Add Pop2Piano (#21785)

* init commit * config updated also some modeling * Processor and Model config combined * extraction pipeline(upto before spectogram & mel_conditioner) added but not properly tested * model loading successful! * feature extractor done! * FE can now be called from HF * postprocessing added in fe file * same as prev commit * Pop2PianoConfig doc done * cfg docs slightly changed * fe docs done * batched * batched working! * temp * v1 * checking * trying to go with generate * with generate and model tests passed * before rebasing * . * tests done docs done remaining others & nits * nits * LogMelSpectogram shifted to FeatureExtractor * is_tf rmeoved from pop2piano/init * import solved * tokenization tests added * minor fixed regarding modeling_pop2piano * tokenizer changed to only return midi_object and other changes * Updated paper abstract(Camera-ready version) (#2) * more comments and nits * ruff changes * code quality fix * sg comments * t5 change added and rebased * comments except batching * batching done * comments * small doc fix * example removed from modeling * ckpt * forward it compatible with fe and generation done * comments * comments * code-quality fix(maybe) * ckpts changed * doc file changed from mdx to md * test fixes * tokenizer test fix * changes * nits done main changes remaining * code modified * Pop2PianoProcessor added with tests * other comments * added Pop2PianoProcessor to dummy_objects * added require_onnx to modeling file * changes * update .md file * remove extra line in index.md * back to the main index * added pop2piano to index * Added tokenizer.__call__ with valid args and batch_decode and aligned the processor part too * changes * added return types to 2 tokenizer methods * the PR build test might work now * added backends * PR build fix * vocab added * comments * refactored vocab into 1 file * added conversion script * comments * essentia version changed in .md * comments * more tokenizer tests added * minor fix * tests extended for outputs acc check * small fix --------- Co-authored-by: Jongho Choi <sweetcocoa@snu.ac.kr>
2023-08-21 21:05:00 +05:30
parent 6f041fcbb8
commit 450a181d8b
38 changed files with 5284 additions and 0 deletions
--- a/docs/source/en/index.md
+++ b/docs/source/en/index.md
@@ -200,6 +200,7 @@ The documentation is organized into five sections:
 1. **[Pix2Struct](model_doc/pix2struct)** (from Google) released with the paper [Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding](https://arxiv.org/abs/2210.03347) by Kenton Lee, Mandar Joshi, Iulia Turc, Hexiang Hu, Fangyu Liu, Julian Eisenschlos, Urvashi Khandelwal, Peter Shaw, Ming-Wei Chang, Kristina Toutanova.
 1. **[PLBart](model_doc/plbart)** (from UCLA NLP) released with the paper [Unified Pre-training for Program Understanding and Generation](https://arxiv.org/abs/2103.06333) by Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang.
 1. **[PoolFormer](model_doc/poolformer)** (from Sea AI Labs) released with the paper [MetaFormer is Actually What You Need for Vision](https://arxiv.org/abs/2111.11418) by Yu, Weihao and Luo, Mi and Zhou, Pan and Si, Chenyang and Zhou, Yichen and Wang, Xinchao and Feng, Jiashi and Yan, Shuicheng.
+1. **[Pop2Piano](model_doc/pop2piano)** released with the paper [Pop2Piano : Pop Audio-based Piano Cover Generation](https://arxiv.org/abs/2211.00895) by Jongho Choi and Kyogu Lee.
 1. **[ProphetNet](model_doc/prophetnet)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
 1. **[PVT](model_doc/pvt)** (from Nanjing University, The University of Hong Kong etc.) released with the paper [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://arxiv.org/pdf/2102.12122.pdf) by Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao.
 1. **[QDQBert](model_doc/qdqbert)** (from NVIDIA) released with the paper [Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation](https://arxiv.org/abs/2004.09602) by Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev and Paulius Micikevicius.
@@ -415,6 +416,7 @@ Flax), PyTorch, and/or TensorFlow.
 |          Pix2Struct           |       ✅        |         ❌         |      ❌      |
 |            PLBart             |       ✅        |         ❌         |      ❌      |
 |          PoolFormer           |       ✅        |         ❌         |      ❌      |
+|           Pop2Piano           |       ✅        |         ❌         |      ❌      |
 |          ProphetNet           |       ✅        |         ❌         |      ❌      |
 |              PVT              |       ✅        |         ❌         |      ❌      |
 |            QDQBert            |       ✅        |         ❌         |      ❌      |