Cerebras Voice Agent: Building Real-Time, Low-Latency Voice AI in 2025

Introduction to Cerebras Voice Agent

Understanding the Cerebras Voice Agent Ecosystem

• Large Language Models (LLMs): State-of-the-art models like Llama 3 (8B/70B), running on Cerebras infrastructure, bring contextual understanding and generative power.
• Speech-to-Text (STT): Services such as Deepgram convert audio input to text with minimal latency.
• Text-to-Speech (TTS): Solutions like Cartesia or Deepgram synthesize natural-sounding responses.
• Orchestration Tools: Frameworks like Pipecat manage the end-to-end workflow, while Cerebrium provides scalable, serverless deployment.

Why Low Latency Matters in Voice AI

Technical vs. Perceptual Latency

• Technical latency: The actual time taken by each component (STT, LLM inference, TTS) to process data.
• Perceptual latency: The delay as perceived by the user, typically anything above 300ms can degrade the experience.

Industry Benchmarks

• Best-in-class target: End-to-end latency < 500ms for real-time voice agents.
• Cerebras: Achieves industry-leading throughput and low time-to-first-token (TTFT), outperforming many GPU-based solutions. This is critical for applications like live sales coaching or educational tutoring where delays can break conversational immersion.

Cerebras Inference: Powering Real-Time Voice Agents

TTFT and TPS in Conversational Pipelines

• TTFT (Time-to-First-Token): Measures how quickly the model produces the first output token after receiving a prompt. Lower TTFT means the user hears a response quicker.
• TPS (Tokens per Second): Indicates how fast the model can continue generating tokens, impacting the overall response speed.

Real-World Benchmark

Example: API Call to Cerebras LLM

1import requests
2
3API_URL = "https://api.cerebras.ai/v1/llm/infer"
4API_KEY = "your_cerebras_api_key"
5
6payload = {
7    "model": "llama3-8b",
8    "prompt": "What is the weather like today?",
9    "max_tokens": 64
10}
11headers = {
12    "Authorization": f"Bearer {API_KEY}",
13    "Content-Type": "application/json"
14}
15
16response = requests.post(API_URL, json=payload, headers=headers)
17print(response.json())
18

Building Your Own Cerebras Voice Agent: Step-by-Step Guide

Setting Up the Environment

• Sign up for Cerebrium to deploy serverless AI endpoints and manage resources.
• Obtain API keys for Cerebras LLM, Deepgram (STT/TTS), and Cartesia (if using).
• Install dependencies: Recommended stack includes Python 3.9+, requests, deepgram-sdk, and pipecat for orchestration.

1pip install requests deepgram-sdk pipecat
2

Data Pipeline: Audio to Text

1from deepgram import Deepgram
2import asyncio
3
4DG_API_KEY = "your_deepgram_api_key"
5dg_client = Deepgram(DG_API_KEY)
6
7async def transcribe_audio(audio_stream):
8    response = await dg_client.transcription.prerecorded({
9        "buffer": audio_stream,
10        "mimetype": "audio/wav"
11    })
12    return response["results"]["channels"][0]["alternatives"][0]["transcript"]
13

Running LLM Inference with Cerebras

1import requests
2
3API_URL = "https://api.cerebras.ai/v1/llm/infer"
4API_KEY = "your_cerebras_api_key"
5
6def query_llm(prompt, model="llama3-8b"):
7    payload = {
8        "model": model,
9        "prompt": prompt,
10        "max_tokens": 128
11    }
12    headers = {
13        "Authorization": f"Bearer {API_KEY}",
14        "Content-Type": "application/json"
15    }
16    response = requests.post(API_URL, json=payload, headers=headers)
17    return response.json()["response"]
18

Text-to-Speech Synthesis

• Choose neural voices for naturalness.
• Stream audio in small chunks for real-time playback.

1import requests
2
3def synthesize_speech(text):
4    url = "https://api.deepgram.com/v1/speak"
5    headers = {
6        "Authorization": "Token your_deepgram_api_key",
7        "Content-Type": "application/json"
8    }
9    payload = {
10        "text": text,
11        "voice": "en-US-Wavenet-D"
12    }
13    response = requests.post(url, json=payload, headers=headers)
14    return response.content  # WAV audio bytes
15

Orchestrating the Workflow with Pipecat

1{
2  "stt": "deepgram",
3  "llm": "cerebras_llama3",
4  "tts": "deepgram_tts",
5  "orchestrator": "pipecat"
6}
7

Deployment and Scaling with Cerebrium

• Scale elastically based on concurrent sessions
• Monitor latency and throughput in real time
• Automate resource allocation for cost and performance optimization

Advanced Topics: Customization, Latency Optimization, and Use Cases

Model Quantization and Hardware Acceleration

Fine-Grained Voice Controls

Real-World Use Cases

• Sales Coaches: Real-time objection handling and script suggestions
• Tutors: Adaptive, conversational learning experiences
• Customer Support Bots: Instantaneous, context-aware responses

Conclusion and Next Steps