Translate Audio to Different Languages with OpenAI: A Practical Guide

Want to easily translate audio content? Dubbing podcasts or videos into different languages expands your audience and reach. Learn how to translate English audio into Hindi using OpenAI's powerful GPT-4o in this comprehensive guide.

Why Use GPT-4o for Voice Translation?

GPT-4o simplifies translating and dubbing audio. Instead of transcribing, translating, and recreating audio, GPT-4o directly translates voice to voice. Translate audio files from one language to another with a single API call.

Streamlined Translation: GPT-4o's voice-to-voice capability eliminates intermediate steps.
Increased Efficiency: Translate audio from one language to another in one API call.
Global Reach: Dub the audio into multiple languages and reach bigger audiences.

Key Steps to Translate Audio with GPT-4o

Here’s how the workflow works, simplified into four key steps for easy understanding:

Transcribe (Optional): Transcribe the original audio into text using GPT-4o.
Dub: Directly translate the audio from the source language to the target language.
Benchmark: Evaluate the translation quality using metrics like BLEU or ROUGE.
Refine: Adjust parameters and prompts for optimal results.

Step-by-Step Guide: Translating English Audio to Hindi

Ready to translate English to Hindi? Here's a detailed walkthrough using GPT-4o.

Step 1: Transcribe the Audio (Optional)

If you don't have a transcript, create one using GPT-4o. This produces a text version of your audio.

Function: process_audio_with_gpt_4o: Sends the audio file to OpenAI's API.
Inputs:
1. Base64-encoded audio file.
2. Desired output modalities (text, audio).
3. System prompt (instructions for the model).
Output: JSON response containing the transcription.

# Make sure requests package is installed
import requests
import os
import json

# Load the API key from the environment variable
api_key = os.getenv("OPENAI_API_KEY")

defprocess_audio_with_gpt_4o(base64_encoded_audio, output_modalities, system_prompt):
# Chat Completions API end point
 url ="https://api.openai.com/v1/chat/completions"

# Set the headers
 headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {api_key}"
 }

# Construct the request data
 data = {
"model": "gpt-4o-audio-preview",
"modalities": output_modalities,
"audio": {
"voice": "alloy",
"format": "wav"
 },
"messages": [
 {
"role": "system",
"content": system_prompt
 },
 {
"role": "user",
"content": [
 {
"type": "input_audio",
"input_audio": {
"data": base64_encoded_audio,
"format": "wav"
 }
 }
 ]
 }
 ]
 }

 request_response = requests.post(url, headers=headers, data=json.dumps(data))
if request_response.status_code ==200:
return request_response.json()
else:
print(f"Error {request_response.status_code}: {request_response.text}")
return

import base64
audio_wav_path ="./sounds/keynote_recap.wav"

# Read the WAV file and encode it to base64
withopen(audio_wav_path, "rb") as audio_file:
 audio_bytes = audio_file.read()
 english_audio_base64 = base64.b64encode(audio_bytes).decode('utf-8')

modalities = ["text"]
prompt ="The user will provide an audio file in English. Transcribe the audio to English text, word for word. Only provide the language transcription, do not include background noises such as applause. "

response_json = process_audio_with_gpt_4o(english_audio_base64, modalities, prompt)

english_transcript = response_json['choices'][0]['message']['content']

print(english_transcript)

Step 2: Dub the Audio into Hindi using GPT-4o

Use GPT-4o to translate and dub the English audio directly into Hindi.

Modality: Set output modality to ["text", "audio"] to get both the translated text and audio.
Prompt: Use specific prompts to maintain key terms in English for clarity (Turbo, OpenAI, etc.).

glossary_of_terms_to_keep_in_original_language ="Turbo, OpenAI, token, GPT, Dall-e, Python"

modalities = ["text", "audio"]
prompt =f"The user will provide an audio file in English. Dub the complete audio, word for word in Hindi. Keep certain words in English for which a direct translation in Hindi does not exist such as ${glossary_of_terms_to_keep_in_original_language}."

response_json = process_audio_with_gpt_4o(english_audio_base64, modalities, prompt)

message = response_json['choices'][0]['message']

This returns both the Hindi transcript and the translated audio file, saving you multiple API calls.

# Make sure pydub is installed
from pydub import AudioSegment
from pydub.playback import play
from io import BytesIO

# Get the transcript from the model. This will vary depending on the modality you are using.
hindi_transcript = message['audio']['transcript']

print(hindi_transcript)

# Get the audio content from the response
hindi_audio_data_base64 = message['audio']['data']

Listen to your translated audio using the code snippet below.

# Play the audio
audio_data_bytes = base64.b64decode(hindi_audio_data_base64)
audio_segment = AudioSegment.from_file(BytesIO(audio_data_bytes), format="wav")

play(audio_segment)

Step 3: Benchmark Translation Quality

Evaluate the translation's quality using BLEU and ROUGE metrics.

BLEU: Measures n-gram overlap; higher scores mean better quality.
ROUGE: Assesses summarization quality by measuring n-gram overlap.

Ideal evaluations use human-translated references. As an alternative, translate the translated audio back to the original language and compare.

# Translate the audio output file generated by the model back into English and compare with the reference text
modalities = ["text"]
prompt ="The user will provide an audio file in Hindi. Transcribe the audio to English text word for word. Only provide the language transcription, do not include background noises such as applause. "

response_json = process_audio_with_gpt_4o(hindi_audio_data_base64, modalities, prompt)

re_translated_english_text = response_json['choices'][0]['message']['content']

print(re_translated_english_text)

Maximize Your Audio Translation with GPT-4o

Translating audio opens new possibilities for global communication. With GPT-4o and this guide, anyone can deliver their message to various languages in a streamlined way.