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import datasets
import csv
import os
# For a future citation perhaps?
# _CITATION = """\
# @inproceedings{luong-vu-2016-non,
# title = "A non-expert {K}aldi recipe for {V}ietnamese Speech Recognition System",
# author = "Luong, Hieu-Thi and
# Vu, Hai-Quan",
# booktitle = "Proceedings of the Third International Workshop on Worldwide Language Service Infrastructure and Second Workshop on Open Infrastructures and Analysis Frameworks for Human Language Technologies ({WLSI}/{OIAF}4{HLT}2016)",
# month = dec,
# year = "2016",
# address = "Osaka, Japan",
# publisher = "The COLING 2016 Organizing Committee",
# url = "https://aclanthology.org/W16-5207",
# pages = "51--55",
# }
# """
_DESCRIPTION = """\
Dataset consisting of isolated beatbox samples ,
reimplementation of the dataset from the following
paper: BaDumTss: Multi-task Learning for Beatbox Transcription
"""
_HOMEPAGE = "https://doi.org/10.1007/978-3-031-05981-0_14"
_LICENSE = "MIT"
_DATA_URL = "https://huggingface.co/datasets/maxardito/beatbox/resolve/main/dataset"
class BeatboxDataset(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
def _info(self):
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
features=datasets.Features({
"path":
datasets.Value("string"),
"class":
datasets.Value("string"),
"audio":
datasets.Audio(sampling_rate=16_000),
}),
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
# citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
dl_manager.download_config.ignore_url_params = True
audio_path = dl_manager.download(_DATA_URL)
local_extracted_archive = dl_manager.extract(
audio_path) if not dl_manager.is_streaming else None
path_to_clips = "dataset"
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"local_extracted_archive":
local_extracted_archive,
"audio_files":
dl_manager.iter_archive(audio_path),
"metadata_path":
dl_manager.download_and_extract(
"dataset/metadata_train.csv.gz"),
"path_to_clips":
path_to_clips,
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"local_extracted_archive":
local_extracted_archive,
"audio_files":
dl_manager.iter_archive(audio_path),
"metadata_path":
dl_manager.download_and_extract(
"dataset/metadata_test.csv.gz"),
"path_to_clips":
path_to_clips,
},
),
]
def _generate_examples(
self,
local_extracted_archive,
audio_files,
metadata_path,
path_to_clips,
):
"""Yields examples."""
data_fields = list(self._info().features.keys())
metadata = {}
with open(metadata_path, "r", encoding="utf-8") as f:
reader = csv.DictReader(f)
for row in reader:
row["path"] = os.path.join(path_to_clips, row["path"])
# if data is incomplete, fill with empty values
for field in data_fields:
if field not in row:
row[field] = ""
metadata[row["path"]] = row
id_ = 0
for path, f in audio_files:
if path in metadata:
result = dict(metadata[path])
# set the audio feature and the path to the extracted file
path = os.path.join(local_extracted_archive,
path) if local_extracted_archive else path
result["audio"] = {"path": path, "bytes": f.read()}
result["path"] = path
yield id_, result
id_ += 1
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