Transition-aware is an CRNN-based piano transcription model, inferring onset, pitch of 88 notes from raw audios. Compared with the existing models, like Onsets and Frames and High-resolution, Transition-aware is a more stable and robust approach as mentioned in our paper.
In this repository, the inference code of Transition-aware model and 50 pieces music of OMAPS dataset are provided. The training part of our code and the complete OMAPS dataset will be available when the paper is published. More details about OMAPS (Ordinary MIDI Aligned Piano Sounds) dataset will be introduced at the end of this document.
Transition-aware model is developed with python3.6, tensorflow-gpu1.10, and other python packages. The development environment dependencies can be easily installed simply by running the following command. You'd better use Anaconda to create a new virtual environment for installation.
pip install -r requirements.txt-
First you need to clone this repository
git clone [email protected]:itec-hust/transition-aware.git
This will take a time for our model's checkpoint files and half of our OMAPS dataset are in this repository.
After download this repository, decompress the compressed package under the checkpoint folder to get the onset and pitch files containing the checkpoint file, and then copy the two folders to the code/checkpoint folder. The 50 pieces music of OMAPS dataset is under the OMAPS folder. You can look it first if you are interested in it.
Please cd to code/ before beginning each step below.
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Transcribe the onset-event probabilities.
python predict_onset.py --ckpt_path="checkpoint/onset/model.ckpt-218000" --audio_dir="your audio path" --predict_dir="your path to save tfrecords" --save_raw_dir="your path to save onset-event probabilities"
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Transcribe the onset probabilities of 88 notes.
python predict_pitch.py --ckpt_path="checkpoint/pitch/model.ckpt-204000" --audio_dir="your audio path" --predict_dir="your path to save tfrecords" --save_raw_dir="your path to save onset probabilities"
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Using peak selection to get the final (onset, pitch) sequence from the onset-event probabilities and onset probabilities.
python raw_res.py --result_path="your path to save the results" --raw_onset_path="your path to save onset-event probabilities" --raw_pitch_path="your path to save onset probabilities"
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Evaluate our model's performance on a dataset.
python eval_result.py --label_path="your label path" --result_path="your path to save the results"
Your labels must be in the text format, with (onset, offset, pitch) three columns. If your label is in the MIDI format, use our code/convert_midi_to_txt.py first to get the text format label.
python convert_midi_to_txt.py --midi_path="your midi label path" --txt_path="your path to save the text format label"
OMAPS dataset stands for Ordinary MIDI Aligned Piano Sounds dataset. OMAPS dataset is recorded from an ordinary electronic Yamaha piano P-115 played by a professional musician under the general recording environment. The MIDI derived from the electronic piano is used as the annotation. This dataset contains 106 pieces of different music, a total of 216 minutes. It is used to evaluate the performance of piano transcription algorithms in an ordinary recording environment. OMAPS dataset is not intended to replace the existing piano transcription dataset MAESTRO or MAPS, but is developed for our multi modal transcription research of video and audio fusion. The complete OMAPS dataset contains audio and video, but at present we only open source audio part and labels.
Here is a picture of our recording environment. Yamaha P-115 piano and logitech C922 PRO HD stream webcam are used for recording in an quiet room. The sampling rate of our audio is 44100 Hz and quality of our video is 1080P/30fps. You can have a look at OMAPS dataset under the OMAPS folder of this repository. The complete audio part of OMAPS dataset will be available when the paper is published. 50 pieces of music and labels are provided now!
This is an sample of our OMAPS dataset. It may not be displayed on GitHub. You can download and listen to it.