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