git a/video2dataset/subsamplers/caption_subsampler.py b/video2dataset/subsamplers/caption_subsampler.py
index e6ece939..c5e08e80 100644
--- a/video2dataset/subsamplers/caption_subsampler.py
+++ b/video2dataset/subsamplers/caption_subsampler.py
@@ -6,6 +6,7 @@
 import torch
 from torch import nn
 from einops import rearrange
+from typing import Optional
 
 from transformers import (
     AutoModelForCausalLM,
@@ -41,11 +42,12 @@ class VideoBlipVisionModel(Blip2VisionModel):
 
     def forward(
         self,
-        pixel_values: torch.FloatTensor = None,
-        output_attentions: bool = None,
-        output_hidden_states: bool = None,
-        return_dict: bool = None,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
     ):
+        """Forward method for vision blip model"""
         if pixel_values is None:
             raise ValueError("You have to specify pixel_values")
         batch, _, time, _, _ = pixel_values.size()