No. The scores should not be read as single-generation quality because they use different candidate-selection protocols, and the README explicitly says ranking differences are not statistically established.

• YuE2 best-of-8 reports a SongBench Avg of 6.9632 after selecting from eight candidates, while standard YuE2 reports 6.7316 with two candidates. The former is therefore not a one-shot result.
• Both YuE2 rows in the comparison use the YuE2-Vae-legacy benchmark decoder. Switching to the default VAE would not reproduce the table.
• The README directs users to the WildSongBench standard-yue2 reproduction instructions and notes that rankings vary by metric; YuE2 does not lead every MuLan or PER measure.
• The saved output includes generation settings and model identities, which helps document configurations but does not replace the evaluation protocol.

The README does not provide the complete candidate-selection implementation, confidence intervals, or significance tests, so these means cannot establish universal superiority.

Yes, because your environment matches the prerequisites stated in the README.

• The Quick start requires Linux, Python 3.12, an NVIDIA GPU with BF16 support, and at least 24 GB of VRAM; your setup satisfies these requirements.
• YuE2 runs without quantization and produces 48 kHz stereo audio. The Python API can load m-a-p/YuE2-3B with device="cuda".
• The first run downloads model files from Hugging Face, so network access, cache storage, and repository permissions can still affect startup.
• The output directory retains the score, semantic tokens, acoustic latents, generation settings, and model identities, which is useful for experiments and reproducibility.

The README does not state remaining VRAM under different song lengths, candidate counts, or concurrency levels. It also provides no standard deployment path for CPUs, AMD GPUs, or mobile devices.

Yes, because the README provides an agent skill and treats scores, lyrics, and version comparison as explicit workflow objects.

• The yue2-music skill teaches an agent to generate songs, transcribe and cover recordings, edit ABC scores, check musical invariants, and organize listening comparisons.
• Its example request directly matches your workflow: create an English piano-pop song, keep the original audio and score, make a jazz-harmony version, and compare both versions.
• The Python API separates the process into plan(), generate_semantic(), synthesize(), and decode(), allowing an agent to edit scores or lyrics between planning and rendering.
• save_artifacts() preserves audio, scores, semantic tokens, acoustic latents, generation settings, and model identities, supporting version tracking and rollback.

However, the README does not identify supported agent frameworks or provide designs for concurrent jobs, error recovery, permission isolation, or long-conversation state management. Those integration details remain your responsibility.

No. YuE2 is not the simplest instant-audition tool for your goal because its value depends on score planning and a local hardware environment that you do not want to manage.

• The default new-song workflow is cot="full", which first creates an editable melody-and-chord plan. This adds an intermediate artifact beyond entering a prompt.
• The project requires Linux, Python 3.12, an NVIDIA GPU with BF16 support, and at least 24 GB of VRAM. The first run also downloads models from Hugging Face.
• The documented learning curve includes ABC scores, planning modes, transcription, and staged generation; the project explicitly describes this as costly for users who only want immediate playback.
• cot="off" can generate directly from lyrics and style, but it gives up explicit score control. It does not remove the local installation or hardware requirements.

The README provides no hosted web workflow, CPU inference path, mobile deployment option, or installation-free standard route.

It depends: use melody when preserving the melody matters more than preserving harmony; use full when the original chords and structure must also remain stable.

• The README describes cot="melody" as “Use a melody plan with free accompaniment” and explicitly recommends it for covers, making it suitable for changing the accompaniment style.
• cot="full" provides an editable melody-and-chord plan. In the 948-work cover evaluation, full-score YuE2 achieved 0.647 CLEWS mAP, compared with 0.006 without a score.
• cot="off" generates directly from lyrics and style, removing explicit score control and therefore conflicting with a strong melody-preservation requirement.
• SheetSage2 transcription becomes the control input. Errors from complex rhythms, polyphony, or noisy recordings can propagate into the cover.

The README does not guarantee simultaneous preservation of singer identity, lyric pronunciation, melody, and target style. It also gives no transcription accuracy for your specific recording.

Yes. YuE2 treats an ABC score as an explicit input in full or melody mode and exposes a staged API suitable for reuse.

• The Quick start explicitly supports abc=..., allowing a user-provided score in either full or melody mode.
• cot="full" controls an editable melody-and-chord plan, while cot="melody" preserves the melody with free accompaniment. These map to full-structure reuse and arrangement-focused adaptation.
• The generation guide is specifically referenced for exact-plan reuse and decoder selection, indicating that the score need not be replanned for every rendering.
• The pipeline is separated into plan(), generate_semantic(), synthesize(), and decode(), while saving scores, tokens, latents, and model identities for comparison.

The README does not define the supported ABC grammar, invalid-score error behavior, or guarantee identical rhythm, timbre, or vocal expression after changing decoders.