Breaking Language Barriers: Unleashing the Power of AI for Multilingual Translation

Artificial Intelligence (AI) has revolutionized numerous fields, and language translation is no exception. With the advent of advanced AI models, we can now perform multilingual translation more efficiently and accurately. In this post, we'll illustrate how to use the latest AI techniques for multilingual translation, with a specific focus on the powerful tools provided by OpenAI and Hugging Face.

Output from bellow Version 2 example of Slovenian to English transaction of sentence:

84 vaščanov majhne švicarske vasice Brienz je imelo le 48 ur časa, da spakirajo svoje stvari in zapustijo svoje domove. Geologi so jih namreč opozorili, da je skalni podor z gore nad njimi neizbežen.

Speech converted

0:00

/0:08

1×

Verison 1: Using OpenAI-Whisper with Google Cloud Text-To-Speech

Whisper, trained on a whopping 680,000 hours of multilingual and multitask supervised data collected from the web, offers highly accurate transcription services. On the other hand, Google Cloud Text-To-Speech enables developers to synthesize natural-sounding speech with 100+ voices, available in multiple languages and variants. When used together, these two tools open up a plethora of opportunities for developers.

ai-translation/whisper.ipynb at master · igorrendulic/ai-translation

Using OpenAI and Google Cloud APIs for Multilingual Text-to-Speech Conversion - ai-translation/whisper.ipynb at master · igorrendulic/ai-translation

GitHubigorrendulic

Version 2: Using newly released HuggingFace Transformers Agents

In the vast universe of AI, HuggingFace's Transformer library is a shining star. It's a popular choice among researchers and developers alike for its wide range of pre-trained models, state-of-the-art architectures, and user-friendly interface. Recently, HuggingFace introduced a new feature - Transformers Agents. This latest addition further empowers developers by providing a simplified way to use these models.

ai-translation/transformers.ipynb at master · igorrendulic/ai-translation

Using OpenAI and Google Cloud APIs for Multilingual Text-to-Speech Conversion - ai-translation/transformers.ipynb at master · igorrendulic/ai-translation

GitHubigorrendulic

Conclusion

As we journey through the fascinating world of AI and machine learning, it's clear that we're witnessing a revolutionary era in multilingual translation. Speech models are becoming remarkably efficient and precise.

However, despite the tremendous progress, there are still challenges to overcome. Languages like Slovene, with fewer speakers and less global prominence, are yet to be effectively incorporated into text-to-speech technologies. (checkout the file in Github called: slo_speech_converted.mp3

GitHub Repository:

GitHub - igorrendulic/ai-translation: Using OpenAI and Google Cloud APIs for Multilingual Text-to-Speech Conversion

Using OpenAI and Google Cloud APIs for Multilingual Text-to-Speech Conversion - GitHub - igorrendulic/ai-translation: Using OpenAI and Google Cloud APIs for Multilingual Text-to-Speech Conversion

GitHubigorrendulic