diff --git a/inference_v2.py b/inference_v2.py
deleted file mode 100644
index 224af8a..0000000
--- a/inference_v2.py
+++ /dev/null
@@ -1,476 +0,0 @@
-import argparse
-import os
-
-import cv2
-import librosa
-import numpy as np
-import soundfile as sf
-from tqdm import tqdm
-
-from lib_v2 import dataset
-from lib_v2 import nets
-from lib_v2 import spec_utils
-
-import torch
-# Variable manipulation and command line text parsing
-from collections import defaultdict
-import tkinter as tk
-import time  # Timer
-import traceback  # Error Message Recent Calls
-
-
-class Namespace:
-    """
-    Replaces ArgumentParser
-    """
-
-    def __init__(self, **kwargs):
-        self.__dict__.update(kwargs)
-
-
-data = {
-    # Paths
-    'input_paths': None,
-    'export_path': None,
-    # Processing Options
-    'gpu': -1,
-    'postprocess': True,
-    'tta': True,
-    'output_image': True,
-    # Models
-    'instrumentalModel': None,
-    'vocalModel': None,
-    'stackModel': None,
-    'useModel': None,
-    # Stack Options
-    'stackPasses': 0,
-    'stackOnly': False,
-    'saveAllStacked': False,
-    # Model Folder
-    'modelFolder': False,
-    # Constants
-    'sr': 44_100,
-    'hop_length': 1_024,
-    'window_size': 320,
-    'n_fft': 2_048,
-}
-default_sr = data['sr']
-default_hop_length = data['hop_length']
-default_window_size = data['window_size']
-default_n_fft = data['n_fft']
-
-
-def update_progress(progress_var, total_files, total_loops, file_num, loop_num, step: float = 1):
-    """Calculate the progress for the progress widget in the GUI"""
-    base = (100 / total_files)
-    progress = base * (file_num - 1)
-    progress += (base / total_loops) * (loop_num + step)
-
-    progress_var.set(progress)
-
-
-def get_baseText(total_files, total_loops, file_num, loop_num):
-    """Create the base text for the command widget"""
-    text = 'File {file_num}/{total_files}:{loop} '.format(file_num=file_num,
-                                                          total_files=total_files,
-                                                          loop='' if total_loops <= 1 else f' ({loop_num+1}/{total_loops})')
-    return text
-
-
-def update_constants(model_name):
-    """
-    Decode the conversion settings from the model's name
-    """
-    global data
-    text = model_name.replace('.pth', '')
-    text_parts = text.split('_')[1:]
-
-    # First set everything to default ->
-    # If file name is not decodeable (invalid or no text_parts), constants stay at default
-    data['sr'] = default_sr
-    data['hop_length'] = default_hop_length
-    data['window_size'] = default_window_size
-    data['n_fft'] = default_n_fft
-
-    for text_part in text_parts:
-        if 'sr' in text_part:
-            text_part = text_part.replace('sr', '')
-            if text_part.isdecimal():
-                try:
-                    data['sr'] = int(text_part)
-                    continue
-                except ValueError:
-                    # Cannot convert string to int
-                    pass
-        if 'hl' in text_part:
-            text_part = text_part.replace('hl', '')
-            if text_part.isdecimal():
-                try:
-                    data['hop_length'] = int(text_part)
-                    continue
-                except ValueError:
-                    # Cannot convert string to int
-                    pass
-        if 'w' in text_part:
-            text_part = text_part.replace('w', '')
-            if text_part.isdecimal():
-                try:
-                    data['window_size'] = int(text_part)
-                    continue
-                except ValueError:
-                    # Cannot convert string to int
-                    pass
-        if 'nf' in text_part:
-            text_part = text_part.replace('nf', '')
-            if text_part.isdecimal():
-                try:
-                    data['n_fft'] = int(text_part)
-                    continue
-                except ValueError:
-                    # Cannot convert string to int
-                    pass
-
-
-def determineModelFolderName():
-    """
-    Determine the name that is used for the folder and appended
-    to the back of the music files
-    """
-    modelFolderName = ''
-    if not data['modelFolder']:
-        # Model Test Mode not selected
-        return modelFolderName
-
-    # -Instrumental-
-    if os.path.isfile(data['instrumentalModel']):
-        modelFolderName += os.path.splitext(os.path.basename(data['instrumentalModel']))[0]
-    # -Vocal-
-    elif os.path.isfile(data['vocalModel']):
-        modelFolderName += os.path.splitext(os.path.basename(data['vocalModel']))[0]
-    # -Stack-
-    if os.path.isfile(data['stackModel']):
-        modelFolderName += '-' + os.path.splitext(os.path.basename(data['stackModel']))[0]
-
-    return modelFolderName
-
-
-def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress_var: tk.Variable,
-         **kwargs: dict):
-    def load_models():
-        text_widget.write('Loading models...\n')  # nopep8 Write Command Text
-        models = defaultdict(lambda: None)
-        devices = defaultdict(lambda: None)
-
-        # -Instrumental-
-        if os.path.isfile(data['instrumentalModel']):
-            device = torch.device('cpu')
-            model = nets.CascadedASPPNet()
-            model.load_state_dict(torch.load(data['instrumentalModel'],
-                                             map_location=device))
-            if torch.cuda.is_available() and data['gpu'] >= 0:
-                device = torch.device('cuda:{}'.format(data['gpu']))
-                model.to(device)
-
-            models['instrumental'] = model
-            devices['instrumental'] = device
-        # -Vocal-
-        elif os.path.isfile(data['vocalModel']):
-            device = torch.device('cpu')
-            model = nets.CascadedASPPNet()
-            model.load_state_dict(torch.load(data['vocalModel'],
-                                             map_location=device))
-            if torch.cuda.is_available() and data['gpu'] >= 0:
-                device = torch.device('cuda:{}'.format(data['gpu']))
-                model.to(device)
-
-            models['vocal'] = model
-            devices['vocal'] = device
-        # -Stack-
-        if os.path.isfile(data['stackModel']):
-            device = torch.device('cpu')
-            model = nets.CascadedASPPNet()
-            model.load_state_dict(torch.load(data['stackModel'],
-                                             map_location=device))
-            if torch.cuda.is_available() and data['gpu'] >= 0:
-                device = torch.device('cuda:{}'.format(data['gpu']))
-                model.to(device)
-
-            models['stack'] = model
-            devices['stack'] = device
-
-        text_widget.write('Done!\n')
-        return models, devices
-
-    def load_wave_source():
-        X, sr = librosa.load(music_file,
-                             data['sr'],
-                             False,
-                             dtype=np.float32,
-                             res_type='kaiser_fast')
-
-        return X, sr
-
-    def stft_wave_source(X, model, device):
-        X = spec_utils.calc_spec(X, data['hop_length'])
-        X, phase = np.abs(X), np.exp(1.j * np.angle(X))
-        coeff = X.max()
-        X /= coeff
-
-        offset = model.offset
-        l, r, roi_size = dataset.make_padding(
-            X.shape[2], data['window_size'], offset)
-        X_pad = np.pad(X, ((0, 0), (0, 0), (l, r)), mode='constant')
-        X_roll = np.roll(X_pad, roi_size // 2, axis=2)
-
-        model.eval()
-        with torch.no_grad():
-            masks = []
-            masks_roll = []
-            length = int(np.ceil(X.shape[2] / roi_size))
-            for i in tqdm(range(length)):
-                update_progress(**progress_kwargs,
-                                step=0.1 + 0.5*(i/(length - 1)))
-                start = i * roi_size
-                X_window = torch.from_numpy(np.asarray([
-                    X_pad[:, :, start:start + data['window_size']],
-                    X_roll[:, :, start:start + data['window_size']]
-                ])).to(device)
-                pred = model.predict(X_window)
-                pred = pred.detach().cpu().numpy()
-                masks.append(pred[0])
-                masks_roll.append(pred[1])
-
-            mask = np.concatenate(masks, axis=2)[:, :, :X.shape[2]]
-            mask_roll = np.concatenate(masks_roll, axis=2)[
-                :, :, :X.shape[2]]
-            mask = (mask + np.roll(mask_roll, -roi_size // 2, axis=2)) / 2
-
-        if data['postprocess']:
-            vocal = X * (1 - mask) * coeff
-            mask = spec_utils.mask_uninformative(mask, vocal)
-
-        inst = X * mask * coeff
-        vocal = X * (1 - mask) * coeff
-
-        return inst, vocal, phase, mask
-
-    def invert_instrum_vocal(inst, vocal, phase):
-        wav_instrument = spec_utils.spec_to_wav(inst, phase, data['hop_length'])  # nopep8
-        wav_vocals = spec_utils.spec_to_wav(vocal, phase, data['hop_length'])  # nopep8
-
-        return wav_instrument, wav_vocals
-
-    def save_files(wav_instrument, wav_vocals):
-        """Save output music files"""
-        vocal_name = None
-        instrumental_name = None
-        save_path = os.path.dirname(base_name)
-
-        # Get the Suffix Name
-        if (not loop_num or
-                loop_num == (total_loops - 1)):  # First or Last Loop
-            if data['stackOnly']:
-                if loop_num == (total_loops - 1):  # Last Loop
-                    if not (total_loops - 1):  # Only 1 Loop
-                        vocal_name = '(Vocals)'
-                        instrumental_name = '(Instrumental)'
-                    else:
-                        vocal_name = '(Vocal_Final_Stacked_Output)'
-                        instrumental_name = '(Instrumental_Final_Stacked_Output)'
-            elif data['useModel'] == 'instrumental':
-                if not loop_num:  # First Loop
-                    vocal_name = '(Vocals)'
-                if loop_num == (total_loops - 1):  # Last Loop
-                    if not (total_loops - 1):  # Only 1 Loop
-                        instrumental_name = '(Instrumental)'
-                    else:
-                        instrumental_name = '(Instrumental_Final_Stacked_Output)'
-            elif data['useModel'] == 'vocal':
-                if not loop_num:  # First Loop
-                    instrumental_name = '(Instrumental)'
-                if loop_num == (total_loops - 1):  # Last Loop
-                    if not (total_loops - 1):  # Only 1 Loop
-                        vocal_name = '(Vocals)'
-                    else:
-                        vocal_name = '(Vocals_Final_Stacked_Output)'
-            if data['useModel'] == 'vocal':
-                # Reverse names
-                vocal_name, instrumental_name = instrumental_name, vocal_name
-        elif data['saveAllStacked']:
-            folder_name = os.path.basename(base_name) + ' Stacked Outputs'  # nopep8
-            save_path = os.path.join(save_path, folder_name)
-
-            if not os.path.isdir(save_path):
-                os.mkdir(save_path)
-
-            if data['stackOnly']:
-                vocal_name = f'(Vocal_{loop_num}_Stacked_Output)'
-                instrumental_name = f'(Instrumental_{loop_num}_Stacked_Output)'
-            elif (data['useModel'] == 'vocal' or
-                  data['useModel'] == 'instrumental'):
-                vocal_name = f'(Vocals_{loop_num}_Stacked_Output)'
-                instrumental_name = f'(Instrumental_{loop_num}_Stacked_Output)'
-
-            if data['useModel'] == 'vocal':
-                # Reverse names
-                vocal_name, instrumental_name = instrumental_name, vocal_name
-
-        # Save Temp File
-        # For instrumental the instrumental is the temp file
-        # and for vocal the instrumental is the temp file due
-        # to reversement
-        sf.write(f'temp.wav',
-                 wav_instrument.T, sr)
-
-        # -Save files-
-        # Instrumental
-        if instrumental_name is not None:
-            instrumental_path = os.path.join(save_path,
-                                             f'{os.path.basename(base_name)}_{instrumental_name}_{modelFolderName}.wav')
-            sf.write(instrumental_path,
-                     wav_instrument.T, sr)
-        # Vocal
-        if vocal_name is not None:
-            vocal_path = os.path.join(save_path,
-                                      f'{os.path.basename(base_name)}_{vocal_name}_{modelFolderName}.wav')
-            sf.write(vocal_path,
-                     wav_vocals.T, sr)
-
-    def output_image():
-        norm_mask = np.uint8((1 - mask) * 255).transpose(1, 2, 0)
-        norm_mask = np.concatenate([
-            np.max(norm_mask, axis=2, keepdims=True),
-            norm_mask], axis=2)[::-1]
-        _, bin_mask = cv2.imencode('.png', norm_mask)
-        text_widget.write(base_text + 'Saving Mask...\n')  # nopep8 Write Command Text
-        with open(f'{base_name}_(Mask).png', mode='wb') as f:
-            bin_mask.tofile(f)
-
-    data.update(kwargs)
-
-    # Update default settings
-    global default_sr
-    global default_hop_length
-    global default_window_size
-    global default_n_fft
-    default_sr = data['sr']
-    default_hop_length = data['hop_length']
-    default_window_size = data['window_size']
-    default_n_fft = data['n_fft']
-
-    stime = time.perf_counter()
-    progress_var.set(0)
-    text_widget.clear()
-    button_widget.configure(state=tk.DISABLED)  # Disable Button
-
-    models, devices = load_models()
-    modelFolderName = determineModelFolderName()
-    if modelFolderName:
-        folder_path = os.path.join(data["export_path"], modelFolderName)
-        if not os.path.isdir(folder_path):
-            os.mkdir(folder_path)
-
-    # Determine Loops
-    total_loops = data['stackPasses']
-    if not data['stackOnly']:
-        total_loops += 1
-
-    for file_num, music_file in enumerate(data['input_paths'], start=1):
-        try:
-            # Determine File Name
-            base_name = os.path.join(folder_path, f'{file_num}_{os.path.splitext(os.path.basename(music_file))[0]}')
-
-            for loop_num in range(total_loops):
-                # -Determine which model will be used-
-                if not loop_num:
-                    # First Iteration
-                    if data['stackOnly']:
-                        if os.path.isfile(data['stackModel']):
-                            model_name = os.path.basename(data['stackModel'])
-                            model = models['stack']
-                            device = devices['stack']
-                        else:
-                            raise ValueError(f'Selected stack only model, however, stack model path file cannot be found\nPath: "{data["stackModel"]}"')  # nopep8
-                    else:
-                        model_name = os.path.basename(data[f'{data["useModel"]}Model'])
-                        model = models[data['useModel']]
-                        device = devices[data['useModel']]
-                else:
-                    model_name = os.path.basename(data['stackModel'])
-                    # Every other iteration
-                    model = models['stack']
-                    device = devices['stack']
-                    # Reference new music file
-                    music_file = 'temp.wav'
-
-                # -Get text and update progress-
-                base_text = get_baseText(total_files=len(data['input_paths']),
-                                         total_loops=total_loops,
-                                         file_num=file_num,
-                                         loop_num=loop_num)
-                progress_kwargs = {'progress_var': progress_var,
-                                   'total_files': len(data['input_paths']),
-                                   'total_loops': total_loops,
-                                   'file_num': file_num,
-                                   'loop_num': loop_num}
-                update_progress(**progress_kwargs,
-                                step=0)
-                update_constants(model_name)
-
-                # -Go through the different steps of seperation-
-                # Wave source
-                text_widget.write(base_text + 'Loading wave source...\n')  # nopep8 Write Command Text
-                X, sr = load_wave_source()
-                text_widget.write(base_text + 'Done!\n')  # nopep8 Write Command Text
-
-                update_progress(**progress_kwargs,
-                                step=0.1)
-                # Stft of wave source
-                text_widget.write(base_text + 'Stft of wave source...\n')  # nopep8 Write Command Text
-                inst, vocal, phase, mask = stft_wave_source(X, model, device)
-                text_widget.write(base_text + 'Done!\n')  # nopep8 Write Command Text
-
-                update_progress(**progress_kwargs,
-                                step=0.6)
-                # Inverse stft
-                text_widget.write(base_text + 'Inverse stft of instruments and vocals...\n')  # nopep8 Write Command Text
-                wav_instrument, wav_vocals = invert_instrum_vocal(inst, vocal, phase)  # nopep8
-                text_widget.write(base_text + 'Done!\n')  # nopep8 Write Command Text
-
-                update_progress(**progress_kwargs,
-                                step=0.7)
-                # Save Files
-                text_widget.write(base_text + 'Saving Files...\n')  # nopep8 Write Command Text
-                save_files(wav_instrument, wav_vocals)
-                text_widget.write(base_text + 'Done!\n')  # nopep8 Write Command Text
-
-                update_progress(**progress_kwargs,
-                                step=0.8)
-
-            else:
-                # Save Output Image (Mask)
-                if data['output_image']:
-                    text_widget.write(base_text + 'Creating Mask...\n')  # nopep8 Write Command Text
-                    output_image()
-                    text_widget.write(base_text + 'Done!\n')  # nopep8 Write Command Text
-
-            text_widget.write(base_text + 'Completed Seperation!\n\n')  # nopep8 Write Command Text
-        except Exception as e:
-            traceback_text = ''.join(traceback.format_tb(e.__traceback__))
-            message = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\nFile: {music_file}\nLoop: {loop_num}\nPlease contact the creator and attach a screenshot of this error with the file and settings that caused it!'
-            tk.messagebox.showerror(master=window,
-                                    title='Untracked Error',
-                                    message=message)
-            print(traceback_text)
-            print(type(e).__name__, e)
-            print(message)
-            progress_var.set(0)
-            button_widget.configure(state=tk.NORMAL)  # Enable Button
-            return
-
-    os.remove('temp.wav')
-    progress_var.set(0)  # Update Progress
-    text_widget.write(f'Conversion(s) Completed and Saving all Files!\n')  # nopep8 Write Command Text
-    text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')  # nopep8
-    torch.cuda.empty_cache()
-    button_widget.configure(state=tk.NORMAL)  # Enable Button