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DebasishDhal99/all-in-one-translation

sourceHugging Faceupdated 9d agoView on Hugging Face
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App README

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Logs

  • —27/05/2025 - This app receieved bulk usage, in terms of data size. 63 requests with input text of size 3.3MB. The https://detectlanguage.com/ API can only support 1MB/day. My personal API key was suspeded, making this app dysfunctional.
  • —28/05/2025 - I have removed the need for an external API. Using langdetect library for the same purpose now. No limitations of usage (Theoretically). **************************** Replication
  • —Requirements
  • —Free API Key from https://detectlanguage.com/ for automatic language detection from text.
  • —GPU for Whisper model inference. It's slower in CPU.
  • —Notes
  • —pytesseract library (For image-to-text) is easier to install in linux machines.
  • —If you have GPU, you can go for more sophisticated image-to-text models.
  • —The image-to-text setup works best for non-decorative and normal sized fonts. *******

The space consists of 3-4 parts: -

  • —Text translator - Input (Input Text, Target language), Output (Translated text in target language, Source language name)
  • —Image translator - Input (Image with any text, Source language, Target language), Output (Image text in source language, Image text translated to target language)
  • —Audio translator - Input (Audio in any language, Model size, Target language), Output (Transcribed original text, Transcribed text translated to target language, Original language name)
  • —Video translator - Input (Video, Model size, Target language), Output (Translated text version of the audio) [Not yet implemented]

Demo

Text translator

  • —Simple deep-translator library usage. image/png

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Image translator

  • —Best works with simple fonts. Performance detoriates with decorative fonts.
  • —For now, you have to choose the language, choosing "English" can work for almost all Latin-script languages like (Spanish, Romanian etc.)
  • —Using pytesseract model for image-to-text conversion. It's installation is a bit complicated. Follow this link for installation

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Audio translator

  • —Since I am on a free-tier space, the inference takes a lot of time (1000 seconds for 10 seconds of audio)
  • —If one has HuggingFace pro, he/she can get a GPU and get reasonable inference time. But for now, this is just a demo.
  • —If you have an OpenAPI key, you can use whisper speech-to-text model via API call. But since I don't have it, I used the whisper library method, where you have to take care of the inference hardware yourself. image/png
  • —Here is a 10 seconds translation of the famous Russian song Kukushka

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference