Generative Audio AI

The Future of
Music Generation

2024 marked a breakthrough: AI can now compose full songs with realistic vocals, coherent lyrics, and professional production. From Suno to MusicGen, explore the state of the art.

Compare Models Evaluation Metrics

Generation Capabilities

4 min
Max Song Length (Suno/Udio)
Full Vocals
AI-Generated Singing
48 kHz
Max Output Quality

How AI Music Generation Works

Modern music generation models use various approaches, from autoregressive transformers to diffusion models. Understanding the architectures helps explain their capabilities and limitations.

Text Input
Step 1: Prompt

Text/Audio Input

Start with a text description ("upbeat pop song about summer") or audio reference for style transfer. Some models also accept melody conditioning.

Generation Process
Step 2: Generation

Neural Music Synthesis

Transformer or diffusion models generate audio tokens (discrete codes representing audio). Multiple stages handle different aspects: structure, melody, vocals, production.

Audio Output
Step 3: Output

Audio Decoding

Tokens are decoded into a waveform using neural vocoders (EnCodec, DAC). Post-processing may enhance quality and ensure seamless audio.

Autoregressive (Suno, MusicGen)

Generate audio tokens one at a time, conditioning each new token on all previous tokens. Similar to how GPT generates text.

  • + Excellent coherence and structure
  • + Can handle long-form content
  • - Sequential generation (slower)
  • - Accumulating errors possible

Diffusion (Stable Audio, Riffusion)

Start with noise and iteratively denoise to create audio. Can work on spectrograms or latent representations.

  • + Parallel generation (faster)
  • + Good for high-fidelity audio
  • - Harder to maintain structure
  • - Fixed output length typical

Model Comparison

Model Quality Vocals Duration Type Year
Suno v3.5
Suno AI
 Excellent Yes 4 min Cloud API 2024
Udio
Udio Inc.
 Excellent Yes 4 min Cloud API 2024
MusicGen Large
Meta
 Good No 30 sec Open Source 2023
Stable Audio 2.0
Stability AI
 Good No 3 min Open Source 2024
AudioCraft
Meta
 Good No 30 sec Open Source 2023
Riffusion
Community
 Fair No 5 sec Open Source 2022

Suno v3.5

Suno AI
Cloud API
Features
Full songs with vocalsLyrics generationStyle transferInpainting
Pros
  • + Best vocal quality
  • + Coherent song structure
  • + Easy to use
Cons
  • - API only
  • - Usage limits on free tier
  • - Training data concerns

Udio

Udio Inc.
Cloud API
Features
High-fidelity vocalsGenre diversityAudio-to-audioRemix
Pros
  • + Exceptional audio quality
  • + Good genre coverage
  • + Creative controls
Cons
  • - API only
  • - Waitlist access
  • - Limited customization

MusicGen Large

Meta
Open Source
Features
Text-to-musicMelody conditioningStereo output
Pros
  • + Fully open source
  • + Runs locally
  • + Good for instrumentals
  • + Melody control
Cons
  • - No vocals
  • - Short clips
  • - Lower quality than Suno/Udio

Stable Audio 2.0

Stability AI
Open Source
Features
Long-form generationAudio-to-audioHigh sample rate
Pros
  • + Open weights
  • + 44.1kHz output
  • + Long generations
Cons
  • - No vocals
  • - Requires GPU
  • - Less coherent than Suno

Evaluation Metrics

Unlike classification tasks with ground truth labels, music generation quality is inherently subjective. The field uses a combination of objective distributional metrics and subjective human evaluation.

FAD

Frechet Audio Distance
0 - 100+

Measures the distance between feature distributions of generated and real music. Lower is better.

SOTA: ~2.0 (best models)
Objective quality assessment

KLD

KL Divergence
0 - inf

Measures how well generated music matches the distribution of real music for classification tasks.

SOTA: ~0.5
Genre/style consistency

MOS

Mean Opinion Score
1.0 - 5.0

Human ratings on a 1-5 scale for naturalness, quality, and musicality.

SOTA: 4.5+ (Suno/Udio)
Subjective quality

CLAP

CLAP Score
0.0 - 1.0

Text-audio alignment score measuring how well the generated music matches the text prompt.

SOTA: ~0.35
Prompt adherence

The Evaluation Problem

Why It's Hard

  • 1. Subjectivity: What makes "good" music varies by genre, culture, and personal taste
  • 2. Multi-faceted: Quality includes melody, harmony, rhythm, production, lyrics, vocals
  • 3. No ground truth: Unlike STT, there's no "correct" output for a prompt
  • 4. Novelty vs Quality: New doesn't mean better; familiar patterns often preferred

Current Best Practices

  • - Use multiple metrics: FAD + MOS + CLAP together
  • - Include human evaluation with diverse raters
  • - Evaluate across multiple genres and prompts
  • - Report win rates vs baselines in A/B tests

Practical Applications

Content Creation

Background music for videos, podcasts, and social media. No licensing fees or copyright concerns with generated music.

Game Development

Adaptive soundtracks that respond to gameplay. Generate variations on themes to avoid repetition in long sessions.

Music Ideation

Artists use AI to quickly prototype ideas, explore new styles, or overcome creative blocks before human refinement.

Which Model Should You Use?

Best Quality

Suno v3.5 / Udio
For professional-quality output with vocals. Best for content that will be published or shared.

Best Open Source

MusicGen Large (Meta)
For local generation, research, or instrumentals. Melody conditioning is unique capability.

Best for Long Form

Stable Audio 2.0
For 3-minute+ ambient or instrumental pieces. High sample rate and open weights.

Contribute to Music AI

Working on new music generation models or evaluation methods? Help the community by sharing your benchmarks and insights.

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