Building Language Translation Apps with LLM, Speech Recognition, and Computer Vision

Description and use

Building a real‑time translation app that integrates LLM, speech recognition, and computer vision, combining transcription, visual context, translation, and TTS via Oxlo.ai.

Problem

Developing a real‑time, production‑grade language translation app that handles audio, visual context, and low‑latency inference across multiple model types.

Outcome

A complete, low‑latency translation pipeline that integrates speech, vision, LLM translation, and TTS using Oxlo.ai’s request‑based pricing and OpenAI SDK compatibility.

Workflow

• Capture audio and send to Whisper for transcription
• Optionally capture image and send to a vision model (Gemma 3 27B or Kimi VL A3B) with the transcript
• Combine transcript and visual context into a translation prompt
• Send prompt to an LLM (Qwen 3 32B or Llama 3.3 70B) for translation
• Send translated text to Kokoro 82M for text‑to‑speech synthesis
• Output MP3 audio to the user

Evidence

Oxlo.ai hosts over 45 open‑source and proprietary models across seven categories, including audio, vision, chat, and speech.

Source

Because Oxlo.ai uses request‑based pricing, a long audio transcript or a detailed visual prompt costs the same flat rate per request regardless of input length.

Source