bilingual_book_maker

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Make bilingual epub books Using AI translate

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Repository: yihong0618/bilingual_book_maker


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README.md

δΈ­ζ–‡ | English
![litellm](https://github.com/BerriAI/litellm)

bilingual_book_maker

The bilingual_book_maker is an AI translation tool that uses ChatGPT to assist users in creating multi-language versions of epub/txt/srt/pdf files and books. This tool is exclusively designed for translating epub and other public domain works and is not intended for copyrighted works. Before using this tool, please review the project's disclaimer.

!image

Supported Models

gpt-5-mini, gpt-4, gpt-3.5-turbo, claude-2, palm, llama-2, azure-openai, command-nightly, gemini, qwen-mt-turbo, qwen-mt-plus
For using Non-OpenAI models, use class liteLLM() - liteLLM supports all models above.
Find more info here for using liteLLM: https://github.com/BerriAI/litellm/blob/main/setup.py

Preparation

1. ChatGPT or OpenAI token [^token]
2. epub/txt/pdf books
3. Environment with internet access or proxy
4. Python 3.8+

Quick Start

A sample book, test_books/animal_farm.epub, is provided for testing purposes.

shell
pip install -r requirements.txt
python3 make_book.py --book_name test_books/animal_farm.epub --openai_key ${openai_key} --test
OR
pip install -U bbook_maker
bbook --book_name test_books/animal_farm.epub --openai_key ${openai_key} --test

Translate Service

- Use --openai_key option to specify OpenAI API key. If you have multiple keys, separate them by commas (xxx,xxx,xxx) to reduce errors caused by API call limits.
Or, just set environment variable BBM_OPENAI_API_KEY instead.
- A sample book, test_books/animal_farm.epub, is provided for testing purposes.
- The default underlying model is GPT-3.5-turbo, which is used by ChatGPT currently. Use --model gpt4 to change the underlying model to GPT4. You can also use GPT4omini.
- Important to note that gpt-4 is significantly more expensive than gpt-4-turbo, but to avoid bumping into rate limits, we automatically balance queries across gpt-4-1106-preview, gpt-4, gpt-4-32k, gpt-4-0613,gpt-4-32k-0613.
- If you want to use a specific model alias with OpenAI (eg gpt-4-1106-preview or gpt-3.5-turbo-0125), you can use --model openai --model_list gpt-4-1106-preview,gpt-3.5-turbo-0125. --model_list takes a comma-separated list of model aliases.
- If using chatgptapi, you can add --use_context to add a context paragraph to each passage sent to the model for translation (see below).

* DeepL
Support DeepL model DeepL Translator need pay to get the token

text
python3 make_book.py --book_name test_books/animal_farm.epub --model deepl --deepl_key ${deepl_key}

* DeepL free

shell
python3 make_book.py --book_name test_books/animal_farm.epub --model deeplfree

* Claude

Use Claude model to translate

shell
python3 make_book.py --book_name test_books/animal_farm.epub --model claude --claude_key ${claude_key}

* Google Translate

shell
python3 make_book.py --book_name test_books/animal_farm.epub --model google

* Caiyun Translate

shell
python3 make_book.py --book_name test_books/animal_farm.epub --model caiyun --caiyun_key ${caiyun_key}

* Gemini

Support Google Gemini model, use --model gemini for Gemini Flash or --model geminipro for Gemini Pro.
If you want to use a specific model alias with Gemini (eg gemini-1.5-flash-002 or gemini-1.5-flash-8b-exp-0924), you can use --model gemini --model_list gemini-1.5-flash-002,gemini-1.5-flash-8b-exp-0924. --model_list takes a comma-separated list of model aliases.

shell
python3 make_book.py --book_name test_books/animal_farm.epub --model gemini --gemini_key ${gemini_key}

* Qwen

Support Alibaba Cloud Qwen-MT specialized translation model. Supports 92 languages with features like terminology intervention and translation memory.
Use --model qwen-mt-turbo for faster/cheaper translation, or --model qwen-mt-plus for higher quality.

Use source_lang to specify the source language explicitly, or leave it empty for auto-detection.

shell
python3 make_book.py --book_name test_books/animal_farm.epub --qwen_key ${qwen_key} --model qwen-mt-turbo --language "Simplified Chinese"
  python3 make_book.py --book_name test_books/animal_farm.epub --qwen_key ${qwen_key} --model qwen-mt-plus --language "Japanese" --source_lang "English"

* Tencent TranSmart

shell
python3 make_book.py --book_name test_books/animal_farm.epub --model tencentransmart

* xAI

shell
python3 make_book.py --book_name test_books/animal_farm.epub --model xai --xai_key ${xai_key}

* Ollama

Support Ollama self-host models,
If ollama server is not running on localhost, use --api_base http://x.x.x.x:port/v1 to point to the ollama server address

shell
python3 make_book.py --book_name test_books/animal_farm.epub --ollama_model ${ollama_model_name}

* groq

GroqCloud currently supports models: you can find from Supported Models

shell
python3 make_book.py --book_name test_books/animal_farm.epub --groq_key [your_key] --model groq --model_list llama3-8b-8192

Use

- Once the translation is complete, a bilingual book named ${book_name}_bilingual.epub would be generated for EPUB inputs; for TXT/MD/SRT inputs a bilingual text (or subtitle) file named ${book_name}_bilingual.txt (or _bilingual.srt) will be generated. For PDF inputs the tool will produce a bilingual .txt fallback and will also attempt to create ${book_name}_bilingual.epub β€” if EPUB creation fails, the TXT fallback remains so you do not need to retranslate.
- If there are any errors or you wish to interrupt the translation by pressing CTRL+C, a temporary bilingual file (for example {book_name}_bilingual_temp.epub or {book_name}_bilingual_temp.txt) would be generated. You can simply rename it to any desired name.

Params

- --test:

Use --test option to preview the result if you haven't paid for the service. Note that there is a limit and it may take some time.

- --language:

Set the target language like --language "Simplified Chinese". Default target language is "Simplified Chinese".
Read available languages by helper message: python make_book.py --help

- --proxy:

Use --proxy option to specify proxy server for internet access. Enter a string such as http://127.0.0.1:7890.

- --resume:

Use --resume option to manually resume the process after an interruption.

shell
python3 make_book.py --book_name test_books/animal_farm.epub --model google --resume

- --translate-tags:

epub is made of html files. By default, we only translate contents in <p>.
Use --translate-tags to specify tags need for translation. Use comma to separate multiple tags.
For example: --translate-tags h1,h2,h3,p,div

- --exclude-translate-tags:

Use --exclude-translate-tags to exclude content within specified HTML tags from translation. This is useful for preserving code blocks, preformatted text, or other special content. Use comma to separate multiple tags.
Default: sup,code.
For example: --exclude-translate-tags code,pre

Tip: Use --exclude-translate-tags "" to translate all content including code blocks (overrides the default exclusion).

- --book_from:

Use --book_from option to specify e-reader type (Now only kobo is available), and use --device_path to specify the mounting point.

- --api_base:

If you want to change api_base like using Cloudflare Workers, use --api_base <URL> to support it.
Note: the api url should be 'https://xxxx/v1'. Quotation marks are required.

- --allow_navigable_strings:

If you want to translate strings in an e-book that aren't labeled with any tags, you can use the --allow_navigable_strings parameter. This will add the strings to the translation queue. Note that it's best to look for e-books that are more standardized if possible.

- --prompt:

To tweak the prompt, use the --prompt parameter. Valid placeholders for the user role template include {text} and {language}. It supports a few ways to configure the prompt:

- If you don't need to set the system role content, you can simply set it up like this: --prompt "Translate {text} to {language}." or --prompt prompt_template_sample.txt (example of a text file can be found at ./prompt_template_sample.txt).

- If you need to set the system role content, you can use the following format: --prompt '{"user":"Translate {text} to {language}", "system": "You are a professional translator."}' or --prompt prompt_template_sample.json (example of a JSON file can be found at ./prompt_template_sample.json).

- You can now use PromptDown format (.md files) for more structured prompts: --prompt prompt_md.prompt.md. PromptDown supports both traditional system messages and developer messages (used by newer AI models). Example:

markdown
# Translation Prompt
      
      ## Developer Message
      You are a professional translator who specializes in accurate translations.
      
      ## Conversation
      
      | Role | Content                                                        |
      | ---- | -------------------------------------------------------------- |
      | User | Please translate the following text into {language}:\n\n{text} |

- You can also set the user and system role prompt by setting environment variables: BBM_CHATGPTAPI_USER_MSG_TEMPLATE and BBM_CHATGPTAPI_SYS_MSG.

- --batch_size:

Use the --batch_size parameter to specify the number of lines for batch translation (default is 10, currently only effective for txt files).

- --accumulated_num:

Wait for how many tokens have been accumulated before starting the translation. gpt3.5 limits the total_token to 4090. For example, if you use --accumulated_num 1600, maybe openai will output 2200 tokens and maybe 200 tokens for other messages in the system messages user messages, 1600+2200+200=4000, So you are close to reaching the limit. You have to choose your own
value, there is no way to know if the limit is reached before sending

- --use_context:

prompts the model to create a three-paragraph summary. If it's the beginning of the translation, it will summarize the entire passage sent (the size depending on --accumulated_num).
For subsequent passages, it will amend the summary to include details from the most recent passage, creating a running one-paragraph context payload of the important details of the entire translated work. This improves consistency of flow and tone throughout the translation. This option is available for all ChatGPT-compatible models and Gemini models.

- --context_paragraph_limit:

Use --context_paragraph_limit to set a limit on the number of context paragraphs when using the --use_context option.

- --parallel-workers:

Use --parallel-workers to enable parallel EPUB chapter processing. Values greater than 1 spin up multiple workers (recommended: 2-4) and automatically fall back to sequential mode for single-chapter books.

- --temperature:

Use --temperature to set the temperature parameter for chatgptapi/gpt4/claude models.
For example: --temperature 0.7.

- --block_size:

Use --block_size to merge multiple paragraphs into one block. This may increase accuracy and speed up the process.
For example: --block_size 5.

- --single_translate:

Use --single_translate to output only the translated book without creating a bilingual version.

- --translation_style:

example: --translation_style "color: #808080; font-style: italic;"

- --retranslate "$translated_filepath" "file_name_in_epub" "start_str" "end_str"(optional):

Retranslate from start_str to end_str's tag:

shell
python3 "make_book.py" --book_name "test_books/animal_farm.epub" --retranslate 'test_books/animal_farm_bilingual.epub' 'index_split_002.html' 'in spite of the present book shortage which' 'This kind of thing is not a good symptom. Obviously'

Retranslate start_str's tag:

shell
python3 "make_book.py" --book_name "test_books/animal_farm.epub" --retranslate 'test_books/animal_farm_bilingual.epub' 'index_split_002.html' 'in spite of the present book shortage which'

- --extra_body:

Pass additional JSON parameters to the API. This is useful for models that support extra configuration options. Provide a JSON string with the desired parameters.

shell
python3 make_book.py --book_name test_books/animal_farm.epub --openai_key ${openai_key} --extra_body '{"chat_template_kwargs": {"enable_thinking": false}}'

Examples

Note if use pip install bbook_maker all commands can change to bbook_maker args

shell
Test quickly

python3 make_book.py --book_name test_books/animal_farm.epub --openai_key ${openai_key}  --test --language zh-hans
Test quickly for src

python3 make_book.py --book_name test_books/Lex_Fridman_episode_322.srt --openai_key ${openai_key}  --test
Or translate the whole book

python3 make_book.py --book_name test_books/animal_farm.epub --openai_key ${openai_key} --language zh-hans
Or translate the whole book using Gemini flash

python3 make_book.py --book_name test_books/animal_farm.epub --gemini_key ${gemini_key} --model gemini
Translate an EPUB with parallel chapter processing

python3 make_book.py --book_name test_books/animal_farm.epub --openai_key ${openai_key} --parallel-workers 4
Use a specific list of Gemini model aliases

python3 make_book.py --book_name test_books/animal_farm.epub --gemini_key ${gemini_key} --model gemini --model_list gemini-1.5-flash-002,gemini-1.5-flash-8b-exp-0924
Set env OPENAI_API_KEY to ignore option --openai_key

export OPENAI_API_KEY=${your_api_key}
Use the GPT-4 model with context to Japanese

python3 make_book.py --book_name test_books/animal_farm.epub --model gpt4 --use_context --language ja
Use a specific OpenAI model alias

python3 make_book.py --book_name test_books/animal_farm.epub --model openai --model_list gpt-4-1106-preview --openai_key ${openai_key}
Note you can use other openai like model in this way
python3 make_book.py --book_name test_books/animal_farm.epub --model openai --model_list yi-34b-chat-0205 --openai_key ${openai_key} --api_base "https://api.lingyiwanwu.com/v1"
Use a specific list of OpenAI model aliases

python3 make_book.py --book_name test_books/animal_farm.epub --model openai --model_list gpt-4-1106-preview,gpt-4-0125-preview,gpt-3.5-turbo-0125 --openai_key ${openai_key}
Use the DeepL model with Japanese

python3 make_book.py --book_name test_books/animal_farm.epub --model deepl --deepl_key ${deepl_key} --language ja
Use the Claude model with Japanese

python3 make_book.py --book_name test_books/animal_farm.epub --model claude --claude_key ${claude_key} --language ja
Use the CustomAPI model with Japanese

python3 make_book.py --book_name test_books/animal_farm.epub --model customapi --custom_api ${custom_api} --language ja
Translate contents in <div> and <p>

python3 make_book.py --book_name test_books/animal_farm.epub --translate-tags div,p
Tweaking the prompt

python3 make_book.py --book_name test_books/animal_farm.epub --prompt prompt_template_sample.txt
or

python3 make_book.py --book_name test_books/animal_farm.epub --prompt prompt_template_sample.json
or

python3 make_book.py --book_name test_books/animal_farm.epub --prompt "Please translate \{text}\ to {language}"
Translate books download from Rakuten Kobo on kobo e-reader

python3 make_book.py --book_from kobo --device_path /tmp/kobo
translate txt file

python3 make_book.py --book_name test_books/the_little_prince.txt --test --language zh-hans
aggregated translation txt file

python3 make_book.py --book_name test_books/the_little_prince.txt --test --batch_size 20
Using Caiyun model to translate

(the api currently only support: simplified chinese <-> english, simplified chinese <-> japanese)

the official Caiyun has provided a test token (3975l6lr5pcbvidl6jl2)

you can apply your own token by following this tutorial(https://bobtranslate.com/service/translate/caiyun.html)

python3 make_book.py --model caiyun --caiyun_key 3975l6lr5pcbvidl6jl2 --book_name test_books/animal_farm.epub

Set env BBM_CAIYUN_API_KEY to ignore option --openai_key

export BBM_CAIYUN_API_KEY=${your_api_key}

More understandable example

shell
python3 make_book.py --book_name 'animal_farm.epub' --openai_key sk-XXXXX --api_base 'https://xxxxx/v1'
Or python3 is not in your PATH

python make_book.py --book_name 'animal_farm.epub' --openai_key sk-XXXXX --api_base 'https://xxxxx/v1'

Microsoft Azure Endpoints

shell
python3 make_book.py --book_name 'animal_farm.epub' --openai_key XXXXX --api_base 'https://example-endpoint.openai.azure.com' --deployment_id 'deployment-name'
Or python3 is not in your PATH

python make_book.py --book_name 'animal_farm.epub' --openai_key XXXXX --api_base 'https://example-endpoint.openai.azure.com' --deployment_id 'deployment-name'

Docker

You can use Docker if you don't want to deal with setting up the environment.

shell
Build image

docker build --tag bilingual_book_maker .
Run container

"$folder_path" represents the folder where your book file locates. Also, it is where the processed file will be stored.
Windows PowerShell

$folder_path=your_folder_path # $folder_path="C:\Users\user\mybook\"
$book_name=your_book_name # $book_name="animal_farm.epub"
$openai_key=your_api_key # $openai_key="sk-xxx"
$language=your_language # see utils.py
docker run --rm --name bilingual_book_maker --mount type=bind,source=$folder_path,target='/app/test_books' bilingual_book_maker --book_name "/app/test_books/$book_name" --openai_key $openai_key --language $language
Linux

export folder_path=${your_folder_path}
export book_name=${your_book_name}
export openai_key=${your_api_key}
export language=${your_language}
docker run --rm --name bilingual_book_maker --mount type=bind,source=${folder_path},target='/app/test_books' bilingual_book_maker --book_name "/app/test_books/${book_name}" --openai_key ${openai_key} --language "${language}"

For example:

shell
Linux

docker run --rm --name bilingual_book_maker --mount type=bind,source=/home/user/my_books,target='/app/test_books' bilingual_book_maker --book_name /app/test_books/animal_farm.epub --openai_key sk-XXX --test --test_num 1 --language zh-hant

Notes

1. API token from free trial has limit. If you want to speed up the process, consider paying for the service or use multiple OpenAI tokens
2. PR is welcome

Thanks

- @yetone

Contribution

- Any issues or PRs are welcome.
- TODOs in the issue can also be selected.
- Please run black make_book.py[^black] before submitting the code.

Others better

- δΉ¦θ―‘ BookTranslator -> Book Translator

Appreciation

Thank you, that's enough.

!image

[^token]: https://platform.openai.com/account/api-keys
[^black]: https://github.com/psf/black


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