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Translate Subtitles Using GPT

Hacker News

Translate Subtitles Using GPT

GPT-4 excels as a translator, but it often encounters issues with content warnings and formatting errors when translating entire subtitle files via ChatGPT. The solution is straightforward: divide the subtitle file into sections, focusing solely on translating the text and disregarding the timestamps. While it's feasible to have ChatGPT maintain the correct format, I've observed a decline in translation quality when attempting this in a single pass. My preferred approach is a two-phase method: first, translate the text, and then, if necessary, request ChatGPT to adjust the formatting. To streamline this process, I developed a simple web application. Alternatively, one could manually refine the translations. The prompt is also customizable, allowing for more specific instructions. For instance, you can specify that certain words should be translated according to a provided dictionary. Considering the cost of translation (approximately $1 for the GPT-4 model per movie), each user utilizes their own API token. The app makes requests directly to OpenAI, ensuring it's safe to share your token. This approach allows users to manage their expenses while benefiting from the advanced translation capabilities of GPT-4. Source code is at https://github.com/afiodorov/subsgpt

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
91%91% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, chatgpt · Missing: mac, agents, macos
86%86% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: users · Missing: mobile apps, ios, personal
56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: users · Missing: plus, platform, intuitive
54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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