Unlocking Audio Analysis with LLMs

Audio is the fastest-growing unstructured data type in enterprise stacks, yet it remains the hardest to query. Unlike text logs or JSON events, you cannot grep a podcast or run SQL over a support call. The practical pattern is to transcribe speech to text with an automatic speech recognition model, then apply a large language model to the transcript for summarization, sentiment analysis, entity extraction, or compliance scoring. Oxlo.ai hosts both the transcription and reasoning layers under a single request-based pricing model, removing the cost uncertainty that scales with transcript length on token-based providers.

The Transcription-Reasoning Pipeline

Most audio analysis workloads follow a two-stage pipeline. First, an audio transcription model converts speech into text. Second, an LLM reasons over that text. On Oxlo.ai, both stages use the same OpenAI-compatible SDK and base URL, so you do not need separate clients or authentication schemes.

For transcription, Oxlo.ai offers Whisper Large v3, Whisper Turbo, and Whisper Medium through the audio/transcriptions endpoint. For reasoning, you can route the resulting text to any chat model, such as Llama 3.3 70B for general analysis, Qwen 3 32B for multilingual agent workflows, or DeepSeek R1 671B MoE for deep reasoning over complex technical conversations.

Code Example: Analyzing a Support Call

The following Python example uses the OpenAI SDK to transcribe a support call and then extract structured insights with an LLM. Because Oxlo.ai is fully OpenAI SDK compatible, the only change is the base_url.

import openai
import json

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

# Stage 1: Transcribe audio to text
with open("support_call.mp3", "rb") as audio_file:
    transcript = client.audio.transcriptions.create(
        model="whisper-large-v3",
        file=audio_file,
        response_format="text"
    )

# Stage 2: Structured analysis with an LLM
completion = client.chat.completions.create(
    model="llama-3.3-70b",
    messages=[
        {
            "role": "system",
            "content": (
                "You are a call-quality analyst. Read the transcript and produce JSON with "
                "keys: sentiment (positive, neutral, negative), speaker_roles (list), and "
                "action_items (list)."
            )
        },
        {"role": "user", "content": transcript}
    ],
    response_format={"type": "json_object"}
)

result = json.loads(completion.choices[0].message.content)
print(json.dumps(result, indent=2))

The same pattern works for batch processing. Point the transcription step at an S3 bucket or local directory, then fan out the text to the chat endpoint with asynchronous workers. Because Oxlo.ai has no cold starts on popular models, the first request in a batch returns as quickly as the hundredth.

Why Request Pricing Matters for Audio

Audio produces verbose transcripts. A thirty-minute interview can easily generate thousands of tokens of text. On token-based providers, the cost to analyze that transcript scales linearly with its length, and if you run multiple analysis passes, the multiplier compounds.

Oxlo.ai uses request-based pricing: one flat cost per API request regardless of prompt length. That means analyzing a long transcript costs the same as a short one, and running ten reasoning passes over the same text costs exactly ten requests. For long-context and agentic audio workflows, this can be 10-100x cheaper than token-based alternatives. See the exact tiers on the Oxlo.ai pricing page.

Scaling Beyond Single Files

Production audio pipelines rarely stop at one file. You can build stateful agents by combining transcription with function calling. For example, after transcribing a customer call, an LLM can decide whether to open a ticket in your CRM, escalate to a human, or append notes to an existing account. Oxlo.ai supports function calling and multi-turn conversations on its chat models, so the decision logic stays in the same request-based layer.

If you need to preserve context across long recordings, use a model with a large context window such as Kimi K2.6, which offers a 131K context and advanced reasoning, or DeepSeek V4 Flash with its 1M context window for near state-of-the-art open-source reasoning over entire audiobooks or day-long meeting archives.

Models and Formats on Oxlo.ai

Oxlo.ai provides dedicated endpoints for every stage of an audio analysis pipeline.

• Speech-to-text: Whisper Large v3, Whisper Turbo, and Whisper Medium via audio/transcriptions.
• Text-to-speech: Kokoro 82M via audio/speech for building voice agents that respond after analysis.
• Reasoning: Llama 3.3 70B, Qwen 3 32B, DeepSeek R1 671B MoE, Kimi K2.6, GLM 5, and others via chat/completions.
• Structured output: JSON mode and function calling are supported on compatible chat models.

All endpoints share the same base URL and authentication, so switching from transcription to reasoning is a single line change in your client configuration.

Next Steps

If you are currently paying token-based rates to analyze transcripts, moving the workload to Oxlo.ai is a drop-in replacement. The Free plan includes 60 requests per day and a 7-day full-access trial, which is enough to prototype a full transcription-to-insights pipeline without a credit card. When you are ready to scale, paid plans add daily request allotments, priority queue access, and dedicated GPU options. Review the details at https://oxlo.ai/pricing and point your OpenAI client to https://api.oxlo.ai/v1 to start.</