Results for “music-production”

任意のジャンル・テーマで曲＋歌詞を制作する汎用作曲エンジン。曲先（メロディ淘汰型）でキャッチーなサビメロディを複数生成→人間が選別→変奏淘汰で確定→歌詞を載せる、ヒット曲プロセス準拠。作曲・作詞に特化。MiniMax music-2.6 主軸。ユーザーが「曲作って」「作曲して」「音楽作って」「歌作って」「オリジナル曲作って」「新しい曲作って」「武将で曲作って」「サイバー和で曲作って」と言った時、または /make-song を呼んだ時にトリガー。テーマ修飾（武将/サイバー和）は Phase 0 の [THEME] 選択で対応。

Generate videos or music as asynchronous media artifacts. Use this skill when the user asks for text-to-video, image-to-video, first-and-last-frame video, multi-reference-image video, music, a song, a soundtrack, or background music. This skill creates video/music files; it is not for text-to-speech.

AudioCraft: MusicGen text-to-music, AudioGen text-to-sound.

Generate music and sound effects from text descriptions using Meta's AudioCraft library, with support for melody conditioning, stereo output, and style transfer.

Generates a short music video from a song theme by creating keyframes, animating them, and producing a matching soundtrack.

PyTorch library for audio generation including text-to-music (MusicGen) and text-to-sound (AudioGen). Use when you need to generate music from text descriptions, create sound effects, or perform melody-conditioned music generation.

Generates 16-step beat sequences from style prompts, producing JSON, MIDI, WAV, and MP3 files.

Generates video production plans including shot lists, scripts, B-roll suggestions, music mood, thumbnail concepts, and platform-specific specs for reels, YouTube, and video ads.

Generate AI images, videos, music, and audio from the terminal via muapi.ai — supports 100+ models including Flux, Midjourney v7, Kling 3.0, Veo3, and Suno V5.

PyTorch library for audio generation including text-to-music (MusicGen) and text-to-sound (AudioGen). Use when you need to generate music from text descriptions, create sound effects, or perform melody-conditioned music generation.

PyTorch library for audio generation including text-to-music (MusicGen) and text-to-sound (AudioGen). Use when you need to generate music from text descriptions, create sound effects, or perform melody-conditioned music generation.

Record, search, and analyze music and audio sessions with playback tracking. Use when logging audio sessions, searching metadata, analyzing listening data.

Analyzes music listening data from Apple Music or Spotify exports to build a taste profile, then generates personalized playlists with AI-generated songs and album covers.

AI music generation with ACE-Step 1.5 — background music, vocal tracks, covers, stem extraction, audio repainting, and continuation for video production. Use when generating music, soundtracks, jingles, or working with audio stems. Triggers include background music, soundtrack, jingle, music generation, stem extraction, cover, style transfer, repaint, continuation, or musical composition tasks.

PyTorch library for audio generation including text-to-music (MusicGen) and text-to-sound (AudioGen). Use when you need to generate music from text descriptions, create sound effects, or perform melody-conditioned music generation.

Teaches music theory concepts with interactive exercises, ear training, and composition analysis

Create and modify SoundCue assets — add/connect nodes (mixer, random, delay, attenuation, modulator, etc.) and set audio properties (SoundCueService). Use when the user asks to create a Sound Cue, build a SoundCue node graph, or wire audio playback logic.

Create and modify MetaSound Source assets — add/connect nodes, wire pins, set input defaults, and play procedurally (MetaSoundService). Use when the user asks to create a MetaSound, build or edit a MetaSound graph, add operator/input/output nodes, or generate procedural audio.

Songwriting craft, AI music generation prompts (Suno focus), parody/adaptation techniques, phonetic tricks, and lessons learned. These are tools and ideas, not rules. Break any of them when the art calls for it.

HeartMuLa: Suno-like song generation from lyrics + tags.

Track, analyze, and manage music and audio files from the command line. Use when organizing playlists, converting formats, or analyzing metadata.

Write artist riders for live performance requirements. TRIGGERS - Use when user needs help with artist-rider related tasks.

Generate full songs and instrumental tracks with ElevenLabs Music on RunComfy via the `runcomfy` CLI. ElevenLabs Music turns a style description plus structured lyrics into studio-quality 44.1 kHz stereo audio — 5 seconds to 5 minutes — with section-level control (Intro / Verse / Chorus / Bridge), multilingual vocals, and commercial-friendly output. Generate a backing track, a full vocal song, a jingle, a podcast intro, a game loop, or an instrumental bed. Calls `runcomfy run elevenlabs/elevenlabs/music-generation` through the local RunComfy CLI. Triggers on "generate music", "make a song", "AI music", "background music", "instrumental track", "ElevenLabs Music", "soundtrack", "jingle", "theme music", "royalty-free music", "compose", or any explicit ask to generate music or a song from a text description.

HeartMuLa: Suno-like song generation from lyrics + tags.

Design and implement real-time audio processing chains using Audio Toolbox streaming objects. Use when building frame-based audio processing loops, multiband filters, dynamic range control, parametric EQ, level metering, loudness metering, SPL metering, octave-band analysis, sample rate conversion, frequency-domain filtering (long impulse responses, custom filter banks), or audio chains in Simulink. Covers visualization (visualize method), interactive tuning (parameterTuner), MIDI control, and Audio Toolbox Simulink blocks. Use when the user says "real-time audio", "streaming audio", "audio filter", "compressor", "equalizer", "level meter", "loudness meter", "SPL meter", "octave bands", "crossover filter", "audio chain", "MIDI control", "convolution reverb", "impulse response streaming", "frequency-domain filter", or asks to process audio frame-by-frame.