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* 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

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* with generate and model tests passed

* before rebasing

* .

* tests done docs done remaining others & nits

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* 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

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* Pop2PianoProcessor added with tests

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* added require_onnx to modeling file

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* added pop2piano to index

* Added tokenizer.__call__ with valid args and batch_decode and aligned the processor part too

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---------

Co-authored-by: Jongho Choi <sweetcocoa@snu.ac.kr>
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Susnato Dhar
2023-08-21 21:05:00 +05:30
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docs/source/en/_toctree.yml
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@@ -584,6 +584,8 @@
title: MMS
- local: model_doc/musicgen
title: MusicGen
- local: model_doc/pop2piano
title: Pop2Piano
- local: model_doc/sew
title: SEW
- local: model_doc/sew-d

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docs/source/en/index.md
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@@ -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 | ✅ | ❌ | ❌ |

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<!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# Pop2Piano
## Overview
The Pop2Piano model was proposed in [Pop2Piano : Pop Audio-based Piano Cover Generation](https://arxiv.org/abs/2211.00895) by Jongho Choi and Kyogu Lee.
Piano covers of pop music are widely enjoyed, but generating them from music is not a trivial task. It requires great
expertise with playing piano as well as knowing different characteristics and melodies of a song. With Pop2Piano you
can directly generate a cover from a song's audio waveform. It is the first model to directly generate a piano cover
from pop audio without melody and chord extraction modules.
Pop2Piano is an encoder-decoder Transformer model based on [T5](https://arxiv.org/pdf/1910.10683.pdf). The input audio
is transformed to its waveform and passed to the encoder, which transforms it to a latent representation. The decoder
uses these latent representations to generate token ids in an autoregressive way. Each token id corresponds to one of four
different token types: time, velocity, note and 'special'. The token ids are then decoded to their equivalent MIDI file.
The abstract from the paper is the following:
*Piano covers of pop music are enjoyed by many people. However, the
task of automatically generating piano covers of pop music is still
understudied. This is partly due to the lack of synchronized
{Pop, Piano Cover} data pairs, which made it challenging to apply
the latest data-intensive deep learning-based methods. To leverage
the power of the data-driven approach, we make a large amount of
paired and synchronized {Pop, Piano Cover} data using an automated
pipeline. In this paper, we present Pop2Piano, a Transformer network
that generates piano covers given waveforms of pop music. To the best
of our knowledge, this is the first model to generate a piano cover
directly from pop audio without using melody and chord extraction
modules. We show that Pop2Piano, trained with our dataset, is capable
of producing plausible piano covers.*
Tips:
1. To use Pop2Piano, you will need to install the 🤗 Transformers library, as well as the following third party modules:
```
pip install pretty-midi==0.2.9 essentia==2.1b6.dev1034 librosa scipy
```
Please note that you may need to restart your runtime after installation.
2. Pop2Piano is an Encoder-Decoder based model like T5.
3. Pop2Piano can be used to generate midi-audio files for a given audio sequence.
4. Choosing different composers in `Pop2PianoForConditionalGeneration.generate()` can lead to variety of different results.
5. Setting the sampling rate to 44.1 kHz when loading the audio file can give good performance.
6. Though Pop2Piano was mainly trained on Korean Pop music, it also does pretty well on other Western Pop or Hip Hop songs.
This model was contributed by [Susnato Dhar](https://huggingface.co/susnato).
The original code can be found [here](https://github.com/sweetcocoa/pop2piano).
## Examples
- Example using HuggingFace Dataset:
```python
>>> from datasets import load_dataset
>>> from transformers import Pop2PianoForConditionalGeneration, Pop2PianoProcessor
>>> model = Pop2PianoForConditionalGeneration.from_pretrained("sweetcocoa/pop2piano")
>>> processor = Pop2PianoProcessor.from_pretrained("sweetcocoa/pop2piano")
>>> ds = load_dataset("sweetcocoa/pop2piano_ci", split="test")
>>> inputs = processor(
... audio=ds["audio"][0]["array"], sampling_rate=ds["audio"][0]["sampling_rate"], return_tensors="pt"
... )
>>> model_output = model.generate(input_features=inputs["input_features"], composer="composer1")
>>> tokenizer_output = processor.batch_decode(
... token_ids=model_output, feature_extractor_output=inputs
... )["pretty_midi_objects"][0]
>>> tokenizer_output.write("./Outputs/midi_output.mid")
```
- Example using your own audio file:
```python
>>> import librosa
>>> from transformers import Pop2PianoForConditionalGeneration, Pop2PianoProcessor
>>> audio, sr = librosa.load("<your_audio_file_here>", sr=44100) # feel free to change the sr to a suitable value.
>>> model = Pop2PianoForConditionalGeneration.from_pretrained("sweetcocoa/pop2piano")
>>> processor = Pop2PianoProcessor.from_pretrained("sweetcocoa/pop2piano")
>>> inputs = processor(audio=audio, sampling_rate=sr, return_tensors="pt")
>>> model_output = model.generate(input_features=inputs["input_features"], composer="composer1")
>>> tokenizer_output = processor.batch_decode(
... token_ids=model_output, feature_extractor_output=inputs
... )["pretty_midi_objects"][0]
>>> tokenizer_output.write("./Outputs/midi_output.mid")
```
- Example of processing multiple audio files in batch:
```python
>>> import librosa
>>> from transformers import Pop2PianoForConditionalGeneration, Pop2PianoProcessor
>>> # feel free to change the sr to a suitable value.
>>> audio1, sr1 = librosa.load("<your_first_audio_file_here>", sr=44100)
>>> audio2, sr2 = librosa.load("<your_second_audio_file_here>", sr=44100)
>>> model = Pop2PianoForConditionalGeneration.from_pretrained("sweetcocoa/pop2piano")
>>> processor = Pop2PianoProcessor.from_pretrained("sweetcocoa/pop2piano")
>>> inputs = processor(audio=[audio1, audio2], sampling_rate=[sr1, sr2], return_attention_mask=True, return_tensors="pt")
>>> # Since we now generating in batch(2 audios) we must pass the attention_mask
>>> model_output = model.generate(
... input_features=inputs["input_features"],
... attention_mask=inputs["attention_mask"],
... composer="composer1",
... )
>>> tokenizer_output = processor.batch_decode(
... token_ids=model_output, feature_extractor_output=inputs
... )["pretty_midi_objects"]
>>> # Since we now have 2 generated MIDI files
>>> tokenizer_output[0].write("./Outputs/midi_output1.mid")
>>> tokenizer_output[1].write("./Outputs/midi_output2.mid")
```
- Example of processing multiple audio files in batch (Using `Pop2PianoFeatureExtractor` and `Pop2PianoTokenizer`):
```python
>>> import librosa
>>> from transformers import Pop2PianoForConditionalGeneration, Pop2PianoFeatureExtractor, Pop2PianoTokenizer
>>> # feel free to change the sr to a suitable value.
>>> audio1, sr1 = librosa.load("<your_first_audio_file_here>", sr=44100)
>>> audio2, sr2 = librosa.load("<your_second_audio_file_here>", sr=44100)
>>> model = Pop2PianoForConditionalGeneration.from_pretrained("sweetcocoa/pop2piano")
>>> feature_extractor = Pop2PianoFeatureExtractor.from_pretrained("sweetcocoa/pop2piano")
>>> tokenizer = Pop2PianoTokenizer.from_pretrained("sweetcocoa/pop2piano")
>>> inputs = feature_extractor(
... audio=[audio1, audio2],
... sampling_rate=[sr1, sr2],
... return_attention_mask=True,
... return_tensors="pt",
... )
>>> # Since we now generating in batch(2 audios) we must pass the attention_mask
>>> model_output = model.generate(
... input_features=inputs["input_features"],
... attention_mask=inputs["attention_mask"],
... composer="composer1",
... )
>>> tokenizer_output = tokenizer.batch_decode(
... token_ids=model_output, feature_extractor_output=inputs
... )["pretty_midi_objects"]
>>> # Since we now have 2 generated MIDI files
>>> tokenizer_output[0].write("./Outputs/midi_output1.mid")
>>> tokenizer_output[1].write("./Outputs/midi_output2.mid")
```
## Pop2PianoConfig
[[autodoc]] Pop2PianoConfig
## Pop2PianoFeatureExtractor
[[autodoc]] Pop2PianoFeatureExtractor
- __call__
## Pop2PianoForConditionalGeneration
[[autodoc]] Pop2PianoForConditionalGeneration
- forward
- generate
## Pop2PianoTokenizer
[[autodoc]] Pop2PianoTokenizer
- __call__
## Pop2PianoProcessor
[[autodoc]] Pop2PianoProcessor
- __call__