Building a Language Translation App with LLM, Speech Recognition, Computer Vision, and Robotics

Building a language translation app today means more than swapping text between languages. Modern use cases demand real-time speech recognition, visual scene understanding, and even robotic actuation to bridge communication gaps physically. Stitching these capabilities together typically forces developers to juggle multiple providers, each with its own SDK, latency profile, and pricing model. Oxlo.ai removes that fragmentation by hosting more than 45 models across seven categories behind a single OpenAI-compatible API with request-based pricing.

Architecture of a Multimodal Translation System

A robust translation pipeline moves through four stages. First, audio input is transcribed into text. Second, an optional vision model analyzes the surrounding scene to disambiguate context. Third, an LLM translates and reasons over the combined input. Fourth, the output is synthesized into speech or converted into a robotic command. Running this on Oxlo.ai means every stage hits the same base URL and uses the same SDK, so you do not need to manage separate authentication or rate-limiting logic for speech, vision, and text.

Speech Recognition with Whisper

Oxlo.ai hosts Whisper Large v3, Whisper Turbo, and Whisper Medium under the audio/transcriptions endpoint. Because the platform charges per request rather than per token, transcribing a long meeting or lecture does not inflate costs as it would with token-based providers such as Together AI, Fireworks AI, OpenRouter, Replicate, or Anyscale.

from openai import OpenAI

client = OpenAI(
    base_url="https://api.oxlo.ai/v1",
    api_key="YOUR_OXLO_API_KEY"
)

with open("conversation.wav", "rb") as audio_file:
    transcript = client.audio.transcriptions.create(
        model="whisper-large-v3",
        file=audio_file,
        response_format="text"
    )
print(transcript)

Adding Visual Context with Vision Models

If your app runs on a device with a camera, you can ground translations in visual reality. Oxlo.ai offers vision-capable models such as Gemma 3 27B and Kimi VL A3B. You pass the image directly into the chat completions endpoint alongside the transcribed text, and the model returns context-aware descriptions or disambiguations.

import base64

def encode_image(path):
    with open(path, "rb") as f:
        return base64.b64encode(f.read()).decode("utf-8")

image_b64 = encode_image("scene.jpg")

response = client.chat.completions.create(
    model="gemma-3-27b-it",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Describe this scene in English for translation."},
                {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_b64}"}}
            ]
        }
    ]
)
scene_context = response.choices[0].message.content

Translation and Reasoning with LLMs

For the actual translation, Oxlo.ai provides several strong options. Qwen 3 32B excels at multilingual