Datasets:
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"""HuggingFace loading script for the JamALT dataset."""
import csv
from dataclasses import dataclass
import json
import os
from pathlib import Path
from typing import Optional
import datasets
# TODO: Add BibTeX citation
_CITATION = """\
"""
# TODO: Add description of the dataset here
_DESCRIPTION = """\
"""
# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""
# TODO: Add the licence for the dataset here
_LICENSE = ""
_METADATA_FILENAME = "metadata.csv"
_LANGUAGE_NAME_TO_CODE = {
"English": "en",
"French": "fr",
"German": "de",
"Spanish": "es",
}
@dataclass
class JamAltBuilderConfig(datasets.BuilderConfig):
language: Optional[str] = None
with_audio: bool = False
decode_audio: bool = True
sampling_rate: Optional[int] = None
mono: bool = True
# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class JamAltDataset(datasets.GeneratorBasedBuilder):
"""TODO: Short description of my dataset."""
VERSION = datasets.Version("0.0.0")
BUILDER_CONFIG_CLASS = JamAltBuilderConfig
BUILDER_CONFIGS = [JamAltBuilderConfig("default")]
DEFAULT_CONFIG_NAME = "default"
def _info(self):
feat_dict = {
"name": datasets.Value("string"),
"text": datasets.Value("string"),
"language": datasets.Value("string"),
}
if self.config.with_audio:
feat_dict["audio"] = datasets.Audio(
decode=self.config.decode_audio,
sampling_rate=self.config.sampling_rate,
mono=self.config.mono,
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(feat_dict),
supervised_keys=("audio", "text") if "audio" in feat_dict else None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
metadata_path = dl_manager.download(_METADATA_FILENAME)
audio_paths, text_paths, metadata = [], [], []
with open(metadata_path, encoding="utf-8") as f:
for row in csv.DictReader(f):
if (
self.config.language is None
or _LANGUAGE_NAME_TO_CODE[row["Language"]] == self.config.language
):
audio_paths.append("audio/" + row["Filepath"])
text_paths.append(
"lyrics/" + os.path.splitext(row["Filepath"])[0] + ".txt"
)
metadata.append(row)
text_paths = dl_manager.download(text_paths)
audio_paths = (
dl_manager.download(audio_paths) if self.config.with_audio else None
)
return [
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs=dict(
text_paths=text_paths,
audio_paths=audio_paths,
metadata=metadata,
),
),
]
def _generate_examples(self, text_paths, audio_paths, metadata):
if audio_paths is None:
audio_paths = [None] * len(text_paths)
for text_path, audio_path, meta in zip(text_paths, audio_paths, metadata):
name = os.path.splitext(os.path.basename(text_path))[0]
with open(text_path, encoding="utf-8") as text_f:
record = {
"name": name,
"text": text_f.read(),
"language": _LANGUAGE_NAME_TO_CODE[meta["Language"]],
}
if audio_path is not None:
record["audio"] = audio_path
yield name, record
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