The Future of Multimodal AI: Text, Audio, Image, and Video

import { Deepgram } from '@deepgram/sdk'

const deepgram = new Deepgram(process.env.DEEPGRAM_API_KEY)

// Transcribe audio file
const transcription = await deepgram.transcription.preRecorded({
  url: 'https://example.com/audio.mp3'
}, {
  punctuate: true,
  language: 'en',
  model: 'nova-2'
})

console.log(transcription.results.channels[0].alternatives[0].transcript)

import { ElevenLabs } from 'elevenlabs-node'

const elevenlabs = new ElevenLabs({ apiKey: process.env.ELEVENLABS_KEY })

// Generate speech from text
const audio = await elevenlabs.textToSpeech({
  text: 'Hello, this is a test of text to speech.',
  voiceId: 'pNInz6obpgDQGcFmaJgB',  // Adam voice
  stability: 0.75,
  similarityBoost: 0.75
})

// Save or stream audio
await fs.writeFile('output.mp3', audio)

import OpenAI from 'openai'

const openai = new OpenAI()

// Analyze image with GPT-4V
const response = await openai.chat.completions.create({
  model: 'gpt-4-vision-preview',
  messages: [{
    role: 'user',
    content: [
      { type: 'text', text: 'What's in this image?' },
      { type: 'image_url', image_url: { url: 'https://example.com/image.jpg' } }
    ]
  }]
})

console.log(response.choices[0].message.content)

// Generate image with DALL-E 3
const image = await openai.images.generate({
  model: 'dall-e-3',
  prompt: 'A serene landscape with mountains and a lake at sunset',
  size: '1024x1024',
  quality: 'hd'
})

console.log(image.data[0].url)

// Coming soon: Video understanding models

const analysis = await gemini.analyzeVideo({
  url: 'https://example.com/video.mp4',
  tasks: ['summarize', 'extract_key_frames', 'detect_objects']
})

console.log(analysis)
// {
//   summary: 'A tutorial on how to cook pasta...',
//   keyFrames: ['00:15', '01:30', '03:45'],
//   objects: ['pot', 'pasta', 'chef', 'kitchen']
// }

interface AudioPreprocessing {
  // Voice Activity Detection (VAD)
  detectSpeech: (audio: Buffer) => { start: number; end: number }[]

  // Noise Reduction
  reduceNoise: (audio: Buffer) => Buffer

  // Format Conversion
  convert: (audio: Buffer, format: 'mp3' | 'wav' | 'ogg') => Buffer

  // Audio Normalization
  normalize: (audio: Buffer) => Buffer
}

// Example: Preprocess audio before transcription
async function preprocessAudio(audioFile: Buffer): Promise<Buffer> {
  // 1. Detect speech segments
  const segments = await detectSpeech(audioFile)

  // 2. Remove silence
  const trimmed = await trimSilence(audioFile, segments)

  // 3. Reduce background noise
  const cleaned = await reduceNoise(trimmed)

  // 4. Normalize volume
  const normalized = await normalizeAudio(cleaned)

  // 5. Convert to optimal format
  return await convertToFormat(normalized, 'wav')
}

// Resize images to optimal dimensions
async function preprocessImage(imageUrl: string): Promise<Buffer> {
  const image = await fetch(imageUrl).then(r => r.arrayBuffer())

  return await sharp(image)
    .resize(1024, 1024, { fit: 'inside' })  // Max 1024x1024
    .jpeg({ quality: 90 })  // Compress to JPEG
    .toBuffer()
}

// Extract key frames from video
async function extractKeyFrames(videoUrl: string): Promise<string[]> {
  // 1. Download video
  const video = await downloadVideo(videoUrl)

  // 2. Extract frames at intervals
  const frames = await extractFrames(video, { interval: 5 })  // Every 5 seconds

  // 3. Detect scene changes
  const keyFrames = await detectSceneChanges(frames)

  // 4. Upload frames for analysis
  return await uploadFrames(keyFrames)
}

// Strategy 1: Extract audio from video, transcribe, then analyze text
const videoUrl = 'https://example.com/lecture.mp4'

// Instead of analyzing video directly ($0.20/min)
const videoAnalysis = await gemini.analyzeVideo(videoUrl)  // $2.00 for 10-min video

// Do this:
const audio = await extractAudio(videoUrl)  // Free
const transcript = await deepgram.transcribe(audio)  // $0.043 for 10-min audio
const summary = await gpt4.summarize(transcript.text)  // $0.05 for summarization

// Total: $0.093 (instead of $2.00) = 95% savings

// Begin with simple multimodal features
async function analyzeProductImage(imageUrl: string): Promise<{
  category: string
  tags: string[]
  description: string
}> {
  const response = await openai.chat.completions.create({
    model: 'gpt-4-vision-preview',
    messages: [{
      role: 'user',
      content: [
        { type: 'text', text: 'Analyze this product image. Return category, tags, and description.' },
        { type: 'image_url', image_url: { url: imageUrl } }
      ]
    }]
  })

  return JSON.parse(response.choices[0].message.content)
}

// Track costs by modality
await trackMultimodalCost({
  modality: 'image',
  model: 'gpt-4-vision',
  cost: 0.01,
  inputSize: 512 * 512
})

// Cache expensive multimodal analysis
const cacheKey = hashImage(imageUrl)
const cached = await cache.get(cacheKey)

if (cached) return cached

const analysis = await analyzeImage(imageUrl)
await cache.set(cacheKey, analysis, 3600)  // Cache 1 hour

return analysis