[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6JDPNR903W4MMPSC812E11M":3,"skill-same:01M6JDPNR903W4MMPSC812E11M":86},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":25,"publishedAt":46,"linkOnly":47,"markdownContent":48,"markdownBody":49,"frontMatter":50,"refName":64,"commitSha":65,"skillDirectory":66,"forksCount":67,"repositoryHtmlUrl":68,"repositoryHomepageUrl":69,"ownerHtmlUrl":70,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":71,"install":72,"resources":85},"01M6JDPNR903W4MMPSC812E11M","ai-music-generation",null,"Generate music and audio with AI — AudioCraft (MusicGen\u002FAudioGen from Meta) and HeartMuLa (open-source Suno-like lyrics+tags generation). Installation, model selection, prompt patterns, and hardware requirements.","S3YED\u002Fappie-kit","S3YED","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F62743867?v=4","01M6JBKBRRVP6KMS0VWZ27T3R6","01M6JBKBX0KG30284PW1DBYJT1",14,"MIT","https:\u002F\u002Fgithub.com\u002FS3YED\u002Fappie-kit\u002Fblob\u002Fmaster\u002Fskills\u002Ffleet\u002Fai-music-generation\u002FSKILL.md",[17,22],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",[26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45],"music generation","music","ai music generation","generation","selection","audiogen","source","audiocraft","MusicGen","Text-to-Music","patterns","macos","model","text","open","Audio","Meta","generate","model selection","linux","2026-11-10 10:12:30",false,"---\nname: ai-music-generation\ndescription: Generate music and audio with AI — AudioCraft (MusicGen\u002FAudioGen from Meta) and HeartMuLa (open-source Suno-like lyrics+tags generation). Installation, model selection, prompt patterns, and hardware requirements.\nversion: 1.0.0\nauthor: Hermes Agent\nlicense: MIT\nplatforms: [linux, macos]\nmetadata:\n  hermes:\n    tags: [music, audio, generation, ai, audiocraft, heartmula, musicgen, text-to-music]\n    related_skills: [songwriting-and-ai-music]\n---\n\n# AI Music Generation\n\n## Overview\n\nTwo open-source AI music generation frameworks, each with different strengths:\n\n| Framework | Best for | Output | License |\n|-----------|----------|--------|---------|\n| **AudioCraft (Meta)** | Text-to-music, text-to-sound, melody conditioning, audio codec | WAV\u002FMP3, 32\u002F16kHz | MIT |\n| **HeartMuLa** | Lyrics+tags → full songs, Suno-like, multilingual | MP3, 48kHz stereo | Apache-2.0 |\n\n**Decision:**\n- **AudioCraft** when you need text prompts only, sound effects, melody conditioning, or stereo audio infrastructure\n- **HeartMuLa** when you have specific lyrics and want a full song with tags (like Suno)\n\n---\n\n## Section A: AudioCraft (MusicGen \u002F AudioGen \u002F EnCodec)\n\n### Quick Start\n\n```bash\npip install audiocraft\n# Or via HuggingFace transformers:\npip install transformers torch torchaudio\n```\n\n### Text-to-Music (MusicGen)\n\n```python\nimport torchaudio\nfrom audiocraft.models import MusicGen\n\nmodel = MusicGen.get_pretrained('facebook\u002Fmusicgen-medium')\nmodel.set_generation_params(duration=8, top_k=250, temperature=1.0)\n\nwav = model.generate([\"happy upbeat electronic dance music\"])\ntorchaudio.save(\"output.wav\", wav[0].cpu(), sample_rate=32000)\n```\n\nUsing HuggingFace Transformers:\n```python\nfrom transformers import AutoProcessor, MusicgenForConditionalGeneration\nimport scipy\n\nprocessor = AutoProcessor.from_pretrained(\"facebook\u002Fmusicgen-small\")\nmodel = MusicgenForConditionalGeneration.from_pretrained(\"facebook\u002Fmusicgen-small\").to(\"cuda\")\n\ninputs = processor(text=[\"80s pop with bassy drums\"], padding=True, return_tensors=\"pt\").to(\"cuda\")\naudio_values = model.generate(**inputs, do_sample=True, guidance_scale=3, max_new_tokens=256)\nscipy.io.wavfile.write(\"output.wav\", rate=model.config.audio_encoder.sampling_rate, data=audio_values[0, 0].cpu().numpy())\n```\n\n### Model Variants\n\n| Model | Size | Use Case |\n|-------|------|----------|\n| `musicgen-small` | 300M | Quick generation |\n| `musicgen-medium` | 1.5B | Balanced quality\u002Fspeed |\n| `musicgen-large` | 3.3B | Best quality |\n| `musicgen-melody` | 1.5B | Melody conditioning |\n| `musicgen-stereo-*` | Varies | Stereo output |\n| `musicgen-style` | 1.5B | Style transfer |\n| `audiogen-medium` | 1.5B | Sound effects |\n\n### Melody-Conditioned Generation\n\n```python\nmodel = MusicGen.get_pretrained('facebook\u002Fmusicgen-melody')\nmodel.set_generation_params(duration=30)\n\nmelody, sr = torchaudio.load(\"melody.wav\")\nwav = model.generate_with_chroma([\"acoustic guitar folk song\"], melody, sr)\ntorchaudio.save(\"output.wav\", wav[0].cpu(), sample_rate=32000)\n```\n\n### Text-to-Sound (AudioGen)\n\n```python\nfrom audiocraft.models import AudioGen\nmodel = AudioGen.get_pretrained('facebook\u002Faudiogen-medium')\nmodel.set_generation_params(duration=5)\nwav = model.generate([\"dog barking in park with birds chirping\"])\ntorchaudio.save(\"sound.wav\", wav[0].cpu(), sample_rate=16000)\n```\n\n### GPU Memory Requirements\n\n| Model | FP32 | FP16 |\n|-------|------|------|\n| musicgen-small | ~4GB | ~2GB |\n| musicgen-medium | ~8GB | ~4GB |\n| musicgen-large | ~16GB | ~8GB |\n\n---\n\n## Section B: HeartMuLa\n\nHardware minimum: 8GB VRAM (`--lazy_load true`), recommended 16GB+.\n\n### Installation\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002FHeartMuLa\u002Fheartlib.git\ncd heartlib\nuv venv --python 3.10 .venv\n. .venv\u002Fbin\u002Factivate\nuv pip install -e .\n\n# Fix dependency conflicts\nuv pip install --upgrade datasets transformers\n```\n\n### Patch Source Code\n\n**Patch 1** — In `src\u002Fheartlib\u002Fheartmula\u002Fmodeling_heartmula.py`, in `setup_caches`, add RoPE reinitialization after `reset_caches`:\n```python\nfrom torchtune.models.llama3_1._position_embeddings import Llama3ScaledRoPE\nfor module in self.modules():\n    if isinstance(module, Llama3ScaledRoPE) and not module.is_cache_built:\n        module.rope_init()\n        module.to(device)\n```\n\n**Patch 2** — In `src\u002Fheartlib\u002Fpipelines\u002Fmusic_generation.py`, add `ignore_mismatched_sizes=True` to all `HeartCodec.from_pretrained()` calls.\n\n### Download Checkpoints\n\n```bash\ncd heartlib\nhf download --local-dir '.\u002Fckpt' 'HeartMuLa\u002FHeartMuLaGen'\nhf download --local-dir '.\u002Fckpt\u002FHeartMuLa-oss-3B' 'HeartMuLa\u002FHeartMuLa-oss-3B-happy-new-year'\nhf download --local-dir '.\u002Fckpt\u002FHeartCodec-oss' 'HeartMuLa\u002FHeartCodec-oss-20260123'\n```\n\n### Generate Music\n\n```bash\n. .venv\u002Fbin\u002Factivate\npython .\u002Fexamples\u002Frun_music_generation.py \\\n  --model_path=.\u002Fckpt --version=\"3B\" \\\n  --lyrics=\".\u002Fassets\u002Flyrics.txt\" --tags=\".\u002Fassets\u002Ftags.txt\" \\\n  --save_path=\".\u002Fassets\u002Foutput.mp3\" --lazy_load true\n```\n\n**Tags format:** comma-separated, no spaces: `piano,happy,wedding,synthesizer`\n**Lyrics format:** use bracketed structural tags: `[Verse]`, `[Chorus]`, `[Bridge]`, `[Outro]`\n\n### Key Parameters\n\n| Parameter | Default | Description |\n|-----------|---------|-------------|\n| `--max_audio_length_ms` | 240000 | Max length in ms (240s = 4 min) |\n| `--topk` | 50 | Top-k sampling |\n| `--temperature` | 1.0 | Sampling temperature |\n| `--cfg_scale` | 1.5 | Classifier-free guidance scale |\n| `--lazy_load` | false | Load\u002Funload models on demand |\n\n### Pitfalls\n\n1. Do NOT use bf16 for HeartCodec — degrades audio quality\n2. Tags may be ignored — lyrics tend to dominate\n3. Triton not available on macOS — Linux\u002FCUDA only\n4. No GPU → extremely slow on CPU (30-60+ min per song)\n5. RTX 5080 incompatibility reported upstream\n\n### Links\n\n- Repo: https:\u002F\u002Fgithub.com\u002FHeartMuLa\u002Fheartlib\n- Models: https:\u002F\u002Fhuggingface.co\u002FHeartMuLa\n- Paper: https:\u002F\u002Farxiv.org\u002Fabs\u002F2601.10547","# AI Music Generation\n\n## Overview\n\nTwo open-source AI music generation frameworks, each with different strengths:\n\n| Framework | Best for | Output | License |\n|-----------|----------|--------|---------|\n| **AudioCraft (Meta)** | Text-to-music, text-to-sound, melody conditioning, audio codec | WAV\u002FMP3, 32\u002F16kHz | MIT |\n| **HeartMuLa** | Lyrics+tags → full songs, Suno-like, multilingual | MP3, 48kHz stereo | Apache-2.0 |\n\n**Decision:**\n- **AudioCraft** when you need text prompts only, sound effects, melody conditioning, or stereo audio infrastructure\n- **HeartMuLa** when you have specific lyrics and want a full song with tags (like Suno)\n\n---\n\n## Section A: AudioCraft (MusicGen \u002F AudioGen \u002F EnCodec)\n\n### Quick Start\n\n```bash\npip install audiocraft\n# Or via HuggingFace transformers:\npip install transformers torch torchaudio\n```\n\n### Text-to-Music (MusicGen)\n\n```python\nimport torchaudio\nfrom audiocraft.models import MusicGen\n\nmodel = MusicGen.get_pretrained('facebook\u002Fmusicgen-medium')\nmodel.set_generation_params(duration=8, top_k=250, temperature=1.0)\n\nwav = model.generate([\"happy upbeat electronic dance music\"])\ntorchaudio.save(\"output.wav\", wav[0].cpu(), sample_rate=32000)\n```\n\nUsing HuggingFace Transformers:\n```python\nfrom transformers import AutoProcessor, MusicgenForConditionalGeneration\nimport scipy\n\nprocessor = AutoProcessor.from_pretrained(\"facebook\u002Fmusicgen-small\")\nmodel = MusicgenForConditionalGeneration.from_pretrained(\"facebook\u002Fmusicgen-small\").to(\"cuda\")\n\ninputs = processor(text=[\"80s pop with bassy drums\"], padding=True, return_tensors=\"pt\").to(\"cuda\")\naudio_values = model.generate(**inputs, do_sample=True, guidance_scale=3, max_new_tokens=256)\nscipy.io.wavfile.write(\"output.wav\", rate=model.config.audio_encoder.sampling_rate, data=audio_values[0, 0].cpu().numpy())\n```\n\n### Model Variants\n\n| Model | Size | Use Case |\n|-------|------|----------|\n| `musicgen-small` | 300M | Quick generation |\n| `musicgen-medium` | 1.5B | Balanced quality\u002Fspeed |\n| `musicgen-large` | 3.3B | Best quality |\n| `musicgen-melody` | 1.5B | Melody conditioning |\n| `musicgen-stereo-*` | Varies | Stereo output |\n| `musicgen-style` | 1.5B | Style transfer |\n| `audiogen-medium` | 1.5B | Sound effects |\n\n### Melody-Conditioned Generation\n\n```python\nmodel = MusicGen.get_pretrained('facebook\u002Fmusicgen-melody')\nmodel.set_generation_params(duration=30)\n\nmelody, sr = torchaudio.load(\"melody.wav\")\nwav = model.generate_with_chroma([\"acoustic guitar folk song\"], melody, sr)\ntorchaudio.save(\"output.wav\", wav[0].cpu(), sample_rate=32000)\n```\n\n### Text-to-Sound (AudioGen)\n\n```python\nfrom audiocraft.models import AudioGen\nmodel = AudioGen.get_pretrained('facebook\u002Faudiogen-medium')\nmodel.set_generation_params(duration=5)\nwav = model.generate([\"dog barking in park with birds chirping\"])\ntorchaudio.save(\"sound.wav\", wav[0].cpu(), sample_rate=16000)\n```\n\n### GPU Memory Requirements\n\n| Model | FP32 | FP16 |\n|-------|------|------|\n| musicgen-small | ~4GB | ~2GB |\n| musicgen-medium | ~8GB | ~4GB |\n| musicgen-large | ~16GB | ~8GB |\n\n---\n\n## Section B: HeartMuLa\n\nHardware minimum: 8GB VRAM (`--lazy_load true`), recommended 16GB+.\n\n### Installation\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002FHeartMuLa\u002Fheartlib.git\ncd heartlib\nuv venv --python 3.10 .venv\n. .venv\u002Fbin\u002Factivate\nuv pip install -e .\n\n# Fix dependency conflicts\nuv pip install --upgrade datasets transformers\n```\n\n### Patch Source Code\n\n**Patch 1** — In `src\u002Fheartlib\u002Fheartmula\u002Fmodeling_heartmula.py`, in `setup_caches`, add RoPE reinitialization after `reset_caches`:\n```python\nfrom torchtune.models.llama3_1._position_embeddings import Llama3ScaledRoPE\nfor module in self.modules():\n    if isinstance(module, Llama3ScaledRoPE) and not module.is_cache_built:\n        module.rope_init()\n        module.to(device)\n```\n\n**Patch 2** — In `src\u002Fheartlib\u002Fpipelines\u002Fmusic_generation.py`, add `ignore_mismatched_sizes=True` to all `HeartCodec.from_pretrained()` calls.\n\n### Download Checkpoints\n\n```bash\ncd heartlib\nhf download --local-dir '.\u002Fckpt' 'HeartMuLa\u002FHeartMuLaGen'\nhf download --local-dir '.\u002Fckpt\u002FHeartMuLa-oss-3B' 'HeartMuLa\u002FHeartMuLa-oss-3B-happy-new-year'\nhf download --local-dir '.\u002Fckpt\u002FHeartCodec-oss' 'HeartMuLa\u002FHeartCodec-oss-20260123'\n```\n\n### Generate Music\n\n```bash\n. .venv\u002Fbin\u002Factivate\npython .\u002Fexamples\u002Frun_music_generation.py \\\n  --model_path=.\u002Fckpt --version=\"3B\" \\\n  --lyrics=\".\u002Fassets\u002Flyrics.txt\" --tags=\".\u002Fassets\u002Ftags.txt\" \\\n  --save_path=\".\u002Fassets\u002Foutput.mp3\" --lazy_load true\n```\n\n**Tags format:** comma-separated, no spaces: `piano,happy,wedding,synthesizer`\n**Lyrics format:** use bracketed structural tags: `[Verse]`, `[Chorus]`, `[Bridge]`, `[Outro]`\n\n### Key Parameters\n\n| Parameter | Default | Description |\n|-----------|---------|-------------|\n| `--max_audio_length_ms` | 240000 | Max length in ms (240s = 4 min) |\n| `--topk` | 50 | Top-k sampling |\n| `--temperature` | 1.0 | Sampling temperature |\n| `--cfg_scale` | 1.5 | Classifier-free guidance scale |\n| `--lazy_load` | false | Load\u002Funload models on demand |\n\n### Pitfalls\n\n1. 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