Harnessing LLMs for Speech Recognition and Voice Control

Speech recognition has evolved far beyond mapping audio to text. Modern voice control systems treat transcription as the first layer in a deeper inference stack where large language models parse intent, maintain conversational state, and trigger tools. By combining dedicated audio models with general-purpose LLMs, developers can build agents that understand context, handle ambiguity, and execute multi-step commands. Oxlo.ai provides the infrastructure for this entire pipeline under a single request-based pricing model that stays predictable even when transcripts grow long.

The Two-Stage Architecture

Traditional ASR gives you text. LLMs give you meaning. The most robust voice control architectures pair Whisper-class transcription with a reasoning model that performs intent classification, slot filling, and function calling. This two-stage approach decouples acoustic fidelity from business logic, letting you upgrade either component without rewriting the other.

Oxlo.ai supports both stages natively. The platform hosts Whisper Large v3, Whisper Turbo, and Whisper Medium for audio transcription, alongside general-purpose and reasoning LLMs such as Llama 3.3 70B, Qwen 3 32B, and DeepSeek R1 671B MoE. All models are accessible through a single OpenAI-compatible API at https://api.oxlo.ai/v1, so you can route audio to text and text to action without managing multiple providers.

Transcription Without Token Taxes

Audio transcripts are deceptively large in token count. A thirty-minute meeting recording can easily exceed ten thousand tokens. On token-based inference platforms, that length directly inflates cost before the text even reaches a language model. Oxlo.ai uses request-based pricing, so a transcription call costs one flat request regardless of audio duration or resulting text length.

This predictability matters when you feed transcripts back into an LLM for summarization, extraction, or command parsing. Because Oxlo.ai does not scale costs with input length, long-form voice workloads, agentic loops, and multi-document context windows avoid the compounding token penalties common with providers like Together AI, Fireworks AI, OpenRouter, Replicate, or Anyscale.

import openai

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

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

print(transcript)

Reasoning Over Speech

Raw transcripts are unstructured. Turning "find another slot if Sarah is busy next Tuesday" into a calendar query requires reasoning, not regex. Oxlo.ai offers models optimized for exactly this kind of work. DeepSeek R1 671B MoE and Kimi K2.6 handle advanced chain-of-thought reasoning, while Llama 3.3 70B provides low-latency general-purpose parsing.

Using the chat/completions endpoint, you can force structured output with JSON mode or attach function definitions for tool use. The model extracts entities, resolves ambiguous dates, and decides whether to call a scheduling API or ask for clarification.

response = client.chat.completions.create(

    model="llama-3.3-70b",

    messages=[

        {

            "role": "system",

            "content