---
title: 'GiantMIDI-Piano: Large-Scale Solo Piano MIDI Dataset'
url: https://www.emergentmind.com/topics/giantmidi-piano
type: topic
---

# GiantMIDI-Piano: Large-Scale Solo Piano MIDI Dataset

GiantMIDI-Piano is a large-scale symbolic dataset for classical solo piano music created by transcribing internet audio recordings into MIDI and organizing them with composer- and work-level metadata extracted from the International Music Score Library Project (IMSLP). Introduced by Kong et al. as a resource for music information retrieval, piano transcription, symbolic music generation, computational musicology, and expressive performance analysis, it contains **38,700,838 transcribed notes**, **10,855 unique solo piano works**, **2,786 composers**, and **1,237 hours** of music; a curated subset contains **7,236 works**, **1,787 composers**, **24,253,495 notes**, and **875 hours** [2010.07061].

## 1. Scope, identity, and corpus composition

GiantMIDI-Piano addresses what Kong et al. describe as a lack of large-scale symbolic datasets for classical piano music. The dataset is explicitly positioned as a classical **solo piano** corpus in MIDI format rather than a mixed-instrument symbolic archive, and this specialization is central to both its construction and its intended uses [2010.07061].

The full release contains **10,855 unique solo piano works composed by 2,786 composers** and totals **38,700,838 transcribed notes** across **1,237 hours**. The paper also reports a curated subset with stricter metadata filtering, containing **7,236 works** by **1,787 composers**, totaling **24,253,495 notes** across **875 hours**. In the full dataset, **90% live-performance MIDI files** and **10% sequence-input MIDI files** are reported; the curated subset is reported as **89% performed MIDI** [2010.07061].

The dataset is described in the paper as the **largest piano MIDI dataset so far**. Its design distinguishes it from aligned performance corpora such as MAESTRO: GiantMIDI-Piano is not based on paired studio recordings and score-aligned MIDI capture, but on a pipeline that begins with bibliographic metadata, retrieves audio from the web, filters for solo piano, and then applies automatic transcription [2010.07061].

This scale has made GiantMIDI-Piano relevant beyond its original release. A later composer-classification study uses it as a transcription-based dataset for large-scale MIDI-based composer classification and reports experiments with up to **100 composers** [2010.14805]. A later multimodal LLM study, MIDI-LLaMA, uses GiantMIDI-Piano as the basis for a symbolic music–text corpus and states that the dataset contains **10,855 MIDI files from 2,786 composers** [2601.21740].

## 2. Data acquisition pipeline

The construction pipeline begins with **IMSLP**, from which the authors parsed composer pages and work lists. They report extracting **143,701 music works** and **18,067 composers**. For composers, they also attempted to extract birth year, death year, and nationality from biography links; for a smaller set of **2,786 composers**, they manually labeled **2,291 birth years**, **2,254 death years**, and **2,115 nationalities**, with unknown values retained as `"unknown"` [2010.07061].

Audio retrieval proceeds by querying YouTube for each IMSLP work using a keyword composed of the **composer first name**, **composer surname**, and **work name**. The pipeline selects the **first returned result** for each query, producing **60,724 audio recordings** downloaded from the **143,701 works** [2010.07061].

To decide whether a returned title matches the intended work, the paper uses a modified Jaccard similarity. If $X$ is the set of words in the search keyword and $Y$ is the set of words in the returned YouTube title, the conventional Jaccard index is described as

$$
J = \frac{|X \cap Y|}{|X \cup Y|}
$$

but the paper instead defines

$$
J = \frac{|X \cap Y|}{|X|}
$$

and applies a threshold of **0.6**, so that if $J > 0.6$, the keyword and title are considered matched [2010.07061].

Because the retrieved recordings are not guaranteed to be solo piano, the authors train an **audio-based solo piano detector**. The detector operates on **1-second audio segments**, uses **log mel spectrograms** as features, and applies a **CNN with four convolutional layers**. Each convolutional layer uses **\(3 \times 3\) convolution**, **batch normalization**, and **ReLU**, and the detector is trained with **binary cross-entropy**. At inference, segment-level predictions are averaged, and an audio recording is classified as solo piano if the probability is **> 0.5** [2010.07061].

From the **60,724 downloaded recordings**, the detector identifies **10,855 solo piano recordings** by **2,786 composers**. These recordings are then transcribed into MIDI and constitute the full GiantMIDI-Piano dataset [2010.07061].

To improve metadata precision, the authors create a curated subset by requiring that the downloaded audio title contain the **surname of the composer**. This surname constraint yields **7,236 works** by **1,787 composers**, and the metadata includes a `youtube_title_contains_surname` flag [2010.07061].

## 3. Symbolic representation and transcription system

Each GiantMIDI-Piano file is a transcription-derived MIDI representation rather than a score-native encoding. The paper states that each transcribed MIDI file contains **onset**, **offset**, **pitch**, and **velocity** attributes of piano notes, together with **pedals** represented as sustain-pedal onset/offset events [2010.07061]. This note- and control-level structure is one reason later work treats GiantMIDI-Piano as well suited to symbolic music understanding and composer classification [2010.14805; 2601.21740].

The transcription backend is the authors’ open-source **high-resolution piano transcription system**, trained on the **MAESTRO v2.0.0 training set**. The model uses a **CRNN** with **8 convolutional layers** and **2 bi-directional GRU layers**, and has separate output modules for **frame-wise pitch classification**, **onset regression**, **offset regression**, and **velocity regression**. The output dimension is **88**, corresponding to the 88 piano keys. For pedal transcription, the model is similar except that it uses a single output for pedal onset/offset [2010.07061].

The inference front end converts audio to **mono** at **16 kHz**. The STFT uses a **Hann window size = 2048** and **hop size = 160**, giving a time resolution of **100 frames per second**. The mel front end uses **229 mel bins**, and the model consumes **log mel spectrograms**. Predicted outputs are then post-processed into MIDI events [2010.07061].

A limitation explicitly noted in the paper is that the dataset does **not** provide **pitch spellings distinguishing enharmonics**, **beats**, **time signatures**, **key signatures**, or **scores**, and it does **not disentangle score content from performance expression** [2010.07061]. These omissions are important when interpreting the symbolic content: GiantMIDI-Piano is a rich note-event corpus, but not a fully notated score corpus.

## 4. Evaluation, quality control, and error characterization

The release evaluates three components: solo piano detection, metadata accuracy, and transcription quality [2010.07061].

For solo piano detection, the authors manually labeled **200 randomly selected music works** from the **60,724 downloaded recordings**. At threshold **0.5**, the detector achieved **Precision: 89.66%**, **Recall: 86.67%**, and **F1: 88.14%** [2010.07061].

For metadata accuracy, the authors manually checked **200 solo piano works** from the full dataset. They report that **174 / 200** were correctly matched, corresponding to **Metadata accuracy: 87%**. After applying the surname constraint, **136 / 140** were correctly matched, corresponding to **Precision: 97.14%**. In the 200-example sample, the paper reports **180 live performances** and **20 sequenced MIDI files** [2010.07061].

The paper also provides composer-specific retrieval accuracies before and after surname filtering.

| Composer | Before surname filtering | After surname constraint |
|---|---:|---:|
| Bach | 59% | 75% |
| Mozart | 71% | 82% |
| Beethoven | 54% | 94% |
| Chopin | 37% | 82% |
| Liszt | 90% | 96% |
| Debussy | 76% | 90% |

These figures show that metadata precision varies substantially by composer and that surname filtering improves precision markedly [2010.07061].

The transcription system itself is evaluated on the **MAESTRO test set**. The reported metrics are **Onset F1: 96.72%**, **Onset + offset F1: 82.47%**, and **Onset + offset + velocity F1: 80.92%**. For pedals, the reported metrics are **Pedal onset F1: 91.86%** and **Pedal onset + offset F1: 86.58%**. The paper states that this system beats the prior Onsets and Frames system in onset F1, comparing **94.80%** to **96.72%** [2010.07061].

Because GiantMIDI-Piano lacks ground-truth aligned reference MIDI for its recordings, the authors assess quality on the dataset itself through an **alignment error rate (ER)**:

$$
ER = \frac{S + D + I}{N}
$$

where $S$ denotes substitutions, $D$ deletions, $I$ insertions, and $N$ the number of reference notes [2010.07061].

To interpret GiantMIDI-Piano’s error, they write

$$
ER_G = e_{\text{performance}_G} + e_{\text{transcription}_G} + e_{\text{alignment}_G}
$$

and, for MAESTRO,

$$
ER_M = e_{\text{performance}_M} + e_{\text{alignment}_M}.
$$

They then define a relative error,

$$
r = ER_G - ER_M \approx e_{\text{transcription}_G},
$$

as a rough estimate of the transcription error contribution in GiantMIDI-Piano [2010.07061].

On **52 shared works**, the paper reports **MAESTRO median ER: 0.061**, **GiantMIDI-Piano median ER: 0.154**, and **Relative median ER: 0.094**. Median component rates are also reported: for MAESTRO, **D = 0.009**, **I = 0.024**, **S = 0.018**, **ER = 0.061**; for GiantMIDI-Piano, **D = 0.015**, **I = 0.051**, **S = 0.069**, **ER = 0.154**; and for the relative difference, **D = 0.006**, **I = 0.026**, **S = 0.047**, **ER = 0.094**. The paper notes that insertions are more common than deletions in the relative comparison [2010.07061].

## 5. Musical analysis and computational use cases

A central claim of the release is that GiantMIDI-Piano is large enough for **composer- and era-level musical analysis**. To demonstrate this, the paper analyzes works by **J. S. Bach**, **Mozart**, **Beethoven**, **Chopin**, **Liszt**, and **Debussy** using pitch-class, interval, trichord, and tetrachord statistics [2010.07061].

For pitch classes, the paper uses the twelve classes

$$
\{C, C\sharp, D, D\sharp, E, F, F\sharp, G, G\sharp, A, A\sharp, B\}
$$

mapped to **0–11**. Reported findings include: **Bach** showing strong use of **D, E, G, A**; **Mozart** emphasizing **C, D, F, G** and using more **A♯/B♭**; **Beethoven** favoring **C, D, G**; **Chopin** favoring **D♯/E♭** and **G♯/A♭**; and **Liszt** and **Debussy** using pitch classes more uniformly. The paper further states that **Romantic and later composers** tend to explore more distant keys than **Baroque/classical composers** [2010.07061].

For intervals, the paper defines

$$
\Delta = y_n - y_{n-1}
$$

where $y_n$ is the MIDI pitch of note $n$. It considers both **melodic intervals** between successive notes and **harmonic intervals** between notes sounding at the same time, examining ordered intervals from **-11 to 11**. The paper reports that all six composers use **major second** and **minor third** most, that intervals are not symmetric around 0, that **Bach** and **Mozart** use more downward major seconds than upward ones, that repeated notes (**interval 0**) are less common in **Bach**, that **major seventh** and **tritone** are least used across composers, and that Romantic/later composers use intervals more uniformly [2010.07061].

For trichords, notes within a **50 ms window** are grouped as chords; windows are non-overlapping; notes can belong to only one chord; only 3-note sets are kept; chords are transposed to **C-based prime forms**; and equivalent chords are merged. Examples given are the major triad $\{0,4,7\}$ and minor triad $\{0,3,7\}$. The paper reports that all composers use the **major triad $\{0,4,7\}$** most, the minor triad $\{0,3,7\}$ is usually second, **Liszt** uses more augmented triads $\{0,4,8\}$, and **Debussy** uses more $\{0,2,5\}$ and $\{0,2,4\}$ [2010.07061].

For tetrachords, the same **50 ms chord windowing** is used, but only 4-note sets are retained and chords are transposed to root position. Examples include the dominant seventh $\{0,4,7,10\}$, diminished seventh $\{0,3,6,9\}$, and minor seventh $\{0,3,7,10\}$. The paper reports that **Bach, Beethoven, Mozart, and Chopin** use dominant seventh most, **Liszt** uses diminished seventh most, and **Debussy** uses minor seventh most; it also states that Debussy’s tetrachord distribution is notably different from the others [2010.07061].

The paper presents these analyses as evidence that the dataset can support computational musicology. A plausible implication is that the corpus scale is sufficient to stabilize style statistics for many composers, although the release itself demonstrates this only for the six-composer case study.

## 6. Downstream adoption, related work, and limitations

GiantMIDI-Piano has been used directly in later symbolic-music research. In large-scale MIDI-based composer classification, it serves as the underlying transcription-based dataset for systems that use **piano rolls**, **onset rolls**, and **velocity rolls** as input representations and **deep neural networks** as classifiers. Using **convolutional recurrent neural networks**, that study reports **10-composer** and **100-composer** classification accuracies of **0.648** and **0.385** on **30-second clips**, and **0.739** and **0.489** on **music pieces**, respectively; it also states that the MIDI-based system outperforms several audio-based baseline classification systems [2010.14805].

In multimodal symbolic music understanding, GiantMIDI-Piano is the basis for MIDI-LLaMA’s music–text corpus. That work states that GiantMIDI-Piano contains **10,855 MIDI files from 2,786 composers** and uses it as a source of **classical piano performances** paired with language supervision. It builds a **fine-grained annotation pipeline** from each piece’s **title and composer**, retrieved contextual text from classical-music websites, GPT-4o extraction of **genre, style, composition background, expressive intent, and perceived emotion**, and manually verified outputs. In a stratified sample of **100 works**, two experts judged annotations acceptable at **89% acceptance for categorical tags** and **93% for descriptive annotations**. The corpus is further expanded by extracting **three non-overlapping 20-second clips** from long pieces, aligned to **bar lines** when possible, yielding **29,409 clips** and about **2.3 million Q/A pairs** [2601.21740].

The dataset also sits within a broader ecosystem of piano and MIDI resources. A large-scale MIDI-to-sheet retrieval study does not mention GiantMIDI-Piano explicitly, but it is relevant in spirit because it addresses scalable methods for linking large symbolic piano corpora to IMSLP sheet music via modified symbolic bootleg scores and reverse-index retrieval [2004.10391]. A later large-scale symbolic generation study cites GiantMIDI-Piano as one of the available piano-MIDI datasets, although it does not use it experimentally [2511.07268]. A large general-MIDI corpus study, GigaMIDI, explicitly references GiantMIDI-Piano as a dataset of **AI-transcribed piano tracks** that may contain expressive performance nuances, but excludes it from that study’s own expressive-performance ground-truth curation [2502.17726].

Several misconceptions are clarified by the primary release. GiantMIDI-Piano is not a score-aligned, notation-complete corpus; it is a transcription-derived MIDI dataset without **beats, time signatures, key signatures, or scores** [2010.07061]. It is also not restricted to manually verified metadata throughout the entire full release; instead, the curated subset provides the stricter precision-oriented variant through the composer-surname constraint [2010.07061]. Finally, although the data contain expressive attributes such as **velocity** and **pedals**, the paper explicitly notes that the dataset does **not disentangle score content from performance expression**, so analytical use requires care when separating compositional regularities from performance-derived variation [2010.07061].

Source: https://www.emergentmind.com/topics/giantmidi-piano