diff --git a/system/tts_diff/tts_diff.py b/system/tts_diff/tts_diff.py
index 55371dc..962e6fd 100644
--- a/system/tts_diff/tts_diff.py
+++ b/system/tts_diff/tts_diff.py
@@ -1,269 +1,241 @@
-import json
-import argparse
-from pathlib import Path
-import os
-import glob
-import platform
-import site
-import subprocess
-import sys
-# version 0.8
-os.environ['KMP_DUPLICATE_LIB_OK']='True'
-
-def find_files_in_path_with_wildcard(pattern):
-    # Get the site-packages directory of the current Python environment
-    site_packages_path = site.getsitepackages()
-    found_paths = []
-    # Adjust the sub-directory based on the operating system
-    sub_directory = "nvidia/cublas"
-    if platform.system() == "Linux":
-        sub_directory = os.path.join(sub_directory, "lib")
-    else:
-        sub_directory = os.path.join(sub_directory, "bin")
-    
-    # Iterate over each site-packages directory (there can be more than one)
-    for directory in site_packages_path:
-        # Construct the search directory path
-        search_directory = os.path.join(directory, sub_directory)
-        # Use glob to find all files matching the pattern in this directory
-        for file_path in glob.glob(os.path.join(search_directory, pattern)):
-            if os.path.isfile(file_path):  # Ensure it's a file
-                found_paths.append(file_path)
-    return found_paths
-
-def detect_cublas():
-    # Use different file patterns based on the operating system
-    file_name_pattern = 'cublas64_11.*' if platform.system() == "Windows" else 'libcublas.so.11*'
-    found_paths = find_files_in_path_with_wildcard(file_name_pattern)
-    if found_paths:
-        print("[AllTalk TTSDiff] \033[94mCublas:\033[0m \033[92mDetected\033[0m", found_paths)
-    else:
-        print("[AllTalk TTSDiff] \033[94mCublas:\033[0m \033[91mERROR Not Detected\033[0m")
-        print("[AllTalk TTSDiff] \033[94mPlease install Cublas from the Nvidia CUDA Toolkit \033[0mhttps://developer.nvidia.com/cuda-downloads")
-        exit(1)
-        
-
-parser = argparse.ArgumentParser(description="Compare TTS output with the original text using detailed comparison.")
-parser.add_argument("--threshold", type=int, default=98, help="Similarity threshold for considering a match (default: 98)")
-parser.add_argument("--ttslistpath", help="Path to the ttsList.json file")
-parser.add_argument("--wavfilespath", help="Path to the wav outputs folder")
-args = parser.parse_args()
-
-if not args.ttslistpath:
-    parser.error("[AllTalk TTSDiff] Please specify the path to the ttslist.json file using the --ttslistpath argument.")
-if not args.wavfilespath:
-    parser.error("[AllTalk TTSDiff] Please specify the path to the wavs output folder file using the --wavfilespath argument.")
-
-json_file_path = Path(args.ttslistpath).resolve()
-wav_file_path = Path(args.wavfilespath).resolve()
-accuracy_threshold = args.threshold
-
-def disclaimer_text():
-    print(f"\nDESCRIPTION: ")
-    print(f"  This script is designed to assist in identifying bad TTS generation ID's from the TTS Generator.\n")
-    print(f"  This will download the Whisper Large-v2 model (if not already downloaded), and use it to compare your text to")
-    print(f"  to the generated TTS. If it finds an issue, it will flag the ID number that has the issue. This does NOT 100%")
-    print(f"  ensure that all TTS generated is perfect, sounds good etc, but it should flag up glaring issues and help with")
-    print(f"  the process of identifying bad TTS generations.")
-    print(f"\nNOTES ON USE: ")
-    print(f"  - Ensure AllTalk is running in its own command prompt/terminal.")
-    print(f"  - This script should be running in its own command prompt/terminal window in the same Python Environment that")
-    print(f"    AllTalk is running in.")
-    print(f"  - After generating your text in the TTS Generator, you need to `Export List to JSON` and save the file in the")
-    print(f"    same folder/directory this script is running from.")
-    print(f"  - You can use `--threshold` with a numerical value from 1 to 100 to set accuracy detection. For example it may")
-    print(f"    detect/transcribe `their` as `there`, which of course, both words sound the same. If you want 100% accuracy")
-    print(f"    you will set 100 as the threshold. You will more than likely want a number in the high 90's. Default is set")
-    print(f"    at 98, which gives a little flexibility.")
-    print(f"  - The JSON file must be named \033[93mttsList.json\033[0m")
-    print(f"  - When you have your ID list, go back into the TTS Generator, correct any lines and regenerate them. If you")
-    print(f"    want to re-test everthing again after re-generating, you will need to export your list again and re-run this")
-    print(f"    script again, against your newly exported JSON list.")
-    print(f"  - This requires access to \033[93mcublas64_11\033[0m, the same as Finetuning.")
-    print(f"    https://github.com/erew123/alltalk_tts/tree/main?#-important-requirements-cuda-118\n")
-    return
-
-try:
-    from faster_whisper import WhisperModel
-    import re
-    import string
-    import spacy
-    import torch
-    from fuzzywuzzy import fuzz
-except ImportError as e:
-    print(f"[AllTalk TTSDiff] ERROR STARTING SCRIPT:")
-    print("[AllTalk TTSDiff] An error occurred importing one or more required libraries.")
-    print("[AllTalk TTSDiff] Please ensure you have \033[94mactivated your Python environment \033[0mand \033[94minstalled all requirements.\033[0m")
-    print("[AllTalk TTSDiff] \033[94mMissing library:\033[93m", str(e), "\033[0m")
-    exit(1)
-
-# Attempt to load the spaCy model
-try:
-    # Load a medium-sized spaCy model, adjust to "en_core_web_md" or "en_core_web_lg" as needed
-    nlp = spacy.load("en_core_web_md")
-except Exception as e:
-    print("[AllTalk TTSDiff] Failed to load the spaCy language model.")
-    
-    # Attempt to download the spaCy model automatically
-    try:
-        print("[AllTalk TTSDiff] Attempting to automatically download the spaCy language model...")
-        subprocess.run([sys.executable, "-m", "spacy", "download", "en_core_web_md"], check=True)
-        
-        # Try to load the model again after downloading
-        nlp = spacy.load("en_core_web_md")
-        print("[AllTalk TTSDiff] Model downloaded and loaded successfully.")
-    except Exception as download_exception:
-        print("[AllTalk TTSDiff] Automatic download failed.")
-        print("[AllTalk TTSDiff] Please manually install the spaCy language model by running:")
-        print("[AllTalk TTSDiff] \033[93mpython -m spacy download en_core_web_md\033[0m")
-        exit(1)
-
-def texts_are_similar(text1, text2, threshold=0.8):
-    # Preliminary check for short texts
-    if len(text1) < 10 or len(text2) < 10:
-        ratio = fuzz.ratio(text1, text2) / 100.0  # Use FuzzyWuzzy for short texts
-        return ratio > threshold
-
-    # Process the texts through the NLP model for longer texts
-    doc1 = nlp(text1)
-    doc2 = nlp(text2)
-    
-    # Compute semantic similarity
-    similarity = doc1.similarity(doc2)
-    
-    return similarity >= threshold
-
-def normalize_text(text):
-    # Normalize or remove CRLF and other non-standard whitespaces
-    text = text.replace("\r\n", " ").replace("\n", " ").replace("\r", " ")
-    
-    # Convert to lowercase
-    text = text.lower()
-    
-    # Standardize and then remove quotation marks
-    text = text.replace("“", '"').replace("”", '"').replace("‘", "'").replace("’", "'")
-    text = text.translate(str.maketrans('', '', '"\''))
-    
-    # Remove all other punctuation except hyphens to preserve compound words
-    text = text.translate(str.maketrans('', '', string.punctuation.replace("-", "")))
-    
-    # Collapse any sequence of whitespace (including spaces) into a single space
-    text = re.sub(r'\s+', ' ', text)
-    
-    return text.strip()
-
-spoken_punctuation_mapping = {
-    "dot": ".",
-    "comma": ",",
-    # Add more mappings as needed
-}
-
-def contains_spoken_punctuation(transcribed_text):
-    for spoken, punctuation in spoken_punctuation_mapping.items():
-        if spoken in transcribed_text:
-            return True
-    return False
-
-def detailed_comparison(original_text, transcribed_text, threshold=98):
-    original_clean = normalize_text(original_text)
-    transcribed_clean = normalize_text(transcribed_text)
-    ratio = fuzz.ratio(original_clean, transcribed_clean)
-    return ratio > threshold
-
-def access_local_file(url):
-    # Extract the file name from the URL
-    file_name = url.split('/')[-1]
-    # Construct the full path to the file on disk
-    file_path = wav_file_path / file_name
-    # Check if the file exists
-    if not file_path.exists():
-        print(f"[AllTalk TTSDiff] File does not exist: {file_path}")
-        return None
-    return file_path
-
-def transcribe_and_compare(file_url, original_text, model, item_id, flagged_ids):
-    audio_file_path = access_local_file(file_url)
-    if audio_file_path is None:
-        print(f"[AllTalk TTSDiff] Could not access local file for URL: {file_url}")
-        return  # Skip this file if it can't be accessed
-
-    try:
-        segments, info = model.transcribe(str(audio_file_path), beam_size=5)
-        transcribed_text = " ".join([segment.text for segment in segments])
-    except Exception as e:
-        print(f"[AllTalk TTSDiff] Error transcribing file {audio_file_path}: {e}")
-        return  # Skip this file if transcription fails
-    
-    segments, info = model.transcribe(str(audio_file_path), beam_size=5)
-    transcribed_text = " ".join([segment.text for segment in segments])
-    
-    # Normalize texts for comparison
-    original_text_normalized = normalize_text(original_text)
-    transcribed_text_normalized = normalize_text(transcribed_text)
-    
-    # Enhanced comparison using detailed fuzzy matching
-    is_detailed_match = detailed_comparison(original_text_normalized, transcribed_text_normalized, args.threshold)
-    has_spoken_punctuation = contains_spoken_punctuation(transcribed_text_normalized) and not contains_spoken_punctuation(original_text_normalized)
-    
-    # Adjust is_match based on detailed comparison and spoken punctuation check
-    is_match = is_detailed_match and not has_spoken_punctuation
-    
-    # Only log and flag IDs for review if there's a mismatch or detected issues
-    if not is_match:
-        #print(f"\033[93mMismatch Detected - ID:\033[0m {item_id}")
-        print(f"[AllTalk TTSDiff] \033[93mText:\033[0m {original_text}")
-        print(f"[AllTalk TTSDiff] \033[91mTTS :\033[0m{transcribed_text}")
-        if has_spoken_punctuation:
-            print(f"[AllTalk TTSDiff] Note: Potential incorrect spoken punctuation detected.")
-        flagged_ids.append(item_id)  # Track the ID for review
-
-def main():
-    print(f"[AllTalk TTSDiff]")
-    print(f"[AllTalk TTSDiff]")
-    print("[AllTalk TTSDiff] \033[92mStarting Compare of Text vs TTS:\033[0m")
-    print("[AllTalk TTSDiff]")
-    print("[AllTalk TTSDiff] \033[94mJSON file  :\033[92m", json_file_path, "\033[0m")
-    print("[AllTalk TTSDiff] \033[94mWAV files  :\033[92m", wav_file_path, "\033[0m")
-    print("[AllTalk TTSDiff] \033[94mAccuracy   :\033[92m", accuracy_threshold, "%", "\033[0m")
-    detect_cublas()
-    model_size = "large-v2"
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    model = WhisperModel(model_size, device=device, compute_type="float32")
-    
-    flagged_ids = []  # Initialize the list to track IDs needing review
-
-    try:
-        with open(json_file_path, "r", encoding="utf-8") as f:
-            tts_list = json.load(f)
-    except FileNotFoundError:
-        print(f"[AllTalk TTSDiff] ERROR STARTING SCRIPT:")
-        print("[AllTalk TTSDiff] Error: \033[93mttsList.json\033[0m file not found.")
-        print("[AllTalk TTSDiff] Please ensure you follow these steps:")
-        print("[AllTalk TTSDiff] After generating your text in the TTS Generator, you need to `Export List to JSON` and save the file in the")
-        print("[AllTalk TTSDiff] same folder/directory this script is running from.")
-        print("[AllTalk TTSDiff] The JSON file must be named \033[93mttsList.json\033[0m")
-        exit(1)
-
-    print(f"[AllTalk TTSDiff]")
-
-    for item in tts_list:
-        print(f"[AllTalk TTSDiff] Processing ID: {item['id']}")
-        transcribe_and_compare(item['fileUrl'], item['text'], model, item['id'], flagged_ids)
-    
-    print(f"[AllTalk TTSDiff]")
-    # Print summary information at the end
-    if flagged_ids:
-        print(f"[AllTalk TTSDiff] \033[94mIDs needing review:\033[0m", ', '.join(map(str, flagged_ids)))
-        print(f"[AllTalk TTSDiff]")
-        print(f"[AllTalk TTSDiff] Review ID's and correct any lines by editing & regenerating as necessary.")
-    else:
-        print("[AllTalk TTSDiff] \033[94mIDs needing review:\033[0m No issues detected.")
-    summary_data = {
-        "flagged_ids": flagged_ids
-    }
-    with open(wav_file_path / "analysis_summary.json", "w") as summary_file:
-        json.dump(summary_data, summary_file)
-    print("[AllTalk TTSDiff]")
-
-if __name__ == "__main__":
-    main()
+import json
+import argparse
+from pathlib import Path
+import os
+import sys
+import subprocess
+# version 0.9
+import warnings
+# Filter Flash Attention warning
+warnings.filterwarnings("ignore", message="1Torch was not compiled with flash attention")
+warnings.filterwarnings("ignore", message="Failed to launch Triton kernels, likely due to missing CUDA toolkit")
+# Try to import whisper, install if not found
+try:
+    import whisper
+except ImportError:
+    print("[AllTalk TTSDiff] OpenAI Whisper not found. Attempting to install...")
+    try:
+        subprocess.check_call([sys.executable, "-m", "pip", "install", "openai-whisper"])
+        import whisper
+        print("[AllTalk TTSDiff] Successfully installed OpenAI Whisper!")
+    except Exception as e:
+        print("[AllTalk TTSDiff] Failed to install OpenAI Whisper:")
+        print(f"[AllTalk TTSDiff] Error: {str(e)}")
+        print("[AllTalk TTSDiff] Please try manually installing with: pip install openai-whisper")
+        sys.exit(1)
+
+parser = argparse.ArgumentParser(description="Compare TTS output with the original text using detailed comparison.")
+parser.add_argument("--threshold", type=int, default=98, help="Similarity threshold for considering a match (default: 98)")
+parser.add_argument("--ttslistpath", help="Path to the ttsList.json file")
+parser.add_argument("--wavfilespath", help="Path to the wav outputs folder")
+args = parser.parse_args()
+
+if not args.ttslistpath:
+    parser.error("[AllTalk TTSDiff] Please specify the path to the ttslist.json file using the --ttslistpath argument.")
+if not args.wavfilespath:
+    parser.error("[AllTalk TTSDiff] Please specify the path to the wavs output folder file using the --wavfilespath argument.")
+
+json_file_path = Path(args.ttslistpath).resolve()
+wav_file_path = Path(args.wavfilespath).resolve()
+accuracy_threshold = args.threshold
+
+def disclaimer_text():
+    print(f"\nDESCRIPTION: ")
+    print(f"  This script is designed to assist in identifying bad TTS generation ID's from the TTS Generator.\n")
+    print(f"  This will download the Whisper Large-v3 model (if not already downloaded), and use it to compare your text to")
+    print(f"  to the generated TTS. If it finds an issue, it will flag the ID number that has the issue. This does NOT 100%")
+    print(f"  ensure that all TTS generated is perfect, sounds good etc, but it should flag up glaring issues and help with")
+    print(f"  the process of identifying bad TTS generations.")
+    print(f"\nNOTES ON USE: ")
+    print(f"  - Ensure AllTalk is running in its own command prompt/terminal.")
+    print(f"  - This script should be running in its own command prompt/terminal window in the same Python Environment that")
+    print(f"    AllTalk is running in.")
+    print(f"  - After generating your text in the TTS Generator, you need to `Export List to JSON` and save the file in the")
+    print(f"    same folder/directory this script is running from.")
+    print(f"  - You can use `--threshold` with a numerical value from 1 to 100 to set accuracy detection. For example it may")
+    print(f"    detect/transcribe `their` as `there`, which of course, both words sound the same. If you want 100% accuracy")
+    print(f"    you will set 100 as the threshold. You will more than likely want a number in the high 90's. Default is set")
+    print(f"    at 98, which gives a little flexibility.")
+    print(f"  - The JSON file must be named \033[93mttsList.json\033[0m")
+    print(f"  - When you have your ID list, go back into the TTS Generator, correct any lines and regenerate them. If you")
+    print(f"    want to re-test everthing again after re-generating, you will need to export your list again and re-run this")
+    print(f"    script again, against your newly exported JSON list.\n")
+    return
+
+try:
+    import re
+    import string
+    import spacy
+    import torch
+    from fuzzywuzzy import fuzz
+except ImportError as e:
+    print(f"[AllTalk TTSDiff] ERROR STARTING SCRIPT:")
+    print("[AllTalk TTSDiff] An error occurred importing one or more required libraries.")
+    print("[AllTalk TTSDiff] Please ensure you have \033[94mactivated your Python environment \033[0mand \033[94minstalled all requirements.\033[0m")
+    print("[AllTalk TTSDiff] \033[94mMissing library:\033[93m", str(e), "\033[0m")
+    exit(1)
+
+# Attempt to load the spaCy model
+try:
+    # Load a medium-sized spaCy model, adjust to "en_core_web_md" or "en_core_web_lg" as needed
+    nlp = spacy.load("en_core_web_md")
+except Exception as e:
+    try:
+        print("[AllTalk TTSDiff] SpaCy language model not installed.")            
+        print("[AllTalk TTSDiff] Attempting to automatically download the spaCy language model.")
+        subprocess.run([sys.executable, "-m", "spacy", "download", "en_core_web_md"], check=True)    
+        # Try to load the model again after downloading
+        nlp = spacy.load("en_core_web_md")
+        print("[AllTalk TTSDiff] Model downloaded and loaded successfully.")
+    except Exception as download_exception:
+        print("[AllTalk TTSDiff] Automatic download failed.")
+        print("[AllTalk TTSDiff] Please manually install the spaCy language model by running:")
+        print("[AllTalk TTSDiff] \033[93mpython -m spacy download en_core_web_md\033[0m")
+        exit(1)
+
+def texts_are_similar(text1, text2, threshold=0.8):
+    # Preliminary check for short texts
+    if len(text1) < 10 or len(text2) < 10:
+        ratio = fuzz.ratio(text1, text2) / 100.0  # Use FuzzyWuzzy for short texts
+        return ratio > threshold
+
+    # Process the texts through the NLP model for longer texts
+    doc1 = nlp(text1)
+    doc2 = nlp(text2)
+    
+    # Compute semantic similarity
+    similarity = doc1.similarity(doc2)
+    
+    return similarity >= threshold
+
+def normalize_text(text):
+    # Normalize or remove CRLF and other non-standard whitespaces
+    text = text.replace("\r\n", " ").replace("\n", " ").replace("\r", " ")
+    
+    # Convert to lowercase
+    text = text.lower()
+    
+    # Standardize and then remove quotation marks
+    text = text.replace(""", '"').replace(""", '"').replace("'", "'").replace("'", "'")
+    text = text.translate(str.maketrans('', '', '"\''))
+    
+    # Remove all other punctuation except hyphens to preserve compound words
+    text = text.translate(str.maketrans('', '', string.punctuation.replace("-", "")))
+    
+    # Collapse any sequence of whitespace (including spaces) into a single space
+    text = re.sub(r'\s+', ' ', text)
+    
+    return text.strip()
+
+spoken_punctuation_mapping = {
+    "dot": ".",
+    "comma": ",",
+    # Add more mappings as needed
+}
+
+def contains_spoken_punctuation(transcribed_text):
+    for spoken, punctuation in spoken_punctuation_mapping.items():
+        if spoken in transcribed_text:
+            return True
+    return False
+
+def detailed_comparison(original_text, transcribed_text, threshold=98):
+    original_clean = normalize_text(original_text)
+    transcribed_clean = normalize_text(transcribed_text)
+    ratio = fuzz.ratio(original_clean, transcribed_clean)
+    return ratio > threshold
+
+def access_local_file(url):
+    # Extract the file name from the URL
+    file_name = url.split('/')[-1]
+    # Construct the full path to the file on disk
+    file_path = wav_file_path / file_name
+    # Check if the file exists
+    if not file_path.exists():
+        print(f"[AllTalk TTSDiff] File does not exist: {file_path}")
+        return None
+    return file_path
+
+def transcribe_and_compare(file_url, original_text, model, item_id, flagged_ids):
+    audio_file_path = access_local_file(file_url)
+    if audio_file_path is None:
+        print(f"[AllTalk TTSDiff] Could not access local file for URL: {file_url}")
+        return  # Skip this file if it can't be accessed
+
+    try:
+        result = model.transcribe(str(audio_file_path))
+        transcribed_text = result["text"]
+    except Exception as e:
+        print(f"[AllTalk TTSDiff] Error transcribing file {audio_file_path}: {e}")
+        return  # Skip this file if transcription fails
+    
+    # Normalize texts for comparison
+    original_text_normalized = normalize_text(original_text)
+    transcribed_text_normalized = normalize_text(transcribed_text)
+    
+    # Enhanced comparison using detailed fuzzy matching
+    is_detailed_match = detailed_comparison(original_text_normalized, transcribed_text_normalized, args.threshold)
+    has_spoken_punctuation = contains_spoken_punctuation(transcribed_text_normalized) and not contains_spoken_punctuation(original_text_normalized)
+    
+    # Adjust is_match based on detailed comparison and spoken punctuation check
+    is_match = is_detailed_match and not has_spoken_punctuation
+    
+    # Only log and flag IDs for review if there's a mismatch or detected issues
+    if not is_match:
+        print(f"[AllTalk TTSDiff] \033[93mText:\033[0m {original_text}")
+        print(f"[AllTalk TTSDiff] \033[91mTTS :\033[0m{transcribed_text}")
+        if has_spoken_punctuation:
+            print(f"[AllTalk TTSDiff] Note: Potential incorrect spoken punctuation detected.")
+        flagged_ids.append(item_id)  # Track the ID for review
+
+def main():
+    print(f"[AllTalk TTSDiff]")
+    print(f"[AllTalk TTSDiff]")
+    print("[AllTalk TTSDiff] \033[92mStarting Compare of Text vs TTS:\033[0m")
+    print("[AllTalk TTSDiff]")
+    print("[AllTalk TTSDiff] \033[94mJSON file  :\033[92m", json_file_path, "\033[0m")
+    print("[AllTalk TTSDiff] \033[94mWAV files  :\033[92m", wav_file_path, "\033[0m")
+    print("[AllTalk TTSDiff] \033[94mAccuracy   :\033[92m", accuracy_threshold, "%", "\033[0m")
+    
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    print("[AllTalk TTSDiff] \033[94mDevice     :\033[92m", device, "\033[0m")
+    print("[AllTalk TTSDiff] Loading Whisper Large-v3 model...")
+    model = whisper.load_model("large-v3", device=device)
+    
+    flagged_ids = []  # Initialize the list to track IDs needing review
+
+    try:
+        with open(json_file_path, "r", encoding="utf-8") as f:
+            tts_list = json.load(f)
+    except FileNotFoundError:
+        print(f"[AllTalk TTSDiff] ERROR STARTING SCRIPT:")
+        print("[AllTalk TTSDiff] Error: \033[93mttsList.json\033[0m file not found.")
+        print("[AllTalk TTSDiff] Please ensure you follow these steps:")
+        print("[AllTalk TTSDiff] After generating your text in the TTS Generator, you need to `Export List to JSON` and save the file in the")
+        print("[AllTalk TTSDiff] same folder/directory this script is running from.")
+        print("[AllTalk TTSDiff] The JSON file must be named \033[93mttsList.json\033[0m")
+        exit(1)
+
+    print(f"[AllTalk TTSDiff]")
+
+    for item in tts_list:
+        print(f"[AllTalk TTSDiff] Processing ID: {item['id']}")
+        transcribe_and_compare(item['fileUrl'], item['text'], model, item['id'], flagged_ids)
+    
+    print(f"[AllTalk TTSDiff]")
+    # Print summary information at the end
+    if flagged_ids:
+        print(f"[AllTalk TTSDiff] \033[94mIDs needing review:\033[0m", ', '.join(map(str, flagged_ids)))
+        print(f"[AllTalk TTSDiff]")
+        print(f"[AllTalk TTSDiff] Review ID's and correct any lines by editing & regenerating as necessary.")
+    else:
+        print("[AllTalk TTSDiff] \033[94mIDs needing review:\033[0m No issues detected.")
+    summary_data = {
+        "flagged_ids": flagged_ids
+    }
+    with open(wav_file_path / "analysis_summary.json", "w") as summary_file:
+        json.dump(summary_data, summary_file)
+    print("[AllTalk TTSDiff]")
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file