Datasets:
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Text Generation
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Sub-tasks:
language-modeling
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README.md
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---
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license:
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- cc
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multilinguality:
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- other-music
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pretty_name: Mutopia Guitar Dataset
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task_categories:
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- text-generation
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task_ids:
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- language-modeling
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---
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# Mutopia Guitar Dataset
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## Table of Contents
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- [Dataset Card Creation Guide](#mutopia-guitar-dataset)
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- [Table of Contents](#table-of-contents)
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- [Dataset Description](#dataset-description)
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- [Dataset Summary](#dataset-summary)
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
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- [Dataset Structure](#dataset-structure)
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- [Data Instances](#data-instances)
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- [Data Fields](#data-fields)
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- [Data Splits](#data-splits)
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## Dataset Description
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- **Homepage:** [Mutopia Project](https://www.mutopiaproject.org/)
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- **Repository implementation of the paper:** [MMM: Exploring Conditional Multi-Track Music Generation with the Transformer and the Johann Sebastian Bach Chorales Dataset](https://github.com/AI-Guru/MMM-JSB)
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- **Based on Paper:** [MMM: Exploring Conditional Multi-Track Music Generation with the Transformer](https://arxiv.org/abs/2008.06048)
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- **Point of Contact:** [Juan Carlos Piñeros](https://www.linkedin.com/in/juancarlospinerosp/)
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### Dataset Summary
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Mutopia guitar dataset consists of the soloist guitar pieces of the [Mutopia Project](https://www.mutopiaproject.org/). I encoded the MIDI files into text tokens using the excellent [implementation](https://github.com/AI-Guru/MMM-JSB) of Dr. Tristan Beheren of the paper: [MMM: Exploring Conditional Multi-Track Music Generation with the Transformer](https://arxiv.org/abs/2008.06048).
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The dataset mainly contains guitar music from western classical composers, such as Sor, Aguado, Carcassi, and Giuliani.
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### Supported Tasks and Leaderboards
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Anyone interested can use the dataset to train a model for symbolic music generation, which consists in treating symbols for music sounds (notes) as text tokens. Then, one can implement a generative model using NLP techniques, such as Transformers.
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## Dataset Structure
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### Data Instances
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Each guitar piece is represented as a line of text that contains a series of tokens, for instance:
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PIECE_START: Where the piece begins
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PIECE_ENDS: Where the piece ends
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TIME_SIGNATURE: Time signature for the piece
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BPM: Tempo of the piece
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BAR_START: Begining of a new bar
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NOTE_ON: Start of a new musical note specifying its MIDI note number
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TIME_DELTA: Duration until the next event
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NOTE_OFF: End of musical note specifying its MIDI note number
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```
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{
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'text': PIECE_START TIME_SIGNATURE=2_4 BPM=74 TRACK_START INST=0 DENSITY=4 BAR_START NOTE_ON=52 TIME_DELTA=2.0 NOTE_OFF=52 NOTE_ON=45 NOTE_ON=49 TIME_DELTA=2.0 NOTE_OFF=49 NOTE_ON=52 TIME_DELTA=2.0 NOTE_OFF=45 NOTE_ON=47 NOTE_OFF=52 NOTE_ON=44 TIME_DELTA=2.0,
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...
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}
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```
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### Data Fields
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- `text`: Sequence of tokens that represent the guitar piece as explained in the paper [MMM: Exploring Conditional Multi-Track Music Generation with the Transformer](https://arxiv.org/abs/2008.06048).
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### Data Splits
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There are, at this moment, 395 MIDI guitar files in the Mutopia Project. I removed files of pieces that were not music for soloist guitar. After this removal, there were 372 MIDI files.
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I used an 80/20 split and augmented the training dataset by transposing the piece 1 octave above and below (24 semitones). The final result is then:
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**Train dataset:** 7325 pieces
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**Test dataset:** 74 pieces
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test.txt → juancopi81--mutopia_guitar_dataset/text-test.parquet
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train.txt → juancopi81--mutopia_guitar_dataset/text-train.parquet
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