Datasets:
File size: 5,642 Bytes
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"""Create a Hugging Face dataset from the JamendoLyrics dataset in its original layout."""
# %%
import glob
import shutil
from pathlib import Path
import datasets
# %%
LANGUAGE_MAP = {"English": "en", "Spanish": "es", "German": "de", "French": "fr"}
# %%
metadata = datasets.load_dataset(
"csv",
data_files={"test": "JamendoLyrics.csv"},
split="test",
)
# %%
features = datasets.Features(
{
"name": datasets.Value("string"),
"file_name": datasets.Value("string"),
"url": datasets.Value("string"),
"artist": datasets.Value("string"),
"title": datasets.Value("string"),
"genre": datasets.Value("string"),
"license_type": datasets.Value("string"),
"language": datasets.Value("string"),
"lyric_overlap": datasets.Value("bool"),
"polyphonic": datasets.Value("bool"),
"non_lexical": datasets.Value("bool"),
"text": datasets.Value("string"),
"words": [
{
"start": datasets.Value("float32"),
"end": datasets.Value("float32"),
"text": datasets.Value("string"),
"line_end": datasets.Value("bool"),
}
],
"lines": [
{
"start": datasets.Value("float32"),
"end": datasets.Value("float32"),
"text": datasets.Value("string"),
}
],
}
)
features_lines_in = datasets.Features(
{
"start_time": datasets.Value(dtype="float32", id=None),
"end_time": datasets.Value(dtype="float32", id=None),
"lyrics_line": datasets.Value(dtype="string", id=None),
}
)
features_words_in = datasets.Features(
{
"word_start": datasets.Value(dtype="float32", id=None),
"word_end": datasets.Value(dtype="float32", id=None),
"line_end": datasets.Value(dtype="float32", id=None),
}
)
# %%
data = {
"name": [x.removesuffix(".mp3") for x in metadata["Filepath"]],
"url": metadata["URL"],
"artist": metadata["Artist"],
"title": metadata["Title"],
"genre": metadata["Genre"],
"license_type": metadata["LicenseType"],
"language": [LANGUAGE_MAP[x] for x in metadata["Language"]],
"lyric_overlap": metadata["LyricOverlap"],
"polyphonic": metadata["Polyphonic"],
"non_lexical": metadata["NonLexical"],
"text": [],
"lines": [],
"words": [],
}
data["file_name"] = [
Path("subsets") / lg / "mp3" / f"{n}.mp3"
for lg, n in zip(data["language"], data["name"])
]
for name in data["name"]:
data["text"].append((Path("lyrics") / (name + ".txt")).read_text())
lines = datasets.load_dataset(
"csv",
features=features_lines_in,
data_files={
"test": str(Path("annotations") / "lines" / glob.escape(name + ".csv"))
},
split="test",
)
data["lines"].append(
[
{
"start": li["start_time"],
"end": li["end_time"],
"text": li["lyrics_line"],
}
for li in lines
]
)
words = datasets.load_dataset(
"csv",
features=features_words_in,
data_files={
"test": str(Path("annotations") / "words" / glob.escape(name + ".csv"))
},
split="test",
)
words_text = (Path("lyrics") / (name + ".words.txt")).read_text().splitlines()
assert len(words) == len(words_text)
assert all(w["line_end"] in [None, w["word_end"]] for w in words)
data["words"].append(
[
{
"start": w["word_start"],
"end": w["word_end"],
"text": text,
"line_end": w["line_end"] is not None,
}
for w, text in zip(words, words_text)
]
)
# %%
dataset = datasets.Dataset.from_dict(data, features=features)
# %%
dataset
# %%
# Divide the MP3 files by language. Hugging Face requires each subset and its metadadta to be in
# a separate directory. However, for backwards compatibility, we also want to keep the top-level
# "mp3" directory and replace the MP3 files with symlinks into the subsets.
# Back up the directory with the original MP3 files
if not Path("mp3_orig").exists():
for path in Path("mp3").glob("*.mp3"):
if path.is_symlink():
target = path.resolve()
path.unlink()
target.rename(path)
Path("mp3").rename("mp3_orig")
elif Path("mp3").exists():
shutil.rmtree("mp3")
Path("mp3").mkdir(exist_ok=True)
subsets_dir = Path("subsets")
if subsets_dir.exists():
shutil.rmtree(subsets_dir)
subsets_dir.mkdir()
# Create language subsets and:
# - hard link the files from mp3_orig to subsets
# - add symlinks from mp3 into subsets
for language in ["en", "es", "de", "fr"]:
subset_dir = subsets_dir / language
subset_dir.mkdir()
subset = dataset.select(
[i for i in range(len(dataset)) if dataset["language"][i] == language]
)
subset_file_names = subset["file_name"]
subset = subset.remove_columns("file_name").add_column(
"file_name", [str(Path(p).relative_to(subset_dir)) for p in subset_file_names]
)
subset.to_json(subset_dir / "metadata.jsonl")
(subset_dir / "mp3").mkdir()
for name in subset["name"]:
(subset_dir / "mp3" / f"{name}.mp3").hardlink_to(
Path("mp3_orig") / f"{name}.mp3"
)
(Path("mp3") / f"{name}.mp3").symlink_to(
Path("..") / subset_dir / "mp3" / f"{name}.mp3"
)
# Create the top-level data file for the "all" config
dataset.to_json("metadata.jsonl")
# %%
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