Skip to main content

Musically motivated piano transcription evaluation

Project description

Towards Musically Informed Evaluation of Piano Transcription Models

PyPI Package DOI

This repository provides a set of evaluation metrics designed for piano transcription evaluation. The metrics are musically informed, meaning they take into account the nuances of musical performance, such as dynamics, note onset, and duration, to offer more differentiated and musically relevant assessments of transcription quality. Note that these metrics are a work in progress and actively being developed/refined/extended. Expect future updates, and feel free to contribute or share feedback!

Setup

The easiest way to install the package is via:

pip install mpteval

Dependencies

  • Python 3.10+
  • Parangonar 3.2.0
  • Partitura 1.8.0

Metrics computation

The following code loads a reference and a predicted MIDI and computes how well the transcription preserves timing information in the performance:

import mpteval
import partitura as pt

from mpteval.metrics_feature.timing import timing_metrics_from_perf

ref_perf = pt.load_performance_midi(mpteval.REF_MID)
pred_perf = pt.load_performance_midi(mpteval.PRED_MID)

timing_metrics = timing_metrics_from_perf(ref_perf, pred_perf)

Demo notebooks

You can find more examples of how the metrics and related functionality can be used in notebooks_demo. Likewise, you can check out our related work on biases in transcription models and [evaluation of large scale transcribed datasets](TODO add link).

Citing

If you use our metrics in your research, please cite the relevant paper:

@inproceedings{hu2024towards,
    title = {{Towards Musically Informed Evaluation of Piano Transcription Models}},
    author = {Hu, Patricia and Mart\'ak, Luk\'a\v{s} Samuel and Cancino-Chac\'on, Carlos and Widmer, Gerhard},
    booktitle = {{Proceedings of the International Society for Music Information Retrieval Conference (ISMIR)}},
    year = {2024}
}

Acknowledgments

This work is supported by the European Research Council (ERC) under the EU’s Horizon 2020 research & innovation programme, grant agreement No. 10101937 ("Whither Music?").

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

mpteval-1.0.1.tar.gz (36.2 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

mpteval-1.0.1-py3-none-any.whl (37.4 kB view details)

Uploaded Python 3

File details

Details for the file mpteval-1.0.1.tar.gz.

File metadata

  • Download URL: mpteval-1.0.1.tar.gz
  • Upload date:
  • Size: 36.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.14.3

File hashes

Hashes for mpteval-1.0.1.tar.gz
Algorithm Hash digest
SHA256 84a7a138554db5634d1267f5360c8dc3a0b5fb04a58a827b40abf872c7d02f36
MD5 f2bcec507fd21fac3c95c1d40037071c
BLAKE2b-256 b83a6157891f8febf755d8b73658a002b01ee304f34117159815801dc6918369

See more details on using hashes here.

File details

Details for the file mpteval-1.0.1-py3-none-any.whl.

File metadata

  • Download URL: mpteval-1.0.1-py3-none-any.whl
  • Upload date:
  • Size: 37.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.14.3

File hashes

Hashes for mpteval-1.0.1-py3-none-any.whl
Algorithm Hash digest
SHA256 2b329884efbdb8bb85a480950bf0ce2473bb1f645c682b18bf0391cfab5fb200
MD5 a907f5178eadb8b5366d62cdae58b7a6
BLAKE2b-256 cbe1527f5494ff3a28177a0b2b1f9f309c9548f22903122730985de27362124a

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page