Minimax Music-02 is a compact, fast, cost-effective MoE music generator (230B params, 10B active) for high-quality music production. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
待機中
$0.031回あたり·~33 / $1
A melancholic indie dream-pop song sung by a soft male voice, light guitars and distant reverb. Emotional and reflective.
An ethereal electronic vocal performance with auto-tune effects, floating tone, cinematic reverb
A gentle female voice singing a heartfelt pop ballad, emotional yet controlled, with a soft piano accompaniment.
A warm acoustic folk song sung by a male voice with gentle guitar and ambient background. Emotional, melodic, storytelling tone.
A powerful rock anthem sung by a raspy male vocal, with driving electric guitars, drums, and cinematic energy. Must sound like a live stadium performance.
An ethereal orchestral vocal performance with reverb and layered harmonies. Female vocal sings in a haunting, angelic tone, with cinematic strings and atmosphere.
A cinematic pop-rock anthem sung by a powerful male voice, with strong rhythm and rising strings. Heroic and emotional.
A melodic modern pop song with emotional female vocals and soft piano + ambient strings. Starts intimate, then builds into an uplifting chorus
A dreamy electronic pop track sung by a female voice, featuring reverb vocals, synth pads, and emotional delivery
An epic cinematic vocal performance with full orchestra, hybrid drums, and a strong female lead voice. Powerful, emotional, heroic tone.
MiniMax Music 02 is an AI music generation model that turns a style prompt + lyrics into a complete song. Describe the mood, genre, and vocal style, paste your lyrics, and the model produces fully arranged audio with vocals and backing instruments.
Prompt-guided composition Generate songs from natural language prompts like “melancholic indie dream-pop with soft male vocals and light guitars.”
Lyric-aware singing Paste structured lyrics (verses, chorus, bridge) and the model sings them, aligning melody and phrasing to your text.
Full arrangement Produces a mixed track with vocals, backing instruments, and effects — ready for demos, temp tracks, or creative exploration.
Configurable audio quality Supports multiple bitrates and sample rates (e.g., 256 kbps, 44.1 kHz) so you can balance quality and file size.
prompt (required) Musical description: genre, mood, instrumentation, vocal style, atmosphere.
lyrics (required) The words to sing. You can structure them by sections (Verse, Chorus, Bridge) using plain text.
bitrate Target audio bitrate, e.g. 256000 for 256 kbps.
sample_rate Audio sample rate, e.g. 44100 Hz.
Output: A single audio file (song) matching your prompt and lyrics.
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/minimax/music-02 with your input as JSON. The endpoint returns a prediction id; poll the prediction endpoint until status flips to completed, then read the output URL from data.outputs[0]. Examples for Music 02 below.
# Submit the prediction
curl -X POST "https://api.wavespeed.ai/api/v3/minimax/music-02" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $WAVESPEED_API_KEY" \
-d '{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"bitrate": 256000,
"sample_rate": 44100
}'
# Response includes a prediction id. Poll for the result:
curl -X GET "https://api.wavespeed.ai/api/v3/predictions/{request_id}/result" \
-H "Authorization: Bearer $WAVESPEED_API_KEY"
# When status is "completed", read the output from data.outputs[0].// npm install wavespeed
const WaveSpeed = require('wavespeed');
const client = new WaveSpeed(); // reads WAVESPEED_API_KEY from env
const result = await client.run("minimax/music-02", {
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"bitrate": 256000,
"sample_rate": 44100
});
console.log(result.outputs[0]); // → URL of the generated output# pip install wavespeed
import wavespeed
output = wavespeed.run(
"minimax/music-02",
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"bitrate": 256000,
"sample_rate": 44100
}
)
print(output["outputs"][0]) # → URL of the generated outputMusic 02 is a MiniMax model for audio generation, exposed as a REST API on WaveSpeedAI. Minimax Music-02 is a compact, fast, cost-effective MoE music generator (230B params, 10B active) for high-quality music production. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.
POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID; poll the prediction endpoint until status flips to "completed", then read the output URL from the result. The playground generates a ready-to-paste code sample in Python, JavaScript, or cURL for whatever inputs you've set. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/minimax/minimax-music-02.
Music 02 starts at $0.030 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.
Key inputs: `prompt`, `bitrate`, `lyrics`, `sample_rate`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/minimax/minimax-music-02.
Average end-to-end generation time on WaveSpeedAI is around 138 seconds per request — measured across recent runs. Queue time scales with global demand; live status is visible in the prediction record.
Commercial usage rights depend on the model's license, set by its provider (MiniMax). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.