erew123
7e6e18c6d9
A BETA test/Proof of concept script to compare Speech that was generated in TTS Generator to the original Text and flag issues. Anyone reading this, you are welcome to try it, the readme instructions are in the folder, however, I am not providing support on this while it is BETA/Proof of concept.
172 lines
7.7 KiB
Python
172 lines
7.7 KiB
Python
import json
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import requests
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from pathlib import Path
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# version 0.1
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def disclaimer_text():
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print(f"\nDISCLAIMER:")
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print(f" \033[92mThis is a work in progress, aka a BETA test at best. It is not refined or production ready,")
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print(f" just a proof of concept and should therefore be viewed and used as such.")
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print(f"\n There is no support on this while its in BETA.\033[0m")
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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" - 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")
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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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disclaimer_text()
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print(f"\nERROR STARTING SCRIPT:")
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print(" An error occurred importing one or more required libraries.")
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print(" Please ensure you have \033[94mactivated your Python environment \033[0mand \033[94minstalled all requirements.\033[0m")
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print("\n \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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disclaimer_text()
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print(f"\nERROR STARTING SCRIPT:")
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print(" Failed to load the spaCy language model.")
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print(" Please ensure the spaCy language model is installed by running:")
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print(" \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 download_file(url, destination):
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response = requests.get(url)
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response.raise_for_status()
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with open(destination, 'wb') as f:
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f.write(response.content)
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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 transcribe_and_compare(file_url, original_text, model, item_id, flagged_ids):
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audio_file = Path(file_url.split('/')[-1])
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download_file(file_url, audio_file)
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segments, info = model.transcribe(str(audio_file), beam_size=5)
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transcribed_text = " ".join([segment.text for segment in segments])
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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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is_match = texts_are_similar(original_text_normalized, transcribed_text_normalized)
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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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if has_spoken_punctuation:
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is_match = False # Consider as mismatch if unexpected spoken punctuation is detected
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if not is_match or has_spoken_punctuation:
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print(f"\033[93mOriginal:\033[0m {original_text}")
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print(f"\033[91mTranscribed:\033[0m {transcribed_text}")
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print(f"Match: {is_match}\n")
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if has_spoken_punctuation:
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print(f"Note: Potential incorrect spoken punctuation detected in ID: {item_id}.")
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flagged_ids.append(item_id) # Track the ID for review
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audio_file.unlink() # Remove the downloaded file after processing
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def main():
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model_size = "large-v3"
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device = "cuda" if torch.cuda.is_available() else "cpu"
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disclaimer_text()
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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("ttsList.json", "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"\nERROR STARTING SCRIPT:")
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print(" Error: \033[93mttsList.json\033[0m file not found.")
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print("\n Please ensure you follow these steps:")
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print(" - 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(" same folder/directory this script is running from.")
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print(" - The JSON file must be named \033[93mttsList.json\033[0m")
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exit(1)
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print(f"\nPROCESSING LIST:\n")
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for item in tts_list:
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print(f"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 summary information at the end
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if flagged_ids:
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print(f" For ID's showm, in the TTS Generator, review and correct any lines by editing & regenerating them. If you")
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print(f" want to re-test everthing again after re-generating, you will need to export the JSON list again and re-run")
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print(f" the script again, against the newly exported JSON list.")
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print("\nSUMMARY: IDs needing review:", ', '.join(map(str, flagged_ids)))
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else:
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print("\nSUMMARY: No issues detected.")
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if __name__ == "__main__":
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main() |