update files and README (#7)

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- files and README updated (fca54f38d780824076230c5a048969bbf972978e)

Co-authored-by: Susnato Dhar <susnato@users.noreply.huggingface.co>

Files changed (10) hide show

README.md +66 -12
demo.mp3 +0 -0
preprocessor_config.json +1 -4
tokenizer_config.json +0 -4
vocab.json +1 -18
vocab_note.json +0 -1
vocab_params.json +0 -18
vocab_special.json +0 -1
vocab_time.json +0 -1
vocab_velocity.json +0 -1

README.md CHANGED Viewed

@@ -12,44 +12,98 @@ music.
 Pop2Piano was proposed in the paper [Pop2Piano : Pop Audio-based Piano Cover Generation](https://arxiv.org/abs/2211.00895) by Jongho Choi and Kyogu Lee.
-Inspired by [T5](https://arxiv.org/abs/1910.10683), Pop2Piano
-is the first model to generate a piano cover directly from pop audio without using melody and
-chord extraction modules.
 ## Model Sources
-- [**Original Repository**](https://github.com/sweetcocoa/pop2piano)
 - [**Paper**](https://arxiv.org/abs/2211.00895)
-- [**Demo**]# TODO (after the ongoing PR is merged)
 # Usage
-First, install the required packages:
 ```
-pip install --upgrade transformers
 ```
 ## Pop music to Piano
- TODO (after the ongoing PR is merged)
 ## Example
-### Pop Music
 <audio controls>
     <source src="https://datasets-server.huggingface.co/assets/sweetcocoa/pop2piano_ci/--/sweetcocoa--pop2piano_ci/test/0/audio/audio.mp3" type="audio/mpeg">
 Your browser does not support the audio element.
 </audio>
-### Generated MIDI
-TODO (after the MIDI version is uploaded to the same repo above)
 ## Tips
- TODO
 # Citation

 Pop2Piano was proposed in the paper [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.
 ## Model Sources
 - [**Paper**](https://arxiv.org/abs/2211.00895)
+- [**Original Repository**](https://github.com/sweetcocoa/pop2piano)
+- [**HuggingFace Space Demo**](https://huggingface.co/spaces/sweetcocoa/pop2piano)
 # Usage
+To use Pop2Piano, you will need to install the 🤗 Transformers library, as well as the following third party modules:
 ```
+pip install https://github.com/huggingface/transformers.git
+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.
 ## Pop music to Piano
+### Code Example
+- Using your own Audio
+```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")
+```
+- Audio from Hugging Face Hub
+```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
+ Here we present an example of generated MIDI.
+- Actual Pop Music
 <audio controls>
     <source src="https://datasets-server.huggingface.co/assets/sweetcocoa/pop2piano_ci/--/sweetcocoa--pop2piano_ci/test/0/audio/audio.mp3" type="audio/mpeg">
 Your browser does not support the audio element.
 </audio>
+- Generated MIDI
+<audio controls>
+    <source src="https://datasets-server.huggingface.co/assets/sweetcocoa/pop2piano_ci/--/sweetcocoa--pop2piano_ci/test/1/audio/audio.mp3" type="audio/mpeg">
+Your browser does not support the audio element.
+</audio>
 ## Tips
+1. Pop2Piano is an Encoder-Decoder based model like T5.
+2. Pop2Piano can be used to generate midi-audio files for a given audio sequence.
+3. Choosing different composers in `Pop2PianoForConditionalGeneration.generate()` can lead to variety of different results.
+4. Setting the sampling rate to 44.1 kHz when loading the audio file can give good performance.
+5. Though Pop2Piano was mainly trained on Korean Pop music, it also does pretty well on other Western Pop or Hip Hop songs.
 # Citation

demo.mp3 DELETED Viewed

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preprocessor_config.json CHANGED Viewed

@@ -8,10 +8,7 @@
   "mel_is_conditioned": true,
   "padding_value": 0,
   "eos_token_id": 1,
-  "vocab_size_special": 4,
-  "vocab_size_note": 128,
-  "vocab_size_velocity": 2,
-  "vocab_size_time": 100,
   "window_size":4096,
   "hop_length":1024,
   "min_frequency":10.0,

   "mel_is_conditioned": true,
   "padding_value": 0,
   "eos_token_id": 1,
+  "default_velocity": 77,
   "window_size":4096,
   "hop_length":1024,
   "min_frequency":10.0,

tokenizer_config.json CHANGED Viewed

@@ -5,9 +5,5 @@
   "eos_token": "1",
   "pad_token": "0",
   "bos_token": "2",
-  "vocab_size_special" : 4,
-  "vocab_size_note" : 128,
-  "vocab_size_velocity" : 2,
-  "vocab_size_time" : 100,
   "num_bars" : 2
 }

   "eos_token": "1",
   "pad_token": "0",
   "bos_token": "2",
   "num_bars" : 2
 }

vocab.json CHANGED Viewed

@@ -1,18 +1 @@
-{"TOKEN_SPECIAL" : 0,
-  "TOKEN_NOTE" : 1,
-  "TOKEN_VELOCITY" : 2,
-  "TOKEN_TIME" : 3,
-  "DEFAULT_VELOCITY" : 77,
-  "TIE" : 2,
-  "EOS" : 1,
-  "PAD" : 0,
-  "vocab_size_special": 4,
-  "vocab_size_note": 128,
-  "vocab_size_velocity": 2,
-  "vocab_size_time": 100,
-  "num_bars": 2,
-  "unk_token": "-1",
-  "eos_token": "1",
-  "pad_token": "0",
-  "bos_token": "2"
-}


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