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shashank ms
shashank ms

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Using LLM for Audio Processing: A Step-by-Step Guide

#aiinfrastructure #oxlo #ai

Audio is becoming a first-class modality for large language models. Whether you are transcribing hours of meeting recordings, synthesizing natural speech for voice agents, or building multimodal pipelines that reason over sound, modern APIs make it possible to treat audio as structured data. Oxlo.ai provides a full stack of audio models, including Whisper Large v3, Whisper Turbo, Whisper Medium, and Kokoro 82M text-to-speech, all behind a single OpenAI-compatible endpoint. Because Oxlo.ai charges one flat cost per API request rather than billing by token count, long audio files and multi-turn voice workflows do not inflate your bill as they would on token-based providers.

What LLM Audio Processing Actually Means

LLM audio processing generally falls into three categories. Speech-to-text converts raw audio into structured transcripts. Text-to-speech generates natural audio from text prompts. Audio understanding, an emerging area, feeds acoustic features or transcripts directly into reasoning models for summarization, entity extraction, or agentic tool use. Oxlo.ai supports the first two today through dedicated audio/transcriptions and audio/speech endpoints, while the platform's chat and reasoning models can operate on the resulting text to complete the loop.

Setting Up Your Environment

Oxlo.ai is a drop-in replacement for the OpenAI SDK. You only need to change the base URL and API key. Install the official client, then point it to https://api.oxlo.ai/v1.

pip install openai
import openai

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

Transcribing Audio with Whisper

Oxlo.ai hosts Whisper Large v3, Whisper Turbo, and Whisper Medium on the audio/transcriptions endpoint. These models handle multilingual speech, timestamps, and noisy inputs. Because Oxlo.ai uses request-based pricing, the cost of transcribing a ten-minute interview is the same flat per-request rate as a thirty-second clip. You can check current rates on the Oxlo.ai pricing page.

import openai

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

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

print(transcript.text)

Generating Speech with Kokoro

For text-to-speech, Oxlo.ai offers Kokoro 82M, a lightweight but high-quality model accessible through the audio/speech endpoint. You can generate voice audio for alerts, audiobooks, or conversational agents with the same SDK methods you would use for any OpenAI-compatible provider.

response = client.audio.speech.create(

    model="kokoro-82m",

    voice="voice_id",  # replace with your

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