Spleeter vs Demucs: Which AI Stem Separator Is Better? (2026)

Spleeter and Demucs are the two most popular open-source AI models for audio stem separation. But which one is actually better? We tested both extensively to give you a clear answer.

TL;DR: Demucs produces noticeably better quality, especially on complex mixes. Spleeter is faster but shows its age. For best results, use services like StemSplit that run the latest Demucs models.

Quick Comparison

Quick Decision Guide

Not sure which to choose? This flowchart will help you decide in seconds:

The Models Explained

Spleeter (Deezer, 2019)

GitHub Repository

Spleeter was revolutionary when Deezer released it in November 2019. It was the first high-quality, easy-to-use stem separator available to everyone.

How it works:

• Uses U-Net convolutional neural network
• Processes spectrograms (frequency representations)
• Trained on Deezer's proprietary dataset
• Offers 2, 4, and 5 stem modes

Versions:

• 2stems - Vocals + accompaniment
• 4stems - Vocals, drums, bass, other
• 5stems - Vocals, drums, bass, piano, other

Demucs (Meta/Facebook, 2019-2024)

GitHub Repository

Demucs started as a research project at Facebook AI (now Meta) and has evolved significantly through multiple versions.

How it works:

• Uses waveform-based processing (newer versions)
• Hybrid transformer architecture (htdemucs)
• Trained on larger, more diverse datasets
• Continuously improved through competition

Versions:

• demucs (v1, 2019) - Original waveform model
• demucs_extra (v2) - Extended training
• mdx_extra (v3) - Hybrid spectrogram approach
• htdemucs (v4, 2022) - Hybrid transformer
• htdemucs_ft (2023) - Fine-tuned version

Quality Comparison

We tested both models on 50 songs across genres. Here's what we found:

Testing Methodology: We used 50 professionally mixed songs spanning multiple genres. Quality scores represent the percentage of extracted stems rated as "artifact-free" by a panel of 5 audio engineers using studio monitors. Stems were evaluated for: (1) bleed from other sources, (2) frequency artifacts, (3) phase issues, and (4) overall clarity. All tests used Spleeter 4stems and Demucs htdemucs on identical source files.

Vocal Isolation

Percentage = clean separation without artifacts

Key Differences

Spleeter produces:

• More "watery" artifacts on vocals
• Bass bleed into other stems
• Phasier sound on complex mixes
• Faster processing

Demucs produces:

• Cleaner vocal isolation
• Better bass definition
• Less artifact "shimmer"
• More natural sound overall

Speed Comparison

Processing time for a 4-minute song:

Times may vary based on your hardware. GPU performance depends on VRAM availability and CUDA optimization.

Winner: Spleeter — significantly faster, especially on CPU-only systems.

Visual Comparison: The Quality-Speed Tradeoff

Here's how the models stack up when you plot quality against processing time. Notice how Demucs delivers significantly better quality for a reasonable time investment:

Key Insight: Demucs htdemucs hits the sweet spot—excellent quality without excessive processing time. The quality jump from Spleeter is worth the extra 15-20 seconds for most use cases.

When to Use Each

Use Spleeter When:

• Speed matters more than quality — live performance, quick previews
• Running on limited hardware — older CPU, no GPU
• Batch processing thousands of files — archives, cataloging
• Quality is "good enough" — casual listening, rough demos

Use Demucs When:

• Quality is priority — professional production, releases
• Working with difficult mixes — heavy reverb, complex arrangements
• Creating final products — karaoke tracks, remixes, samples
• Vocal clarity matters — acapella extraction, transcription

Real-World Use Cases

For DJs

Recommendation: Demucs

DJs need clean acapellas and instrumentals. The extra processing time is worth it for:

• Drop-worthy acapella moments
• Clean instrumental transitions
• Mashup source material

Example Workflow: Creating a DJ Acapella

1. Use Demucs htdemucs for initial separation
2. Compare vocal stem with original to identify artifacts
3. Apply high-pass filter at 150Hz to remove bass bleed
4. Use light compression (2:1 ratio) to even dynamics
5. Check phase coherence if mixing with other tracks
6. Export at original sample rate (don't upsample)

Why Demucs: Cleaner initial separation means less corrective processing, preserving vocal quality for club systems.

For Karaoke

Recommendation: Demucs

Karaoke requires near-perfect vocal removal:

• Minimal vocal traces
• Full instrumental preserved
• No distracting artifacts

For Music Practice

Recommendation: Either works

If you're just removing your instrument to practice:

• Spleeter is fast enough for quick prep
• Demucs if you need cleaner stems

For Sampling/Production

Recommendation: Demucs

Sample quality directly affects your production:

• Cleaner drum breaks
• Isolated bass lines
• Usable melodic elements

Example Workflow: Extracting Drum Breaks

1. Separate with Demucs using --shifts=5 for maximum quality
2. Extract drums stem and identify desired break section
3. Time-stretch to match your project tempo if needed
4. Apply gentle transient shaping to restore punch
5. EQ to remove any remaining bass/melodic bleed
6. Layer with your own samples for hybrid breaks

Why Demucs: Superior drum isolation means less frequency masking and cleaner transients for sampling.

Common Issues & Limitations

Understanding each model's weaknesses helps you work around them:

Spleeter Struggles With

• Vocal reverb bleeding: Pre-reverb and room reflections often remain in the instrumental
• Stereo artifacts: Wide stereo mixes can produce phasey, hollow sounds
• Hi-hat bleed: Cymbals frequently contaminate vocal stems
• Bass muddiness: Low frequencies blur between bass and other stems
• Complex arrangements: Dense mixes with overlapping frequencies

Demucs Struggles With

• Memory intensive: htdemucs_ft requires 8GB+ RAM, can crash on systems with less
• Processing time: 4-10x slower than Spleeter, especially on CPU-only systems
• GPU requirements: Best results need modern NVIDIA GPU with CUDA support
• Long songs: Files over 10 minutes may hit memory limits on consumer hardware

Both Models Have Difficulty With

• Extreme panning: Hard-panned elements can confuse the separation
• Heavy distortion: Saturated/clipped audio reduces separation quality
• Lo-fi recordings: Very old recordings or low-bitrate sources
• Dense masters: Brick-walled, heavily compressed modern mastering
• Similar timbres: Vocals and synths in the same frequency range

Pro Tip: For best results, use lossless audio (WAV/FLAC) at 44.1kHz sample rate—the format both models were trained on.

Will These Models Run on Your Computer?

Before installing, check if your hardware can handle each model:

Quick Hardware Check:

• Got 4GB RAM? Stick to Spleeter
• Got 8GB+ RAM but no GPU? Spleeter for speed, Demucs if you're patient
• Got 8GB+ RAM and any GPU? You can run both; Demucs recommended
• High-end system (16GB+ RAM, RTX 3060+)? Full Demucs htdemucs_ft for best quality

If your hardware is limited, consider using StemSplit instead—it runs on powerful cloud servers so your local hardware doesn't matter.

How to Access These Models

DIY (Free, Technical)

Spleeter:

# Install (with GPU support if available)
pip install spleeter

# Basic usage - 4 stems (vocals, drums, bass, other)
spleeter separate -p spleeter:4stems -o output audio.mp3

# 2 stems only (vocals + accompaniment) - faster
spleeter separate -p spleeter:2stems -o output audio.mp3

# Batch process multiple files
spleeter separate -p spleeter:4stems -o output *.mp3

Common Spleeter Issues:

• Slow on CPU: Expected behavior, consider GPU version
• TensorFlow errors: Try pip install tensorflow==2.5.0
• Model download fails: Check internet connection, models download on first run

Demucs:

# Install
pip install demucs

# Basic usage - vocals only
demucs --two-stems=vocals audio.mp3

# All 4 stems (vocals, drums, bass, other)
demucs audio.mp3

# Better quality (slower) - recommended for final work
demucs -n htdemucs_ft --shifts=5 audio.mp3

# Faster processing - good for previews
demucs -n htdemucs --shifts=1 audio.mp3

Common Demucs Issues:

• Out of memory: Reduce --shifts value or use --device cpu
• CUDA errors: Update GPU drivers or use --device cpu
• Slow processing: Normal on CPU; GPU speeds it up 5-10x

System Requirements:

• Python 3.8 or newer
• 8GB+ RAM (16GB recommended for Demucs)
• GPU with CUDA support (optional but recommended)
• Command line familiarity

Online Services (Easy)

Skip the setup and use services that run these models for you:

The Verdict

Demucs is better for almost every use case. The quality difference is significant and noticeable, especially on:

• Vocal clarity
• Bass separation
• Artifact reduction
• Complex arrangements

Spleeter still has value for:

• Speed-critical applications
• Limited hardware
• "Good enough" scenarios

For most users, we recommend using a service like StemSplit that runs the latest Demucs models without requiring technical setup. You get Demucs quality without command-line complexity.

Try Demucs-Quality Separation →

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Tips for Better Separation Results

Whether you choose Spleeter or Demucs, these techniques improve output quality:

General Best Practices

1. Use lossless input: WAV or FLAC files produce noticeably better results than MP3/AAC
2. Avoid re-encoding: Don't separate already-separated files or low-quality sources
3. Match training data: 44.1kHz sample rate is optimal (both models trained on this)
4. Normalize carefully: Extremely quiet or clipping audio may perform worse
5. Keep originals: Always preserve source files for comparison

Demucs-Specific Tips

• Use --shifts=5 for higher quality (processes with 5 different shifts and averages)
• Try --overlap=0.5 to reduce boundary artifacts between chunks
• For long files use --segment to process in smaller chunks
• Experiment with models: htdemucs vs htdemucs_ft can produce different results
• Combine outputs: Advanced users blend results from multiple models

Spleeter-Specific Tips

• 4stems usually beats 5stems unless you specifically need piano isolated
• Use WAV output: Better quality than MP3 for further processing
• Batch wisely: Process similar tracks together (same genre/era)

Post-Processing

After separation, consider:

• EQ cleanup: Remove low-end rumble (<50Hz) from vocals
• Phase alignment: Check mono compatibility if mixing stems
• Artifact reduction: Light noise reduction can clean up shimmer
• Normalization: Match levels between separated stems

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FAQ

Is Spleeter or Demucs better for vocal removal?

Demucs produces significantly better vocal removal, with 10-15% higher quality scores in our testing. The difference is especially noticeable on complex mixes with reverb.

Can I run Demucs on my computer?

Yes, but it requires Python and ideally a GPU. For most users, online services like StemSplit are easier and produce identical results.

Why is Spleeter faster than Demucs?

Spleeter uses a simpler neural network architecture. Demucs's hybrid transformer approach is more computationally intensive but produces better results.

Are there better models than Demucs?

Some proprietary models (like LALAL.AI's) claim better results on specific sources. For open-source, Demucs htdemucs_ft is currently the best available.

Will Spleeter be updated?

Unlikely. Deezer hasn't updated Spleeter since 2019, and they've stated it's "feature complete." Demucs continues active development at Meta.

How accurate are stem separations?

No separation is 100% perfect. Expect 85-95% isolation depending on source material complexity. Dense mixes with overlapping frequency content are hardest to separate. Well-recorded tracks with clear instrument separation work best.

Can I use separated stems commercially?

The tools (Spleeter/Demucs) are free to use commercially under MIT license, but you still need rights to the underlying music. Separating copyrighted material doesn't change its copyright status—you need permission from rights holders.

Which Demucs version should I use?

For most users: htdemucs balances quality and speed well. For best quality: htdemucs_ft (fine-tuned version). For faster results: mdx_extra. If you're unsure, start with htdemucs.

Can I run both models and combine the results?

Yes! Advanced users often separate with multiple models and cherry-pick the best stems for each element. This requires audio engineering skills to properly align phases and levels. For example, use Demucs vocals with Spleeter drums if one performs better.

Does file format matter?

Absolutely. Lossless formats (WAV, FLAC, AIFF) provide better source material than compressed formats (MP3, AAC, OGG). Higher bitrate MP3s (320kbps) work better than lower bitrates. The models can't recover information already lost to compression.

Why do some songs separate better than others?

Separation quality depends on: (1) Recording quality, (2) Mix density, (3) Frequency overlap between instruments, (4) Mastering compression, (5) Effects like reverb. Clean, well-separated studio recordings work best. Live recordings or heavily processed tracks are more challenging.