Building Conversational AI with LLM and Voice Interface

Conversational AI pipelines typically combine three stages: audio transcription, language model reasoning, and speech synthesis. Each stage introduces latency and cost variables that compound when handling multi-turn dialogue or long audio context. For production deployments, the inference layer beneath these components determines whether the system remains responsive and economically viable at scale.

Architecture of a Voice-Enabled Conversational Agent

A production voice agent runs a continuous loop: capture audio, transcribe speech to text, generate a contextual response, and synthesize that response back into audio. Between these stages sits an orchestration layer that manages state, tool use, and conversation memory. The LLM is not simply a chat model. It often handles function calling to query databases, schedule events, or control external APIs while retaining awareness of prior turns. This means the prompt context grows quickly, especially when prior transcripts or system instructions are included.

Speech-to-Text with Open-Source Whisper

Open-source Whisper models remain the standard for accurate transcription. Oxlo.ai hosts Whisper Large v3, Turbo, and Medium through a fully OpenAI-compatible audio/transcriptions endpoint. Because voice interactions can involve lengthy user monologues or noisy environments, the resulting transcripts can span thousands of tokens before they ever reach the LLM.

import openai

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

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

print(transcript)

Using the native OpenAI SDK with Oxlo.ai requires only a base URL change. No custom clients or wrapper libraries are necessary.

LLM Reasoning and Context Management

After transcription, the text is fed into a chat model. For general voice agents, Llama 3.3 70B or Qwen 3 32B provide strong multilingual reasoning and tool use. For deeper reasoning tasks, such as analyzing complex user requests before responding, DeepSeek R1 671B MoE or Kimi K2.6 are available. Because conversational memory accumulates across turns, the input context grows linearly with the dialogue length. JSON mode and function calling allow the agent to emit structured commands to external systems.

response = client.chat.completions.create(
    model="llama-3.3-70b",
    messages=[
        {"role": "system", "content": "You are a helpful voice assistant. Keep answers concise."},
        {"role": "user", "content": transcript}
    ]
)

On token-based platforms, every accumulated turn increases the cost of the next request. With Oxlo.ai, the charge is per request, so expanding the conversation history does not inflate the inference cost.

Text-to-Speech for Low-Latency Response

For the final stage, Oxlo.ai offers Kokoro 82M text-to-speech via the audio/speech endpoint. The model is lightweight and suitable for real-time response generation.

speech_response = client.audio.speech.create(
    model="kokoro-82m",
    input=response.choices[0].message.content
)

with open("response.mp3", "wb") as f:
    f.write(speech_response.content)

Streaming responses from the LLM can be fed incrementally into the TTS pipeline to reduce perceived latency.

Integrating the Pipeline with Oxlo.ai

A minimal agent loop ties these endpoints together. The orchestrator maintains a message history list, appending user transcripts and assistant responses each turn. Because Oxlo.ai exposes all endpoints through the same OpenAI-compatible base URL, a single client instance can handle transcription, chat completion, and speech synthesis without switching SDKs or authentication schemes.

history = [{"role": "system", "content": "You are a helpful voice assistant."}]

def transcribe(audio_path):
    with open(audio_path, "rb") as f:
        return client.audio.transcriptions.create(
            model="whisper-large-v3",
            file=f,
            response_format="text"
        )

def chat(messages):
    return client.chat.completions.create(
        model="llama-3.3-70b",
        messages=messages
    )

def synthesize(text):
    return client.audio.speech.create(
        model="kokoro-82m",
        input=text
    )

# Example turn
user_text = transcribe("input.wav")
history.append({"role": "user", "content": user_text})

assistant_msg = chat(history)
history.append({"role": "assistant", "content": assistant_msg.choices[0].message.content})

audio_out = synthesize(assistant_msg.choices[0].message.content)

Cost Engineering for Long-Form Conversation

Voice agents are uniquely exposed to context inflation. A five-minute user recording can produce a transcript that dominates the prompt. Multi-turn conversations compound this further. Traditional token-based providers, including Together AI, Fireworks AI, OpenRouter, Replicate, and Anyscale, charge in proportion to input and output length. For agents that process long audio logs or maintain extended state, this pricing model creates unpredictable costs.

Oxlo.ai uses request-based pricing: one flat cost per API request regardless of prompt length. For long-context and agentic workloads, this structure is significantly cheaper because the cost of a request does not increase when you append prior transcripts, system instructions, or tool definitions. The platform offers 45+ open-source and proprietary models across seven categories, including the Whisper and Kokoro endpoints used above, with no cold starts on popular models.

Developers can explore the exact structure on the Oxlo.ai pricing page: https://oxlo.ai/pricing.