--- title: "lmms-eval vs ParallelWaveGAN" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/evolvinglmms-lab-lmms-eval-vs-kan-bayashi-parallelwavegan" tools: ["evolvinglmms-lab-lmms-eval", "kan-bayashi-parallelwavegan"] --- # lmms-eval vs ParallelWaveGAN *GraphCanon updated Jul 11, 2026* ## Verdict Pick lmms-eval when lmms-eval is primarily Python; ParallelWaveGAN is Jupyter Notebook; pick ParallelWaveGAN when parallelWaveGAN is primarily Jupyter Notebook; lmms-eval is Python. [lmms-eval](https://www.lmms-lab.com) reports 4.3k GitHub stars, 616 forks, and 44 open issues, last pushed Jul 7, 2026. [ParallelWaveGAN](https://kan-bayashi.github.io/ParallelWaveGAN/) has 1.6k stars, 352 forks, and 43 open issues, last pushed Apr 22, 2024. Figures are from public GitHub metadata via [lmms-eval's repository](https://github.com/EvolvingLMMs-Lab/lmms-eval) and [ParallelWaveGAN's repository](https://github.com/kan-bayashi/ParallelWaveGAN). | | [lmms-eval](/tools/evolvinglmms-lab-lmms-eval.md) | [ParallelWaveGAN](/tools/kan-bayashi-parallelwavegan.md) | | --- | --- | --- | | Tagline | One-for-All Multimodal Evaluation Toolkit Across Text, Image, Video, and Audio Tasks | Unofficial Parallel WaveGAN (+ MelGAN & Multi-band MelGAN & HiFi-GAN & StyleMelGAN) with Pytorch | | Stars | 4,298 | 1,644 | | Forks | 616 | 352 | | Open issues | 44 | 43 | | Language | Python | Jupyter Notebook | | Adopt for | lmms-eval is a comprehensive multimodal evaluation toolkit for large language models, enabling reproduction of LLaVA-1.5 results and supporting various datasets including text, images, videos, and audio tasks. | - | | Persona | - | - | | Runtime | - | - | | License | Other | MIT | | Categories | LLM Frameworks, Computer Vision, Speech & Audio | Model Training, Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [lmms-eval](/tools/evolvinglmms-lab-lmms-eval.md) | [ParallelWaveGAN](/tools/kan-bayashi-parallelwavegan.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 4d | 810d | | Open issues (now) | 44 | 43 | | Owner type | Organization | User | | Full report | [trust report](/tools/evolvinglmms-lab-lmms-eval/trust.md) | [trust report](/tools/kan-bayashi-parallelwavegan/trust.md) | ## Shared compatibility - **Python**: [lmms-eval](/tools/evolvinglmms-lab-lmms-eval.md) - Python runtime; [ParallelWaveGAN](/tools/kan-bayashi-parallelwavegan.md) - Python runtime ## Decision facts: lmms-eval - **Requirements:** Min 4 GB RAM; Requires Python 3.12 or higher for installation.; Java 8 is required when testing datasets like COCO, RefCOCO, and NoCaps due to dependency on pycocoeval API. - **Adopt for:** lmms-eval is a comprehensive multimodal evaluation toolkit for large language models, enabling reproduction of LLaVA-1.5 results and supporting various datasets including text, images, videos, and audio tasks. ## Choose when ### Choose lmms-eval if… - lmms-eval is primarily Python; ParallelWaveGAN is Jupyter Notebook. - License: lmms-eval is Other, ParallelWaveGAN is MIT. - Requirements: Min 4 GB RAM; Requires Python 3.12 or higher for installation.; Java 8 is required when testing datasets like COCO, RefCOCO, and NoCaps due to dependency on pycocoeval API.. - Tags unique to lmms-eval: evaluation, benchmark, large-language-models, multimodal-evaluation. - Also covers LLM Frameworks, Computer Vision. - When you need to evaluate the performance of multimodal large language models across diverse benchmarks including text, image, video, and audio. ### Choose ParallelWaveGAN if… - ParallelWaveGAN is primarily Jupyter Notebook; lmms-eval is Python. - License: ParallelWaveGAN is MIT, lmms-eval is Other. - Tags unique to ParallelWaveGAN: parallel-wavenet, style-melgan, realtime, hifigan. - Also covers Model Training. ## When NOT to use lmms-eval - If your project does not involve multimodal large language models or you are only interested in unimodal datasets. - When you need a simpler toolkit that focuses solely on text-based evaluations, as lmms-eval's extensive capabilities might be unnecessary and introduce complexity. ## When NOT to use ParallelWaveGAN - Last GitHub push was 811 days ago (dormant maintenance, Apr 22, 2024). Validate activity before betting a new project on ParallelWaveGAN. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## Common questions ### What is the difference between lmms-eval and ParallelWaveGAN? lmms-eval: One-for-All Multimodal Evaluation Toolkit Across Text, Image, Video, and Audio Tasks. ParallelWaveGAN: Unofficial Parallel WaveGAN (+ MelGAN & Multi-band MelGAN & HiFi-GAN & StyleMelGAN) with Pytorch. See the comparison table for live GitHub stats and shared categories. ### When should I choose lmms-eval over ParallelWaveGAN? Choose lmms-eval over ParallelWaveGAN when lmms-eval is primarily Python; ParallelWaveGAN is Jupyter Notebook; License: lmms-eval is Other, ParallelWaveGAN is MIT; Requirements: Min 4 GB RAM; Requires Python 3.12 or higher for installation.; Java 8 is required when testing datasets like COCO, RefCOCO, and NoCaps due to dependency on pycocoeval API.; Tags unique to lmms-eval: evaluation, benchmark, large-language-models, multimodal-evaluation; Also covers LLM Frameworks, Computer Vision; When you need to evaluate the performance of multimodal large language models across diverse benchmarks including text, image, video, and audio. ### When should I choose ParallelWaveGAN over lmms-eval? Choose ParallelWaveGAN over lmms-eval when ParallelWaveGAN is primarily Jupyter Notebook; lmms-eval is Python; License: ParallelWaveGAN is MIT, lmms-eval is Other; Tags unique to ParallelWaveGAN: parallel-wavenet, style-melgan, realtime, hifigan; Also covers Model Training. ### When should I avoid lmms-eval? If your project does not involve multimodal large language models or you are only interested in unimodal datasets. When you need a simpler toolkit that focuses solely on text-based evaluations, as lmms-eval's extensive capabilities might be unnecessary and introduce complexity. ### When should I avoid ParallelWaveGAN? Last GitHub push was 811 days ago (dormant maintenance, Apr 22, 2024). Validate activity before betting a new project on ParallelWaveGAN. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### Is lmms-eval or ParallelWaveGAN more popular on GitHub? lmms-eval has more GitHub stars (4,298 vs 1,644). Stars measure visibility, not whether either tool fits your constraints. ### Are lmms-eval and ParallelWaveGAN open source? Yes - both are open-source projects on GitHub (lmms-eval: Other, ParallelWaveGAN: MIT). ### Where can I find alternatives to lmms-eval or ParallelWaveGAN? GraphCanon lists graph-backed alternatives at [lmms-eval alternatives](/tools/evolvinglmms-lab-lmms-eval/alternatives) and [ParallelWaveGAN alternatives](/tools/kan-bayashi-parallelwavegan/alternatives) ([lmms-eval markdown twin](/tools/evolvinglmms-lab-lmms-eval/alternatives.md), [ParallelWaveGAN markdown twin](/tools/kan-bayashi-parallelwavegan/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/evolvinglmms-lab-lmms-eval-vs-kan-bayashi-parallelwavegan.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, lmms-eval or ParallelWaveGAN? lmms-eval: Very active. ParallelWaveGAN: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for lmms-eval and ParallelWaveGAN? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [lmms-eval trust report](/tools/evolvinglmms-lab-lmms-eval/trust); [ParallelWaveGAN trust report](/tools/kan-bayashi-parallelwavegan/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=evolvinglmms-lab-lmms-eval`](/api/graphcanon/graph?tool=evolvinglmms-lab-lmms-eval) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_