GPT_subtitles
1.0.0
? GPT Subtitler is a web application inspired by this project, with many powerful features:
Supports high-quality subtitle translation using various models such as Anthropic Claude, GPT-3.5, and GPT-4. Currently, the Claude-Haiku model is recommended.
Additionally, the Gemini-1.5-flash and Gemini-1.5-pro models are available for free users to try, although they may not be as accurate as the Claude-Haiku model.
? New users receive 100,000 free tokens upon registration, enough to translate 20 minutes of video subtitles for free.
? Free tokens can be claimed daily, and tokens can also be purchased at low prices. No API key is required to use AI translation.
? Real-time preview of translation results, support for editing prompts, few-shot examples, and the ability to stop translation at any time and restart from any position. After translation, multiple SRT subtitle file formats can be exported (translation + original or original + translation bilingual subtitles).
The website is currently in the early development stage and needs your support and feedback! You are welcome to try it out and provide valuable suggestions.
If you encounter any bugs or have any suggestions during use, please feel free to raise an issue on the GitHub project or send feedback via email.
Website link https://gptsubtitler.com/en
Thanks for your support and for reading this far!
Here is a redeem code for 100,000 tokens: GPTSubtitler_github_repo
You can use it at Settings
下载 YouTube 视频（或提供您自己的视频）并使用 Whisper 和翻译API生成双语字幕，中文文档请见 中文
This project is a Python script that downloads a YouTube video (or uses a local video file), transcribes it, translates the transcript into a target language, and generates a video with dual subtitles (original and translated). The transcription and translation are powered by the Whisper model and the translation API (M2M100, google, GPT3.5), respectively.
GPT-3.5 translation compared to Google Translate
Arguments:
Additionally, when running the script for first time, it will download the following pre-trained models:
pip install -r requirements.txt
You can provide either a YouTube URL or a local video file for processing. The script will transcribe the video, translate the transcript, and generate dual subtitles in the form of an SRT file.
python main.py --youtube_url [YOUTUBE_URL] --target_language [TARGET_LANGUAGE] --model [WHISPER_MODEL] --translation_method [TRANSLATION_METHOD]
--youtube_url: The URL of the YouTube video.
--local_video: The path to the local video file.
--target_language: The target language for translation (default: 'zh').
--model: Choose one of the Whisper models (default: 'small', choices: ['tiny', 'base', 'small', 'medium', 'large']).
--translation_method: The method to use for translation. (default: 'google', choices: ['m2m100', 'google', 'whisper', 'gpt', 'no_translate']).
--no_transcribe: Skip the transcription step. Assume there is a SRT file with the same name as the video file
Note: You must provide either --youtube_url or --local_video, but not both.
To download a YouTube video, transcribe it, and generate subtitles in target language using the google api to translate:
python main.py --youtube_url [YOUTUBE_URL] --target_language 'zh' --model 'small' --translation_method 'google'
To process a local video file, transcribe it, and generate subtitles in target language using gpt3.5-16k (you will need to provide an OpenAI API key)):
python main.py --local_video [VIDEO_FILE_PATH] --target_language 'zh' --model 'medium' --translation_method 'gpt'
The script will generate the following output files in the same directory as the input video:
This script translates subtitles using OpenAI's GPT-3.5 language model. It requires an OpenAI API key to function. In most cases, GPT-based translation produce much better results compared to Google Translate, especially when dealing with context-specific translations or idiomatic expressions. This script aims to provide an alternative method for translating subtitles when traditional translation services like Google Translate do not produce satisfactory results.
OPENAI_API_KEY=your_api_key_here
Replace your_api_key_here with the API key you obtained from OpenAI.
python translate_gpt.py --input_file INPUT_FILE_PATH [--batch_size BATCH_SIZE] [--target_language TARGET_LANGUAGE] [--source_language SOURCE_LANGUAGE] [--video_info VIDEO_INFO] [--model MODEL_NAME] [--no_mapping] [--load_tmp_file]
You can check the response.log file in the folder containing the input video file for live updates, similar to the experience with ChatGPT.
Note:
Video Information: The --video_info argument accepts details in any language. It can be used to inform the GPT model about the video's content, improving the translation of context-specific terms, such as proper nouns within a game. For instance, if translating a video related to gaming, you might instruct GPT to use precise translations for in-game terminology.
Translation Mapping: This functionality maintains consistency for frequently used terms by storing source-target translation pairs. When enabled, it prevents variations in translating terms like proper nouns and technical jargon across the video. Disable this with the --no_mapping flag if preferred.
Resuming Translations: Use the --load_tmp_file flag to continue a translation task from where it was previously interrupted. The script saves progress in tmp_subtitles.json, allowing for a seamless resumption without redoing prior work.
Language Support: While the script excels with English-to-Simplified Chinese translations, it can accommodate other language pairs. Enhance the accuracy for additional languages by adding tailored few-shot examples to few_shot_examples.json. Note that the GPT models' performance may vary with multilingual inputs, and prompt adjustments in translate_gpt.py might be necessary.
Contributions are more than welcome!
You can also try out this script using a Google Colab notebook. Click the link below to access the example:
Follow the instructions in the notebook to download the necessary packages and models, and to run the script on your desired YouTube video or local video file.
