Imported from jeremylongshore/tons-of-skills-marketplace (plugins/saas-packs/assemblyai-pack/skills/assemblyai-core-workflow-b/SKILL.md). Install upstream with npx skills add jeremylongshore/tons-of-skills-marketplace --skill assemblyai-core-workflow-b. Copyright stays with the author (MIT).

AssemblyAI Core Workflow B — Streaming & LeMUR

Overview

Two advanced workflows: (1) real-time streaming transcription via WebSocket for live captioning and voice agents, and (2) LeMUR for applying LLMs to transcripts — summarization, Q&A, action items, and custom tasks.

Prerequisites

• assemblyai package installed (npm install assemblyai)
• API key configured in ASSEMBLYAI_API_KEY
• For streaming: microphone or audio stream source

Instructions

Use server-controlled credentials for service-to-service work and short-lived tokens for browser streaming. Obtain consent for live audio, set a session and retention limit, and process only the transcript IDs and prompts approved for the stated purpose. Validate structured LeMUR output before taking any downstream action.

Part 1: Real-Time Streaming Transcription

Step 1: Basic Streaming Setup

import { AssemblyAI } from 'assemblyai';

const client = new AssemblyAI({
  apiKey: process.env.ASSEMBLYAI_API_KEY!,
});

const transcriber = client.streaming.createService({
  // Model options: 'nova-3' (default), 'nova-3-pro' (highest accuracy)
  speech_model: 'nova-3',
  sample_rate: 16000,
});

transcriber.on('open', ({ sessionId }) => {
  console.log('Session opened:', sessionId);
});

transcriber.on('transcript', (message) => {
  // message_type: 'PartialTranscript' or 'FinalTranscript'
  if (message.message_type === 'FinalTranscript') {
    console.log('[Final]', message.text);
  } else {
    process.stdout.write(`\r[Partial] ${message.text}`);
  }
});

transcriber.on('error', (error) => {
  console.error('Streaming error:', error);
});

transcriber.on('close', (code, reason) => {
  console.log('Session closed:', code, reason);
});

await transcriber.connect();

// Send audio chunks (16-bit PCM, 16kHz mono)
// transcriber.sendAudio(audioBuffer);

// When done:
// await transcriber.close();

Step 2: Stream from Microphone (Node.js)

import { AssemblyAI } from 'assemblyai';
import { spawn } from 'child_process';

const client = new AssemblyAI({
  apiKey: process.env.ASSEMBLYAI_API_KEY!,
});

const transcriber = client.streaming.createService({
  speech_model: 'nova-3',
  sample_rate: 16000,
});

transcriber.on('transcript', (msg) => {
  if (msg.message_type === 'FinalTranscript' && msg.text) {
    console.log(msg.text);
  }
});

await transcriber.connect();

// Use SoX to capture microphone audio as raw PCM
const mic = spawn('sox', [
  '-d',                  // default audio device
  '-t', 'raw',           // raw PCM output
  '-b', '16',            // 16-bit
  '-r', '16000',         // 16kHz sample rate
  '-c', '1',             // mono
  '-e', 'signed-integer',
  '-',                   // pipe to stdout
]);

mic.stdout.on('data', (chunk: Buffer) => {
  transcriber.sendAudio(chunk);
});

mic.on('close', async () => {
  await transcriber.close();
});

// Handle Ctrl+C
process.on('SIGINT', async () => {
  mic.kill();
  await transcriber.close();
  process.exit(0);
});

Step 3: Browser-Safe Temporary Token

// Server-side: generate a short-lived token for the browser
const token = await client.streaming.createTemporaryToken({
  expires_in_seconds: 300, // 5 minutes
});

// Send `token` to your frontend
// Client-side uses token instead of API key:
// const transcriber = new StreamingTranscriber({ token: receivedToken });

Step 4: Streaming with Word Boost and Speaker Labels

const transcriber = client.streaming.createService({
  speech_model: 'nova-3-pro',
  sample_rate: 16000,
  word_boost: ['AssemblyAI', 'LeMUR', 'transcription'],
  enable_extra_session_information: true,
});

transcriber.on('turn', (turn) => {
  // Speaker-labeled turns (available with nova-3-pro)
  console.log(`Speaker ${turn.speaker}: ${turn.transcript}`);
});

Part 2: LeMUR — LLM-Powered Audio Analysis

Step 5: Summarize a Transcript

// First transcribe (or use an existing transcript_id)
const transcript = await client.transcripts.transcribe({
  audio: 'https://example.com/meeting.mp3',
});

// Summarize with LeMUR
const { response } = await client.lemur.summary({
  transcript_ids: [transcript.id],
  context: 'This is a weekly engineering standup meeting.',
  answer_format: 'bullet points',
});

console.log('Summary:', response);

Step 6: Ask Questions About Audio

const { response: answers } = await client.lemur.questionAnswer({
  transcript_ids: [transcript.id],
  questions: [
    { question: 'What decisions were made?', answer_format: 'list' },
    { question: 'Were there any blockers discussed?', answer_format: 'short sentence' },
    { question: 'Who owns the next action items?', answer_format: 'list' },
  ],
});

for (const qa of answers) {
  console.log(`Q: ${qa.question}`);
  console.log(`A: ${qa.answer}\n`);
}

Step 7: Extract Action Items

const { response: actionItems } = await client.lemur.actionItems({
  transcript_ids: [transcript.id],
  context: 'This is a product planning meeting.',
  answer_format: 'Each action item should include the owner and deadline.',
});

console.log('Action Items:', actionItems);

Step 8: Custom LeMUR Task

const { response } = await client.lemur.task({
  transcript_ids: [transcript.id],
  prompt: `Analyze this customer support call and provide:
    1. Customer sentiment (positive/neutral/negative)
    2. Issue category
    3. Resolution status
    4. CSAT prediction (1-5)
    Format as JSON.`,
});

const analysis = JSON.parse(response);
console.log(analysis);

Step 9: Multi-Transcript Analysis

// LeMUR can analyze up to 100 hours of audio in a single request
const transcriptIds = [
  'transcript-1', 'transcript-2', 'transcript-3',
];

const { response } = await client.lemur.task({
  transcript_ids: transcriptIds,
  prompt: 'Compare themes across these three customer interviews. What patterns emerge?',
});

console.log(response);

Streaming Specifications

Output

• Real-time partial and final transcripts via WebSocket
• Speaker-labeled streaming turns (nova-3-pro)
• LeMUR summaries, Q&A responses, action items
• Custom LLM analysis with structured output

Examples

For live captions, issue a short-lived browser token from an authenticated backend, apply a session duration and concurrency limit, and discard partial text by default after delivery. For LeMUR, submit only approved transcript IDs, constrain the prompt to the stated purpose, validate structured output before use, and record a redacted request receipt rather than the response body.

Error Handling

Resources

• Streaming Speech-to-Text Guide
• LeMUR Documentation
• LeMUR API Reference
• Streaming API Reference

Next Steps

For error troubleshooting, see assemblyai-common-errors.