latentscore 0.1.1

LatentScore audio synthesis library.

LatentScore

⚠️ Alpha: This library is under active development. API may change between versions.

Generate ambient music from text descriptions. Locally. No GPU required.

Read more about how it works here.

import latentscore as ls

ls.render("warm sunset over water").play()

Install

Conda

conda create -n latentscore python=3.10
conda activate latentscore
conda install pip

pip install latentscore

Pip

python -m venv .venv
source .venv/bin/activate

pip install latentscore

Requires Python 3.10. If you don't have it: brew install python@3.10 (macOS) or pyenv install 3.10

Usage

import latentscore as ls

# Render and play
audio = ls.render("warm sunrise over water")
audio.play()
audio.save("output.wav")

Streaming

import latentscore as ls

# Stream a single vibe
ls.stream("warm sunset over water", duration=120).play()

# Stream multiple vibes with crossfade
ls.stream(
    "morning coffee",
    "afternoon focus", 
    "evening wind-down",
    duration=60,
    transition=5.0,
).play()

Async Streaming

import latentscore as ls
import asyncio

async def main():
    items = [
        ls.Streamable(content="morning coffee", duration=30),
        ls.Streamable(content="afternoon focus", duration=30),
    ]
    async for chunk in ls.astream(items):
        # Process chunks as they arrive
        print(f"Got {len(chunk)} samples")

asyncio.run(main())

Playlists

import latentscore as ls

playlist = ls.Playlist(tracks=(
    ls.Track(content="morning energy", duration=60),
    ls.Track(content="deep focus", duration=120),
    ls.Track(content="evening calm", duration=60),
))
playlist.play()

Modes

• fast (default): Embedding lookup. Instant.
• expressive: Local LLM. Slower, more creative. Run latentscore download expressive first.
• external: Route through Claude, Gemini, etc. Best quality, needs API key.

# Use expressive mode
ls.render("jazz cafe at midnight", model="expressive").play()

# Use external LLM
ls.render(
    "cyberpunk rain",
    model="external:gemini/gemini-3-flash-preview",
    api_key="..."
).play()

CLI

latentscore demo                  # Generate and play a sample
latentscore download expressive   # Fetch local LLM weights
latentscore doctor                # Check setup

Architecture

See docs/architecture.md for the data_work pipeline map and environment notes.

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Research & Training Pipeline (data_work/)

The data_work/ folder hosts the full research + training pipeline (data prep, SFT/GRPO on Modal, CLAP benchmarking, eval suites, exports).
If you want anything beyond the core library, start here:

• data_work/README.md
• docs/architecture.md

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Contributing

See CONTRIBUTE.md for environment setup and contribution guidelines.

See docs/coding-guidelines.md for code style requirements.