README.md · sander-wood/wikimusictext at feb3cdd76ff1de7d100cab091abd7a0132f6aca1

metadata

license: mit
task_categories:
  - text-classification
  - text2text-generation
pretty_name: wikimt
size_categories:
  - 1K<n<10K
language:
  - en
tags:
  - music

Dataset Summary

In CLaMP: Contrastive Language-Music Pre-training for Cross-Modal Symbolic Music Information Retrieval, we introduce WikiMusicText (WikiMT), a new dataset for the evaluation of semantic search and music classification. It includes 1010 lead sheets in ABC notation sourced from Wikifonia.org, each accompanied by a title, artist, genre, and description. The title and artist information is extracted from the score, whereas the genre labels are obtained by matching keywords from the Wikipedia entries and assigned to one of the 8 classes (Jazz, Country, Folk, R&B, Pop, Rock, Dance, and Latin) that loosely mimic the GTZAN genres. The description is obtained by utilizing BART-large to summarize and clean the corresponding Wikipedia entry. Additionally, the natural language information within the ABC notation is removed.

WikiMT is a unique resource to support the evaluation of semantic search and music classification. However, it is important to acknowledge that the dataset was curated from publicly available sources, and there may be limitations concerning the accuracy and completeness of the genre and description information. Further research is needed to explore the potential biases and limitations of the dataset and to develop strategies to address them.

Certainly, let's maintain a more concise and to-the-point style in GitHub readme fashion:

Apologies for the oversight. Here's the revised section that includes the "provided code":

How to Obtain ABC Notation Music Scores

To access ABC notation music scores from the WikiMT dataset, follow these steps:

Download WikiMT Metadata: Get the metadata file for WikiMT here. This file has important dataset information.
Get the xml2abc.py Script: Download the xml2abc.py script from this link and save it in your local directory.
Find Wikifonia MusicXML Data Link: Locate a download link for the Wikifonia dataset in MusicXML format (with .mxl extension). You can search the internet or Wikifonia-related websites for it.
Run the Provided Code: After acquiring the WikiMT metadata, the xml2abc.py script, and the Wikifonia MusicXML data link, execute the provided Python code below. It'll prompt you for the Wikifonia data URL, like this:

Enter the Wikifonia URL: [Paste your URL here]

Paste your URL pointing to the Wikifonia.zip file and press Enter. The code will take care of downloading, processing, and extracting the music content, making it ready for your research or applications.

import subprocess
import os
import json
import zipfile
import io

# Install the required packages if they are not installed
try:
    from unidecode import unidecode
except ImportError:
    subprocess.check_call(["python", '-m', 'pip', 'install', 'unidecode'])
    from unidecode import unidecode

try:
    from tqdm import tqdm
except ImportError:
    subprocess.check_call(["python", '-m', 'pip', 'install', 'tqdm'])
    from tqdm import tqdm

try:
    import requests
except ImportError:
    subprocess.check_call(["python", '-m', 'pip', 'install', 'requests'])
    import requests

def filter(lines):
    # Filter out all lines that include language information
    music = ""
    for line in lines:
        if line[:2] in ['A:', 'B:', 'C:', 'D:', 'F:', 'G', 'H:', 'I:', 'N:', 'O:', 'R:', 'r:', 'S:', 'T:', 'W:', 'w:', 'X:', 'Z:'] \
        or line=='\n' \
        or (line.startswith('%') and not line.startswith('%%score')):
            continue
        else:
            if "%" in line and not line.startswith('%%score'):
                line = "%".join(line.split('%')[:-1])
                music += line[:-1] + '\n'
            else:
                music += line + '\n'
    return music

def load_music(filename):
    # Convert the file to ABC notation
    p = subprocess.Popen(
        f'cmd /u /c python xml2abc.py -m 2 -c 6 -x "{filename}"',
        stdout=subprocess.PIPE,
        stderr=subprocess.PIPE,
        shell=True
    )
    out, err = p.communicate()

    output = out.decode('utf-8').replace('\r', '')  # Capture standard output
    music = unidecode(output).split('\n')
    music = filter(music).strip()

    return music

def download_and_extract(url):
    print(f"Downloading {url}")

    # Send an HTTP GET request to the URL and get the response
    response = requests.get(url, stream=True)

    if response.status_code == 200:
        # Create a BytesIO object and write the HTTP response content into it
        zip_data = io.BytesIO()
        total_size = int(response.headers.get('content-length', 0))
        
        with tqdm(total=total_size, unit='B', unit_scale=True) as pbar:
            for data in response.iter_content(chunk_size=1024):
                pbar.update(len(data))
                zip_data.write(data)

        # Use the zipfile library to extract the file
        print("Extracting the zip file...")
        with zipfile.ZipFile(zip_data, "r") as zip_ref:
            zip_ref.extractall("")
        
        print("Done!")

    else:
        print("Failed to download the file. HTTP response code:", response.status_code)

# Download the Wikifonia dataset
wikifonia_url = input("Enter the Wikifonia URL: ")
download_and_extract(wikifonia_url)

wikimusictext = []
with open("wikimusictext.jsonl", "r", encoding="utf-8") as f:
    for line in f.readlines():
        wikimusictext.append(json.loads(line))

updated_wikimusictext = []

for song in tqdm(wikimusictext):
    filename = song["artist"] + " - " + song["title"] + ".mxl"
    filepath = os.path.join("Wikifonia", filename)
    song["music"] = load_music(filepath)
    updated_wikimusictext.append(song)

with open("wikimusictext.jsonl", "w", encoding="utf-8") as f:
    for song in updated_wikimusictext:
        f.write(json.dumps(song, ensure_ascii=False)+"\n")

By following these steps and running the provided code, you can efficiently access ABC notation music scores from the WikiMT dataset. Just ensure you have the metadata, the xml2abc.py script, and the correct download link before starting. Enjoy your musical journey!

Copyright Disclaimer

WikiMT was curated from publicly available sources, and all rights to the original content and data remain with their respective copyright holders. The dataset is made available for research and educational purposes, and any use, distribution, or modification of the dataset should comply with the terms and conditions set forth by the original data providers.

BibTeX entry and citation info

@misc{wu2023clamp,
      title={CLaMP: Contrastive Language-Music Pre-training for Cross-Modal Symbolic Music Information Retrieval}, 
      author={Shangda Wu and Dingyao Yu and Xu Tan and Maosong Sun},
      year={2023},
      eprint={2304.11029},
      archivePrefix={arXiv},
      primaryClass={cs.SD}
}