Install Open Source Suno Bark Locally [Guide]

Most people hunting “open source Suno” want the closed music app without the subscription. Wrong target. Bark is Suno’s MIT-licensed text-to-audio model: speech, laughs, SFX, rough musical bits on your GPU. Treat it as local infrastructure, not song-app cosplay.

This guide installs open source Suno Bark from the official GitHub main branch (package metadata: suno-bark0.0.1a in current pyproject.toml, as of the latest main listing), sets VRAM flags, runs a smoke test, and tears the stack down cleanly. Commands first.

System requirements before you touch pip

Skip the table and you eat CUDA OOM or a silent CPU crawl. Numbers below track the official Bark README and the HF hub file tree (hub totals can shift – recheck before you clear a disk).

CPU-only runs. Budget minutes per short clip, not interactive studio pace.

Official download source

Only trust https://github.com/suno-ai/bark. No separate binary release on that repo. First run pulls weights from Hugging Face into ~/.cache/suno/bark_v0 (XDG_CACHE_HOME-aware). Parallel path: Transformers API on the HF Bark model docs (Bark landed in Transformers from 4.31.0).

Install open source Suno Bark step by step

Clean venv. Install Torch for your CUDA stack first so pip does not drop a CPU wheel by accident. Index URLs move – confirm on pytorch.org/get-started.

1. Create and activate an environment:

   python3 -m venv bark-env
   # Linux/macOS:
   source bark-env/bin/activate
   # Windows:
   bark-envScriptsactivate

2. Upgrade pip, then install PyTorch for your platform (pattern only – verify the index for your CUDA):

   pip install --upgrade pip
   pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu126

3. Install Bark from GitHub (supported path):

   pip install git+https://github.com/suno-ai/bark.git

   Or:

   git clone https://github.com/suno-ai/bark
   cd bark
   pip install .

Hard rule from upstream: never pip install bark. That hits a different PyPI project. You want package name suno-bark.

First-time configuration (VRAM flags)

Env vars are read at import time. Set them beforefrom bark import ... or checkpoint choice is already locked.

import os
# PyTorch 2.6+ trusted-checkpoint workaround - see errors section
os.environ["TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD"] = "1"

# Pick based on VRAM:
# 12GB+: leave both unset for full models
os.environ["SUNO_USE_SMALL_MODELS"] = "True" # ~8GB class
os.environ["SUNO_OFFLOAD_CPU"] = "True" # tight cards / ~2GB class path

from bark import SAMPLE_RATE, generate_audio, preload_models
from scipy.io.wavfile import write as write_wav

preload_models() # first run downloads multi-GB weights

Shell note: Windows PowerShell – $env:SUNO_USE_SMALL_MODELS="True" and $env:TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD="1" in the same session before Python. Bash – export both first.

Cache on a full disk? Set XDG_CACHE_HOME to a roomy volume before import so the multi-gig dump skips a tiny home partition.

Verify the install works

Smoke test without a script:

python -m bark --text "Bark install check." --output_filename bark_smoke.wav

Or in Python after the env block:

audio = generate_audio("Install verification. One short sentence.")
write_wav("bark_smoke.wav", SAMPLE_RATE, audio)
print("SAMPLE_RATE", SAMPLE_RATE) # expect 24000
print("wrote bark_smoke.wav", len(audio))

Pass criteria: WAV on disk, 24 kHz (SAMPLE_RATE), no CUDA OOM, no Weights only load failed. First run is download-bound; later runs hit ~/.cache/suno/bark_v0.

Some nights the first clip still coughs, flips accent, or half-sings – even when every install check is green. That’s the generative model, not a broken deploy.

Common install errors and fixes

• ModuleNotFoundError: No module named 'bark' – Script is on a different interpreter than the venv. Activate it; re-check which python / where python.
• pip install bark “works” but the API is wrong – Uninstall that package; install from the GitHub URL above (suno-bark).
• Weights only load failed / numpy.core.multiarray.scalar on PyTorch 2.6+ – PyTorch 2.6+ defaults torch.load(..., weights_only=True). Bark’s older checkpoint paths are not plain state_dicts everywhere, so load blows up. Set TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD=1before import (community workaround while upstream load calls lag the default change; only for checkpoints you trust – official Suno weights). Pinning an older torch is the alternate if you refuse the env override.
• CUDA out of memory during preload_models – SUNO_USE_SMALL_MODELS / SUNO_OFFLOAD_CPU must already be set before import; closing other GPU apps helps fragmented VRAM.
• ImportError: tokenizers>=... but found tokenizers==... – Fresh-env pin fight between transformers and tokenizers (reported on GitHub, e.g. issue threads around #633). Recreate the venv or carefully pip install -U transformers tokenizers until imports succeed.

Upgrade and uninstall

Upgrade (latest main into the env):

pip install --upgrade --force-reinstall git+https://github.com/suno-ai/bark.git

Main is git-tracked more than a polished semver train. Re-run the smoke test after upgrades; pins move.

Uninstall / cleanup:

pip uninstall suno-bark -y
# drop model cache when you want the disk back:
rm -rf ~/.cache/suno/bark_v0
# Windows: %USERPROFILE%.cachesunobark_v0

Delete the venv folder if it existed only for Bark.

Native package fighting your stack? Use Transformers (BarkModel / AutoProcessor on suno/bark or suno/bark-small) per the HF docs – half-precision and enable_cpu_offload() are first-class there. Lineage note: Bark sits near work such as AudioLM (arXiv:2209.03143). MIT commercial use was announced 2023-05-01 on the repo updates log.

FAQ

Is Bark the same as Suno’s music app?

No. Bark is the open text-to-audio research model. Suno’s consumer music product is separate and closed.

Which VRAM flags should I set on an 8 GB card?

Set SUNO_USE_SMALL_MODELS=True before any Bark import – that is the README path for roughly 8 GB. Still OOM? Add SUNO_OFFLOAD_CPU=True (slower, keeps the pipeline alive). Short voiceover lines for a local tool prototype on an RTX 4060-class card usually clear with small models alone.

Why did install succeed but load fail after a PyTorch upgrade?

Almost always the 2.6 weights_only default colliding with Bark’s load path. Use the env override in the errors section (trusted official weights only), or pin torch. Not an audio-quality bug.

Next: activate the venv, export the VRAM flags for your card plus the PyTorch override if you are on 2.6+, run python -m bark --text "Bark is live." --output_filename bark_smoke.wav, open the WAV. If it plays, you are deployed. Worth a second listen for weird non-speech artifacts – those are model quirks, not proof the install failed.