Translating Audio Content Using GPT-4o: A Step-by-Step Guide

Translating audio content into different languages can significantly enhance its accessibility and reach. With the advent of OpenAI's GPT-4o, this process has become more streamlined and efficient. This guide will walk you through translating an English audio file into Arabic using GPT-4o's advanced audio capabilities.

Step 1: Transcribe the Audio

Before dubbing, you may want to transcribe the source audio into its original language script. This step is optional if you already have the transcription. Using GPT-4o, you can transcribe the audio by sending a base64-encoded audio file to the API and specifying the desired output modality as text.

import base64
 
# Read the WAV file and encode it to base64
with open('audio.wav', 'rb') as audio_file:
    audio_bytes = audio_file.read()
    audio_base64 = base64.b64encode(audio_bytes).decode('utf-8')
 
modalities = ["text"]
prompt = "Transcribe the audio to English text, ignoring background noises."
response_json = process_audio_with_gpt_4o(audio_base64, modalities, prompt)
transcript = response_json['choices'][0]['message']['content']
print(transcript)

Step 2: Dub the Audio

With GPT-4o, you can directly dub the audio from English to Arabic. This involves setting the output modality to both text and audio, allowing you to receive the Arabic transcription and the dubbed audio in one API call.

glossary_of_terms = "GPT, OpenAI, token"
modalities = ["text", "audio"]
prompt = f"Dub the audio in Arabic, keeping certain terms in English: {glossary_of_terms}."
response_json = process_audio_with_gpt_4o(audio_base64, modalities, prompt)
arabic_transcript = response_json['choices'][0]['message']['audio']['transcript']
print(arabic_transcript)

Step 3: Evaluate Translation Quality

To ensure the quality of the translation, you can use metrics like BLEU or ROUGE. These metrics compare the translated text to a reference translation, providing a score that indicates the translation's accuracy.

import sacrebleu
from rouge_score import rouge_scorer
 
reference_text = "Your reference English text here"
candidate_text = "The re-translated English text from Arabic audio"
 
# BLEU Score
bleu = sacrebleu.corpus_bleu([candidate_text], [[reference_text]])
print(f"BLEU Score: {bleu.score}")
 
# ROUGE Score
scorer = rouge_scorer.RougeScorer(['rouge1', 'rougeL'], use_stemmer=True)
scores = scorer.score(reference_text, candidate_text)
print(f"ROUGE-1 Score: {scores['rouge1'].fmeasure}")
print(f"ROUGE-L Score: {scores['rougeL'].fmeasure}")

Conclusion

By following these steps, you can effectively translate and dub audio content from English to Arabic, making it accessible to a broader audience. This method is applicable across various industries, including education, entertainment, and business, enabling creators to reach diverse linguistic groups.

Reference: This article is inspired by the work of Mandeep Singh on voice translation using GPT-4o. Special thanks to the original author for their comprehensive guide.