arioso

Unified Python facade for AI music generation.

One interface, many backends. Arioso wraps 16 AI music generation platforms — from local open-source models to commercial REST APIs — behind a single generate() call.

Install

pip install arioso

Quick start

import arioso

# Generate music (defaults to MusicGen, runs locally)
song = arioso.generate("upbeat jazz piano")

# The result is a Song object with audio data
song.audio.audio_array  # numpy array (for local models)
song.audio.sample_rate  # e.g. 32000

# Use a different platform
song = arioso.generate("epic orchestral soundtrack", platform="elevenlabs", duration=30)
song.audio.audio_bytes  # MP3 bytes

Enhance existing audio (audio-to-audio)

Beyond text-to-music, arioso can transform audio you already have -- e.g. turn a plain synthesized/"MIDI" render into something richer, or re-generate music that follows an input melody. This is the enhance() entry point:

import arioso

rendered = (
    ...
)  # a Song, a file path, bytes, an (array, sample_rate) pair, or a numpy waveform

# Audio-to-audio: continue/transform the input (Stable Audio Open, local)
better = arioso.enhance(rendered, "warm analog studio band", platform="stable_audio")

# Subtle variation: low strength keeps the input, high lets the prompt take over
# (Stable Audio 2.5 on fal.ai; needs FAL_KEY)
close = arioso.enhance(
    rendered, "full symphony orchestra", platform="stable_audio_25", strength=0.3
)

# Remix toward style tags (ACE-Step on fal.ai)
remix = arioso.enhance(rendered, "orchestral, film score, brass", platform="ace_step")

# Melody-conditioned: keep the tune, take the style from the prompt (MusicGen-melody)
variation = arioso.enhance(rendered, "energetic EDM", platform="musicgen")

# Discover which platforms accept input audio
arioso.supports_audio_input("stable_audio")  # True
arioso.supports_audio_input("mubert")  # False

enhance(audio, prompt='', *, platform='stable_audio', strength=None, as_='auto') routes your audio into the platform's audio-conditioning affordance (audio_input, melody, or reference_audio) and generates. Input audio is accepted in any form a Song/AudioResult/bytes/path/(array, sample_rate)/ numpy-waveform can take (normalized internally). Note that stable_audio via diffusers uses the input as an initial waveform (continuation/init) and has no strength knob (a value passed there is ignored with a warning); for a strength-controlled transform use stable_audio_25.

Platforms

16 platforms are included, spanning local models, REST APIs, and SDK-based services:

# See what's available
arioso.list_platforms()
# ['beatoven', 'elevenlabs', 'harmonai', 'jen', 'loudly', 'lyria2', ...]

# Inspect a platform's configuration
arioso.get_platform_info("musicgen")

MusicGen (local, no API key)

Runs on your machine. Tries audiocraft first, falls back to HuggingFace transformers.

song = arioso.generate(
    "chill lofi beats",
    platform="musicgen",
    duration=10,
    temperature=0.8,
    guidance=3.0,
    model="facebook/musicgen-small",  # or medium, large, melody
)

ElevenLabs

Needs ELEVENLABS_API_KEY environment variable.

song = arioso.generate(
    "dramatic film score",
    platform="elevenlabs",
    duration=60,
    instrumental=True,
    output_format="mp3_44100_128",
)

# With lyrics
song = arioso.generate(
    "pop ballad",
    platform="elevenlabs",
    lyrics="[Verse]\nWalking through the rain\n[Chorus]\nI found my way home",
    title="Coming Home",
)

Suno (via sunoapi.org)

Needs SUNO_API_KEY environment variable.

songs = arioso.generate_many(
    "summer reggae vibes",
    platform="sunoapi",
    genre="reggae, tropical",
    instrumental=True,
)
# Suno returns 2 songs per call
for song in songs:
    print(song.title, song.audio_url)

Parameters

All platforms share a common vocabulary of parameter names. Use any that the platform supports — unsupported ones are warned about and ignored.

song = arioso.generate(
    "ambient soundscape",  # prompt (required)
    platform="musicgen",
    duration=15,  # seconds
    temperature=1.2,  # sampling randomness
    top_k=250,  # top-k sampling
    guidance=3.0,  # classifier-free guidance
    seed=42,  # reproducibility
)

The full set of unified parameter names (42):

Each platform maps these to its native parameter names automatically. For example, instrumental=True becomes make_instrumental=True for Suno and force_instrumental=True for ElevenLabs.

Output

Every call returns a Song object:

song = arioso.generate("jazz piano", platform="musicgen")

song.status  # 'complete'
song.platform  # 'musicgen'
song.title  # ''
song.metadata  # {'model': 'facebook/musicgen-small', ...}

# Audio is in song.audio (an AudioResult)
song.audio.audio_array  # numpy array (local models)
song.audio.audio_bytes  # raw bytes (REST APIs)
song.audio.audio_url  # URL string (Suno)
song.audio.sample_rate  # e.g. 32000
song.audio.format  # 'wav', 'mp3', etc.

# Shortcuts
song.audio_array  # same as song.audio.audio_array
song.audio_bytes  # same as song.audio.audio_bytes
song.sample_rate  # same as song.audio.sample_rate

Use generate_many() when you want all results (some platforms return multiple):

songs = arioso.generate_many("pop song", platform="sunoapi")
# Returns list[Song]

Adding a new platform

Arioso uses a plugin architecture. Each platform is a subfolder under arioso/platforms/ with two files:

arioso/platforms/myplatform/
    __init__.py
    config.py        # required: declares PLATFORM_CONFIG
    adapter.py       # optional: custom generation logic

Minimal example (REST API)

For a REST API, you may only need config.py:

# arioso/platforms/myplatform/config.py

PLATFORM_CONFIG = {
    "name": "myplatform",
    "display_name": "My Platform",
    "website": "https://myplatform.com",
    "tier": "simple",
    "access_type": "rest_api",
    "auth": {
        "type": "bearer_token",
        "env_var": "MYPLATFORM_API_KEY",
    },
    "param_map": {
        "prompt": {"native_name": "text", "required": True},
        "duration": {"native_name": "length_seconds"},
    },
    "supported_affordances": ["prompt", "duration"],
    "on_unsupported_param": "warn",
    "output": {
        "default_format": "mp3",
        "sample_rate": 44100,
        "returns": "bytes",
    },
    "api": {
        "base_url": "https://api.myplatform.com",
        "generate_endpoint": {"method": "post", "path": "/v1/generate"},
    },
}

The platform is auto-discovered on the next arioso.list_platforms() call.

Custom adapter (Python library)

For platforms that are Python libraries rather than REST APIs, add an adapter.py:

# arioso/platforms/myplatform/adapter.py

from arioso.base import Song, AudioResult


class Adapter:
    def __init__(self, config):
        self.config = config

    def generate(self, prompt, *, duration=10, **kwargs):
        # Your generation logic here
        from some_library import generate_audio

        audio = generate_audio(prompt, length=duration)

        return Song(
            audio=AudioResult(audio_array=audio, sample_rate=44100, format="wav"),
            platform="myplatform",
            status="complete",
        )

Manual registration

Third-party packages can register platforms at runtime:

from arioso.registry import register_platform

register_platform("custom", my_config_dict, my_adapter_instance)

Platforms Without Public APIs

The following platforms were considered during the design of arioso's unified parameter vocabulary and affordance system, but do not have public APIs (nor a reliable third-party API wrapper). They are listed here for completeness.

Research & Background

Arioso's design is informed by extensive research into the AI music generation landscape. Two reference documents in misc/docs/ provide the full background:

Platform comparison and API landscape

AI Music Generation Tools: A Unified API Reference for 21 Platforms maps the full union of capabilities across every tool we investigated. Highlights:

• General characteristics comparison — pricing, API availability, max length, sample rate, vocal support, and quality tier for 18 tools
• Documentation and integration links — docs URLs, OpenAPI specs, Python/JS SDKs, and MCP servers per platform
• Feature affordance matrix — which platforms support text prompts, negative prompts, lyrics, audio conditioning, melody conditioning, duration, BPM, key, genre tags, energy, instruments, and more
• Unified affordance mapping — the 40 common parameter names that arioso exposes, with native parameter names per platform
• Complete API parameter catalogs — full endpoint and parameter documentation for Suno, ElevenLabs, MusicGen, Lyria RealTime, Stable Audio Open, and YuE

Prompt engineering across platforms

Prompt Engineering for Music AI Generation: A Resource Guide compiles 78 resources (papers, datasets, tools, taxonomies, and guides) covering how to write effective prompts for text-to-music systems. Key sections:

• Papers on prompt engineering techniques — chain-of-thought prompting, prompt tuning, user studies, and structured prompting strategies
• Model architecture and text encoders — how T5, CLAP, FLAN-T5, and MuLan encoders determine which prompt styles work best
• Text-audio alignment models — CLAP, MuLan, LAION-CLAP, and how they shape the vocabulary space
• CFG and parameter interactions — how guidance scale, temperature, and other parameters interact with text prompts
• Captioning datasets defining vocabulary — MusicCaps, LP-MusicCaps, MusicBench, and 11 other datasets that trained these models
• Vocabulary and taxonomy resources — AudioSet ontology, Music Ontology, Cyanite AI taxonomy
• Platform-specific prompt guides — official and community guides for Stable Audio, Udio, Lyria, Suno, ElevenLabs, and more

Each platform also has a <PLATFORM>_REFERENCE.md in its directory under arioso/platforms/ with platform-specific prompt engineering guidance, API details, and links to further reading.

Architecture

arioso/
    __init__.py          # Facade: generate(), list_platforms()
    base.py              # Song, AudioResult, AFFORDANCES (42 unified params)
    registry.py          # Auto-discovery, lazy loading, manual registration
    translation.py       # Parameter renaming & coercion (common -> native)
    _util.py             # Auth helpers, HTTP session factory

    platforms/
        _base_adapter.py   # BaseRestAdapter (shared REST infrastructure)
        musicgen/          # Local inference via audiocraft/transformers
        stable_audio/      # Local inference via diffusers
        harmonai/          # Unconditional generation via Dance Diffusion
        riffusion/         # Spectrogram-based via diffusers
        elevenlabs/        # REST with OpenAPI spec via ho
        sunoapi/           # REST via sunoapi.org
        lyria2/            # Google Vertex AI REST
        lyria_rt/          # Google Lyria RealTime WebSocket
        mubert/            # Mubert REST API
        beatoven/          # Beatoven.ai REST API
        loudly/            # Loudly REST API
        jen/               # Jen REST API
        yue/               # YuE via fal.ai or local CLI
        stable_audio_25/   # Stable Audio 2.5 via fal.ai (audio-to-audio with strength)
        ace_step/          # ACE-Step via fal.ai (tags -> music, audio remix)
        udio/              # Udio via unofficial wrapper

Key design choices:

• Zero required dependencies. The core package imports nothing outside stdlib. Platform dependencies are lazy-imported when you first call generate().
• Config-driven plugins. Each platform declares a PLATFORM_CONFIG dict with parameter mappings, auth scheme, endpoints, and output format. Adding a platform is mostly configuration.
• Automatic parameter translation. The translation layer renames unified affordance names to native platform names and applies type coercions (e.g. duration seconds to music_length_ms milliseconds for ElevenLabs).
• Leverages existing libraries. Uses ho for OpenAPI-to-Python-function generation, i2 for signature manipulation and function wrapping.