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Challenge

Build a Realtime Speech-to-Speech Translator

Develop a real-time speech-to-speech translation system leveraging OpenAI's newly available Realtime API and its advanced gpt-realtime model. The system should handle multiple languages and provide a seamless user experience with minimal latency. Consider integrating features to improve accuracy and efficiency, such as noise cancellation and speaker diarization. The focus should be on building a robust and scalable solution that can handle real-world scenarios.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Create a real-time speech-to-speech translation application using OpenAI's gpt-realtime model and addressing challenges in accuracy, latency, and scalability.

Delivery guide

How work is evaluated

Evaluation

The solution will be evaluated based on translation accuracy, latency, language support, robustness, and scalability.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Learning goals

What you should walk away with

  • Design a scalable architecture to handle multiple users concurrently.

  • Handle multiple languages with high accuracy.

  • Minimize latency and ensure a smooth user experience.

  • Implement a real-time speech-to-text and text-to-speech pipeline.

  • Integrate OpenAI's gpt-realtime model for translation.

Resources and assets

Reference links and supporting material

Dataset notes

Sample data for 1 tasks

Challenge setup

The solution will be evaluated based on translation accuracy, latency, language support, robustness, and scalability.

Challenge input

Audio files in various languages

Agent execution

No agent tool is listed for this challenge.

Evaluated output

Translated audio files and latency measurements

Evaluation checks
  • Check if system supports at least 3 languages
Configured evaluation evidence
  • Translation Accuracy (BLEU Score) target: 0.8
Configured runtime
  • Python execution harness

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

No tool records are attached.

Evaluation contract

  • Check if system supports at least 3 languages

Metrics

  • Translation Accuracy (BLEU score) · Target 0.8
  • Latency · Target 0.7

Workflow tasks

  1. Task 1

    Input: Audio files in various languages

    Output: Translated audio files and latency measurements

    Execution harness: Python

Recipe freeze marker

No recipe freeze time is recorded.

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