File size: 6,242 Bytes
a27a700 9a39598 a27a700 9a39598 a27a700 9a39598 7fc795c a27a700 9a39598 a27a700 9a39598 a27a700 82ceaf5 a27a700 9a39598 a27a700 9a39598 a27a700 9a39598 a27a700 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 |
# coding=utf-8
# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Elite Voice Project"""
import csv
import json
import os
import datasets
_CITATION = """\
@InProceedings{elitevoiceproject:dataset,
title = {Elite Voice Project},
author={Elite35P Server.},
year={2022}
}
"""
_HOMEPAGE = "https://nyahello.jp/"
_LICENSE = "https://hololive.hololivepro.com/guidelines/"
_BASE_URL = "https://huggingface.co/datasets/Elite35P-Server/EliteVoiceProject/resolve/main/"
_AUDIO_URL = _BASE_URL + "audio/{platform}/{split}/{platform}_{split}_{version}.tar"
_TRANSCRIPT_URL = _BASE_URL + "transcript/{platform}/{split}/{platform}_{split}_{version}.tsv"
class EliteVoiceProjectConfig(datasets.BuilderConfig):
"""BuilderConfig for EliteVoiceProject."""
def __init__(self, name, version, **kwargs):
self.language = kwargs.pop("language", None)
self.release_date = kwargs.pop("release_date", None)
description = (
f"Elite Voice Project speech to text dataset in {self.language} released on {self.release_date}. "
)
super(EliteVoiceProjectConfig, self).__init__(
name=name,
version=datasets.Version(version),
description=description,
**kwargs,
)
class EliteVoiceProject(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1000
BUILDER_CONFIGS = [
EliteVoiceProjectConfig(
name=platporm,
version='0.0.1',
language='Japanese',
release_date='2022-12-06',
)
for platporm in ["twitter", "youtube", "youtube"]
]
def _info(self):
description = (
"Elite Voice Project はホロライブ所属VTuberのさくらみこ氏の声をデータセット化することを目的に"
"TwitterのSpace配信等のアーカイブから音声を切り出し、センテンスを当てています。"
"当データセットは、hololive productionの二次創作ガイドラインに沿ってご利用ください。"
)
features = datasets.Features(
{
"audio": datasets.features.Audio(sampling_rate=48_000),
"sentence": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=description,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
version=self.config.version,
)
def _split_generators(self, dl_manager):
platporm = self.config.name
version = self.config.version
audio_urls = {}
#splits = ("twitter", "youtube", "twitch", "test")
splits = {"train", "test"}
for split in splits:
audio_urls[split] = [
_AUDIO_URL.format(platform=platporm, split=split, version=version)
]
archive_paths = dl_manager.download(audio_urls)
local_extracted_archive_paths = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {}
meta_urls = {split: _TRANSCRIPT_URL.format(platform=platporm, split=split, version=version) for split in splits}
meta_paths = dl_manager.download_and_extract(meta_urls)
split_generators = []
split_names = {
"train": datasets.Split.TRAIN,
"test": datasets.Split.TEST,
}
for split in splits:
split_generators.append(
datasets.SplitGenerator(
name=split_names.get(split, split),
gen_kwargs={
"local_extracted_archive_paths": local_extracted_archive_paths.get(split),
"archives": [dl_manager.iter_archive(path) for path in archive_paths.get(split)],
"meta_path": meta_paths[split],
},
),
)
return split_generators
def _generate_examples(self, local_extracted_archive_paths, archives, meta_path):
data_fields = list(self._info().features.keys())
metadata = {}
with open(meta_path, encoding="utf-8") as f:
reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
for row in tqdm(reader, desc="Reading metadata..."):
if not row["path"].endswith(".mp3"):
row["path"] += ".mp3"
# accent -> accents in CV 8.0
if "accents" in row:
row["accent"] = row["accents"]
del row["accents"]
# if data is incomplete, fill with empty values
for field in data_fields:
if field not in row:
row[field] = ""
metadata[row["path"]] = row
for i, audio_archive in enumerate(archives):
for filename, file in audio_archive:
_, filename = os.path.split(filename)
if filename in metadata:
result = dict(metadata[filename])
# set the audio feature and the path to the extracted file
path = os.path.join(local_extracted_archive_paths[i], filename) if local_extracted_archive_paths else filename
result["audio"] = {"path": path, "bytes": file.read()}
# set path to None if the audio file doesn't exist locally (i.e. in streaming mode)
result["path"] = path if local_extracted_archive_paths else filename
yield path, result |