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Dataset.md

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Datasets

In this document, we demonstrate our standard data processing pipeline in our team. Following the instructions and runnung corresponding python scripts, you can easily generate and customized your your own dataset.

1. From audio to midi_transcribed

We collect audio clips of piano performance from YouTube.

2. From midi_transcribed to midi_synchronized

In this step, we use madamom for beat/downbeat tracking. Next, We interpolate 480 ticks between two adjacent beats, and map the absolute time into its according tick. Lastly, we infer the tempo changes from the time interval between adjacent beats. We choose beat resolution=480 because it's a common setting in modern DAW. Notice that we don't quantize any timing in this step hence we can keep tiny offset for future purposes.

  • run synchronizer.py

3. From midi_synchronized to midi_analyzed

In this step, we develop in-house rule-based symbolic melody extraction and chord recognition algorithm to obtain desired information. The code for chord recognition are open sourced here. We plan to open rhe code for melody extraction in the future.

  • run analyzer.py

4. From midi_analyzed to Corpus

We quantize everything (duration, velocity, bpm) in this step. Also append the data with EOS(end of sequence) token.

  • run midi2corpus.py

5. From Corpus to Representation

We have 2 kinds of representation - Compound Word (CP) and REMI, and 2 tasks - unconditional and conditional generation, which resulting 4 combinations. Go to corresponding folder <task>/<repre> and run the scripts.

  • run corpus2events.py: to generate human readable tokens and re-arrange data.
  • run events2words.py: to build dictionary and renumber the tokens.
  • run compile.py: to discard disqualified songs that exceeding length limits, reshape the data for transformer-XL, and generate mask for variable length.

AILabs.tw Pop1K7 dataset

Alternatively, you can refer to here to obtain the entire workspace and the pre-processed training data, which originally used in our paper.