diff --git a/requirements.txt b/requirements.txt
index 08920b1..6d0a036 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -47,6 +47,7 @@ SoundFile==0.10.3.post1
 subprocess32==3.5.4
 threadpoolctl==2.1.0
 tokenizers==0.10.2
+--find-links https://download.pytorch.org/whl/torch_stable.html
 torch==1.8.1+cu111
 torchaudio==0.8.1
 torchlibrosa==0.0.9
diff --git a/src/CLAPWrapper.py b/src/CLAPWrapper.py
index 6900c3d..d69d3bf 100644
--- a/src/CLAPWrapper.py
+++ b/src/CLAPWrapper.py
@@ -6,8 +6,8 @@ import re
 import torch.nn.functional as F
 import numpy as np
 from transformers import AutoTokenizer
-from .models.utils import read_config_as_args
-from .models.clap import CLAP
+from models.utils import read_config_as_args
+from models.clap import CLAP
 import math
 import torchaudio.transforms as T
 import os
@@ -26,7 +26,7 @@ class CLAPWrapper():
         self.default_collate_err_msg_format = (
             "default_collate: batch must contain tensors, numpy arrays, numbers, "
             "dicts or lists; found {}")
-        self.config_as_str = files('CLAP_API.configs').joinpath('config.yml').read_text()
+        self.config_as_str = files('configs').joinpath('config.yml').read_text()
         self.model_fp = model_fp
         self.use_cuda = use_cuda
         self.clap, self.tokenizer, self.args = self.load_clap()
diff --git a/src/__init__.py b/src/__init__.py
index bd3b45b..e69de29 100644
--- a/src/__init__.py
+++ b/src/__init__.py
@@ -1 +0,0 @@
-from .CLAPWrapper import CLAPWrapper as CLAP
\ No newline at end of file
diff --git a/src/examples/esc50_dataset.py b/src/esc50_dataset.py
similarity index 100%
rename from src/examples/esc50_dataset.py
rename to src/esc50_dataset.py
diff --git a/src/examples/zero_shot_classification.py b/src/zero_shot_classification.py
similarity index 82%
rename from src/examples/zero_shot_classification.py
rename to src/zero_shot_classification.py
index 130a1ef..098184d 100644
--- a/src/examples/zero_shot_classification.py
+++ b/src/zero_shot_classification.py
@@ -3,7 +3,7 @@ This is an example using CLAP to perform zeroshot
     classification on ESC50 (https://github.com/karolpiczak/ESC-50).
 """
 
-from src.CLAPWrapper import CLAP
+from CLAPWrapper import CLAPWrapper
 from esc50_dataset import ESC50
 import torch.nn.functional as F
 import numpy as np
@@ -11,14 +11,14 @@ from tqdm import tqdm
 from sklearn.metrics import accuracy_score
 
 # Load dataset
-dataset = ESC50(root='data', download=False)
+dataset = ESC50(root="C:\\Users\\benjaminm\\Datasets", download=False)
 prompt = 'this is a sound of '
 y = [prompt + x for x in dataset.classes]
 
 
 # Load and initialize CLAP
-weights_path = '<insert your weights file path>'
-clap_model = CLAP(weights_path, use_cuda=False)
+weights_path = "C:\\Users\\benjaminm\\OneDrive - Microsoft\\CLAP_shared\\CLAP_models\\best.pth"
+clap_model = CLAPWrapper(weights_path, use_cuda=False)
 
 
 # Computing text embeddings
diff --git a/src/examples/zero_shot_predictions.py b/src/zero_shot_predictions.py
similarity index 81%
rename from src/examples/zero_shot_predictions.py
rename to src/zero_shot_predictions.py
index 4333f51..e8297b5 100644
--- a/src/examples/zero_shot_predictions.py
+++ b/src/zero_shot_predictions.py
@@ -3,22 +3,22 @@ This is an example using CLAP for zero-shot
         inference using ESC50 (https://github.com/karolpiczak/ESC-50).
 """
 
-from src.CLAPWrapper import CLAP
+from CLAPWrapper import CLAPWrapper
 from esc50_dataset import ESC50
 import torch.nn.functional as F
 
 # Load ESC50 dataset
-dataset = ESC50(root='data', download=True) # set download=True when dataset is not downloaded
+dataset = ESC50(root="C:\\Users\\benjaminm\\Datasets", download=True) # set download=True when dataset is not downloaded
 audio_file, target, one_hot_target = dataset[1000]
 audio_file = [audio_file]
 prompt = 'this is a sound of '
 y = [prompt + x for x in dataset.classes]
 
 # Load and initialize CLAP
-weights_path = '<insert your weights file path>'
+weights_path = "C:\\Users\\benjaminm\\OneDrive - Microsoft\\CLAP_shared\\CLAP_models\\best.pth"
 
 # Setting use_cuda = True will load the model on a GPU using CUDA
-clap_model = CLAP(weights_path, use_cuda=False)
+clap_model = CLAPWrapper(weights_path, use_cuda=False)
 
 # compute text embeddings from natural text 
 text_embeddings = clap_model.get_text_embeddings(y)