ChatGPT has also demonstrated its capabilities as a robust translator, capable of handling not just common languages, but also unconventional forms of writing like emojis and word scrambling. However, it may not always produce a deterministic output or adhere to line-to-line correlation, potentially disrupting the timing of subtitles, even when instructed to follow precise instructions and with the model temperature parameter set to 0.

This utility uses the OpenAI ChatGPT API to translate text, with a specific focus on line-based translation, especially for SRT subtitles. The translator optimizes token usage by removing SRT overhead and grouping text into batches, resulting in arbitrary length translations without excessive token consumption while ensuring a one-to-one match between line input and output.

• Web User Interface (Web UI) and Command Line Interface (CLI)
• New: Supports Structured Output: for more concise results, available in the Web UI and in CLI with --experimental-structured-mode
• New: Supports Prompt Caching: by including the full context of translated data, the system instruction and translation context are packaged to work well with prompt caching, enabled with --experimental-use-full-context (CLI only)
• Supports any OpenAI API compatible providers such as running Ollama locally
• Line-based batching: avoids token limit per request, reduces overhead token wastage, and maintains translation context to a certain extent
• Checks with the free OpenAI Moderation tool: prevents token wastage if the model is highly likely to refuse to translate
• Streaming process output
• Request per minute (RPM) rate limits
• Progress resumption (CLI only)

Reference: https://github.com/openai/openai-quickstart-node#setup

• Node.js version >= 16.13.0 required. This README assumes bash shell environment
• Clone this repository and

  git clone https://github.com/Cerlancism/chatgpt-subtitle-translator

• Navigate into the directory

  cd chatgpt-subtitle-translator

• Install the requirements

  npm install

• Give executable permission

  chmod +x cli/translator.mjs

• Copy .example.env to .env

  cp .env.example .env

• Add your API key to the newly created .env file
  • (Optional) Set rate limits: https://platform.openai.com/docs/guides/rate-limits/overview

cli/translator.mjs --help

Usage: translator [options]

Translation tool based on ChatGPT API

Options:

• --from <language>
  Source language (default: "")

• --to <language>
  Target language (default: "English")

• -i, --input <file>
  Input source text with the content of this file, in .srt format or plain text

• -o, --output <file>
  Output file name, defaults to be based on input file name

• -p, --plain-text <text>
  Input source text with this plain text argument

• -s, --system-instruction <instruction>
  Override the prompt system instruction template Translate ${from} to ${to} with this plain text, ignoring --from and --to options

• --initial-prompts <prompts>
  Initial prompts for the translation in JSON (default: "[]")

• --no-use-moderator
  Don't use the OpenAI API Moderation endpoint

• --moderation-model
  (default: "omni-moderation-latest") https://platform.openai.com/docs/models/moderation

• --no-prefix-number
  Don't prefix lines with numerical indices

• --no-line-matching
  Don't enforce one to one line quantity input output matching

• -l, --history-prompt-length <length>
  Length of prompt history to retain for next request batch (default: 10)

• -b, --batch-sizes <sizes> Batch sizes of increasing order for translation prompt slices in JSON Array (default: "[10,100]")

  The number of lines to include in each translation prompt, provided that they are estimated to be within the token limit.
  In case of mismatched output line quantities, this number will be decreased step by step according to the values in the array, ultimately reaching one.

  Larger batch sizes generally lead to more efficient token utilization and potentially better contextual translation.
  However, mismatched output line quantities or exceeding the token limit will cause token wastage, requiring resubmission of the batch with a smaller batch size.

• --experimental-structured-mode [mode]
  Enable structured response. (default: array, choices array)

  • --experimental-structured-mode array Structures the input and output into a plain array format. This option is more concise compared to base mode, though it uses slightly more tokens per batch.

• --experimental-use-full-context
  Include the full context of translated data to work well with prompt caching.

  The translated lines per user and assistant message pairs are sliced as defined by --history-prompt-length (by default --history-prompt-length 10), it is recommended to set this to the largest batch size (by default --batch-sizes "[10,100]"): --history-prompt-length 100.

  Enabling this may risk running into the model's context window limit, typically 128K, but should be sufficient for most cases.

• --log-level <level>
  Log level (default: debug, choices: trace, debug, info, warn, error, silent)

• --silent
  Same as --log-level silent

• --quiet
  Same as --log-level silent

Additional Options for GPT:

• -m, --model <model>
  (default: "gpt-4o-mini") https://platform.openai.com/docs/api-reference/chat/create
• --stream
  Stream progress output to terminal https://platform.openai.com/docs/api-reference/chat/create#chat-create-stream
• -t, --temperature <temperature>
  Sampling temperature to use, should set a low value below 0.3 to be more deterministic for translation (default: 1) https://platform.openai.com/docs/api-reference/chat/create#chat-create-temperature
• --top_p <top_p>
  Nucleus sampling parameter, top_p probability mass https://platform.openai.com/docs/api-reference/chat/create#chat-create-top_p
• --presence_penalty <presence_penalty>
  Penalty for new tokens based on their presence in the text so far https://platform.openai.com/docs/api-reference/chat/create#chat-create-presence_penalty
• --frequency_penalty <frequency_penalty
  Penalty for new tokens based on their frequency in the text so far https://platform.openai.com/docs/api-reference/chat/create#chat-create-frequency_penalty
• --logit_bias <logit_bias>
  Modify the likelihood of specified tokens appearing in the completion https://platform.openai.com/docs/api-reference/chat/create#chat-create-logit_bias

cli/translator.mjs --plain-text "你好"

Standard Output

Hello.

cli/translator.mjs --stream --to "Emojis" --temperature 0 --plain-text "$(curl 'https://api.chucknorris.io/jokes/0ECUwLDTTYSaeFCq6YMa5A' | jq .value)"

Input Argument

Chuck Norris can walk with the animals, talk with the animals; grunt and squeak and squawk with the animals... and the animals, without fail, always say 'yessir Mr. Norris'.

Standard Output

👨‍🦰💪🚶‍♂️🦜🐒🐘🐅🐆🐎🐖🐄🐑🦏🐊🐢🐍🐿️🐇🐿️❗️🌳💬😲👉🤵👨‍🦰👊=🐕🐑🐐🦌🐘🦏🦍🦧🦓🐅🦌🦌🦌🐆🦍🐘🐘🐗🦓=👍🤵.

cli/translator.mjs --stream --system-instruction "Scramble characters of words while only keeping the start and end letter" --no-prefix-number --no-line-matching --temperature 0 --plain-text "Chuck Norris can walk with the animals, talk with the animals;"

Standard Output

Cuhck Nroris can wakl wtih the aiamnls, talk wtih the aiamnls;

cli/translator.mjs --stream --system-instruction "Unscramble characters back to English" --no-prefix-number --no-line-matching --temperature 0 --plain-text "Cuhck Nroris can wakl wtih the aiamnls, talk wtih the aiamnls;"

Standard Output

Chuck Norris can walk with the animals, talk with the animals;

cli/translator.mjs --stream --temperature 0 --input test/data/test_cn.txt

Input file: test/data/test_cn.txt

你好。
拜拜！

Standard Output

Hello.  
Goodbye!

cli/translator.mjs --stream --temperature 0 --input test/data/test_ja_small.srt

Input file: test/data/test_ja_small.srt

1
00:00:00,000 --> 00:00:02,000
おはようございます。

2
00:00:02,000 --> 00:00:05,000
お元気ですか？

3
00:00:05,000 --> 00:00:07,000
はい、元気です。

4
00:00:08,000 --> 00:00:12,000
今日は天気がいいですね。

5
00:00:12,000 --> 00:00:16,000
はい、とてもいい天気です。

Output file: test/data/test_ja_small.srt.out_English.srt

1
00:00:00,000 --> 00:00:02,000
Good morning.

2
00:00:02,000 --> 00:00:05,000
How are you?

3
00:00:05,000 --> 00:00:07,000
Yes, I'm doing well.

4
00:00:08,000 --> 00:00:12,000
The weather is nice today, isn't it?

5
00:00:12,000 --> 00:00:16,000
Yes, it's very nice weather.

System Instruction
Tokens: 4

Translate to English

1
00:00:00,000 --> 00:00:02,000
おはようございます。

2
00:00:02,000 --> 00:00:05,000
お元気ですか？

3
00:00:05,000 --> 00:00:07,000
はい、元気です。

4
00:00:08,000 --> 00:00:12,000
今日は天気がいいですね。

5
00:00:12,000 --> 00:00:16,000
はい、とてもいい天気です。

1. おはようございます。
2. お元気ですか？
3. はい、元気です。
4. 今日は天気がいいですね。
5. はい、とてもいい天気です。

1. Good morning.
2. How are you?
3. Yes, I'm doing well.
4. The weather is nice today, isn't it?
5. Yes, it's very nice weather.

1
00:00:00,000 --> 00:00:02,000
Good morning.

2
00:00:02,000 --> 00:00:05,000
How are you?

3
00:00:05,000 --> 00:00:07,000
Yes, I'm doing well.

4
00:00:08,000 --> 00:00:12,000
The weather is nice today, isn't it?

5
00:00:12,000 --> 00:00:16,000
Yes, it's very nice weather.

Test data can be found in the test/data directory. Token count also roughly includes message payload structure and prompt token overheads.

SRT Text Format: Full SRT text format, including timestamps for input/output.
No Batching: SRT formatting and timestamp stripping, but one line per prompt with system instruction overhead, including up to 10 historical entries for context per prompt.
ChatGPT Subtitle Translator: SRT formatting and timestamp stripping, with line batching of 100, including up to 10 historical entries for context per batch.

This analysis assumes perfect input/output quantity matching. In reality, this depends on model and subtitle quality. Typically, buffer an additional 20%~30% token usage for retries, refer to the --batch-sizes CLI option.