Audiyo

Small Python library to run Stable Audio Open and MiniMax-Music3, and fine-tune both with LoRA.

It wraps the Diffusers pipelines with input checks, seeded output, memory presets, and a LoRA training path.

Supported checkpoints:

• stabilityai/stable-audio-open-1.0 (stereo, 44100 Hz, up to about 47 seconds).
• TeamAudiyo/Minimax-Music3-GGUF — MiniMax-Music3 with quantized GGUF DiT weights (default q4_k_m, 1.49 GB of weights, about 9 to 10 GB free VRAM and 14 to 16 GB system RAM, fits a 15 GB card).
• TeamAudiyo/Minimax-Music3-GGUF:MiniMax-Music3-Q4_K_M.gguf — pick a specific quant (q3_k_m, q4_k_m, q5_k_m, q6_k, q8_0, f16).
• MiniMaxAI/MiniMax-Music3 — full bf16 checkpoint (needs diffusers>=0.40 and a CUDA GPU; ~20.6 GB sequential, ~10.3 GB with the LLM in 8-bit).

Install

Needs Python 3.10 or newer and a Hugging Face account with access to the checkpoint.

pip install audiyo
pip install audiyo[train]

One install covers generation and LoRA fine-tuning. The [train] extra only adds audio resampling.

Accept the license at https://huggingface.co/stabilityai/stable-audio-open-1.0, then log in:

huggingface-cli login

You can also set HF_TOKEN in your environment.

Command line

audiyo info
audiyo hardware
audiyo presets
audiyo generate "Rain against a window" -o rain.wav --duration 10 --seed 42
audiyo generate "Rain on a roof" --seeds 1,2,3 -o rain.wav
audiyo quality rain.wav
audiyo compare before.wav after.wav
audiyo sheet ./clips --out sheet.csv
audiyo estimate --memory-mode balanced
audiyo ui
audiyo finetune my_data -o my_adapter --max-steps 200
audiyo adapters my_adapter/adapter
audiyo benchmark --duration 10 --steps 20

Run audiyo with no args, or audiyo chat, for an interactive menu. Quit with 6 or Ctrl-C.

📊 VRAM Benchmarks (Tesla T4 GPU / bfloat16)

Tested on stabilityai/stable-audio-open-1.0 (44.1kHz Stereo, 10s audio generation):

Generate music with MiniMax-Music3

Songs from lyrics plus a description, with the DiT loaded from quantized GGUF weights:

from audiyo import AudioModel

model = AudioModel.from_pretrained(
    "TeamAudiyo/Minimax-Music3-GGUF",
    device="auto",
    memory_mode="balanced",
)

result = model.generate(
    prompt="upbeat synth-pop with bright analog arps",
    lyrics="[verse]\nMorning light\n[chorus]\nWe run the night",
    duration_seconds=30,
    seed=42,
)
result.save("song.wav")

Lyrics follow the MiniMax tag format: section tags like [verse] sit alone on their own line. Songs run up to about six minutes. The 8B LLM, DiT, and vocoder swap in and out of GPU RAM sequentially, so a 6GB card handles the default q4_k_m quant.

Dual-component LoRA fine-tuning trains either the LLM composer (target="llm"), the DiT voice (target="transformer"), or both (target="both"); base weights stay frozen:

report = model.finetune(dataset="my_music", target="both", output_dir="my_adapter", max_steps=200)
model.load_adapter("my_adapter")

Generate audio

from audiyo import AudioModel

model = AudioModel.from_pretrained(
    "stabilityai/stable-audio-open-1.0",
    device="auto",
    memory_mode="balanced",
)

result = model.generate(
    prompt="Rain against a window with distant thunder",
    duration_seconds=10,
    seed=42,
)
result.save("rain.wav")

Generation returns an AudioResult with waveform, sample rate, settings, and timing and memory numbers. save() writes audio as produced unless you pass normalize=True.

Fine-tune with LoRA

Make a folder of audio clips with matching captions:

my_data/rain.wav
my_data/rain.txt
my_data/cafe.wav
my_data/cafe.txt

Then:

report = model.finetune(
    dataset="my_data",
    output_dir="my_adapter",
    max_steps=200,
    rank=16,
)
model.load_adapter("my_adapter/adapter")

Adapters include audiyo_adapter.json with base model id, rank, and target modules. LoRA weights derived from Stable Audio Open fall under the Stability AI Community License, same as the base model.

Memory modes

Check what a loaded model uses:

print(model.describe_memory())

Memory is reported before and after a run. See docs/memory.md.

Docs

• docs/quickstart.md - install and first generation
• docs/generation.md - variations, fades, formats, config files
• docs/quality.md - check, compare, and listening sheets
• docs/ui.md - web demo, config files, size check
• docs/memory.md - what each preset changes
• docs/training.md - dataset format and LoRA notes
• docs/benchmarking.md - how to run fair comparisons
• docs/troubleshooting.md - common errors
• docs/backends.md - the backend dispatcher and evaluated models
• docs/api/ - module map for the source tree
• docs/testkit.md - small test model for local development
• CONTRIBUTING.md - how to contribute
• SECURITY.md - privacy and how to report issues
• COMMUNITY.md - how we treat each other
• PLAN.md - decisions made before coding

Licenses

Audiyo code is Apache License 2.0, see LICENSE. Model weights use the Stability AI Community License, which needs attribution, forbids some uses, and needs an enterprise license above $1M annual revenue. Audiyo never bundles weights. Each user downloads them after accepting the gate.

Status

Version 0.3.5: the music loader now switches torchvision checks off before diffusers loads, so broken vision installs cannot break audio. Fast tests run without any checkpoint.