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# coding=utf-8
# Copyright 2021 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.
""" VinDataVLSP Dataset"""
import datasets
import pandas as pd
import re
_DATA_URL = "https://dutudn-my.sharepoint.com/:u:/g/personal/122180028_sv1_dut_udn_vn/ESeeV5dFDtVKmnvwJA3jUd4BLLJ7DhpOwsyb8QwpldKHwQ?download=1"
_PROMPTS_URLS = {
"train": "https://drive.google.com/uc?export=download&id=1eOOvCDz0uOBBRzsHK7NALcGA70-XbQrd",
"test": "https://drive.google.com/uc?export=download&id=1r2wy5K0VL7wL_iMdtzMhGEy-_k3M2Gdv",
"validation": "https://drive.google.com/uc?export=download&id=1c0YsA4x1Up9qjDpsj1VKH_86m85cTi79"
}
_DESCRIPTION = """\
"""
_LANGUAGES = {
"vi": {
"Language": "Vietnamese",
"Date": "2021-12-11",
"Size": "11 GB",
"Version": "vi_100h_2021-12-11",
},
}
class VinDataVLSPConfig(datasets.BuilderConfig):
"""BuilderConfig for CommonVoice."""
def __init__(self, name, sub_version, **kwargs):
"""
Args:
data_dir: `string`, the path to the folder containing the files in the
downloaded .tar
citation: `string`, citation for the data set
url: `string`, url for information about the data set
**kwargs: keyword arguments forwarded to super.
"""
self.sub_version = sub_version
self.language = kwargs.pop("language", None)
self.date_of_snapshot = kwargs.pop("date", None)
self.size = kwargs.pop("size", None)
self.validated_hr_total = kwargs.pop("val_hrs", None)
self.total_hr_total = kwargs.pop("total_hrs", None)
self.num_of_voice = kwargs.pop("num_of_voice", None)
description = ""
super(VinDataVLSPConfig, self).__init__(
name=name, version=datasets.Version("0.1.0", ""), description=description, **kwargs
)
class VinDataVLSP(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1000
BUILDER_CONFIGS = [
VinDataVLSPConfig(
name=lang_id,
language=_LANGUAGES[lang_id]["Language"],
sub_version=_LANGUAGES[lang_id]["Version"],
# date=_LANGUAGES[lang_id]["Date"],
# size=_LANGUAGES[lang_id]["Size"],
# val_hrs=_LANGUAGES[lang_id]["Validated_Hr_Total"],
# total_hrs=_LANGUAGES[lang_id]["Overall_Hr_Total"],
# num_of_voice=_LANGUAGES[lang_id]["Number_Of_Voice"],
)
for lang_id in _LANGUAGES.keys()
]
def _info(self):
features = datasets.Features(
{
"file_path": datasets.Value("string"),
"script": datasets.Value("string"),
"audio": datasets.Audio(sampling_rate=16_000),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
tsv_files = dl_manager.download(_PROMPTS_URLS)
archive = dl_manager.download(_DATA_URL)
path_to_clips = "./VinDataVLSP"
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"tsv_files": tsv_files["train"],
"audio_files": dl_manager.iter_archive(archive),
"path_to_clips": path_to_clips,
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"tsv_files": tsv_files["test"],
"audio_files": dl_manager.iter_archive(archive),
"path_to_clips": path_to_clips,
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"tsv_files": tsv_files["validation"],
"audio_files": dl_manager.iter_archive(archive),
"path_to_clips": path_to_clips,
},
),
]
def _generate_examples(self, tsv_files, audio_files, path_to_clips):
"""Yields examples."""
data_fields = list(self._info().features.keys())
# audio is not a header of the csv files
data_fields.remove("audio")
examples = {}
df = pd.read_csv(tsv_files, sep="\t", header=0)
df = df.dropna()
chars_to_ignore_regex = r'[,?.!\-;:"“%\'�]'
for file_path, script in zip(df["file_path"], df["script"]):
# set full path for mp3 audio file
audio_path = path_to_clips + "/" + file_path
# Preprocessing script
if ":" in script:
two_dot_index = script.index(":")
script = script[two_dot_index + 1:]
script = script.replace("\n", " ")
script = re.sub(chars_to_ignore_regex, '', script).lower()
examples[audio_path] = {
"file_path": audio_path,
"script": script,
}
for path, f in audio_files:
if path.startswith(path_to_clips):
if path in examples:
audio = {"path": path, "bytes": f.read()}
yield path, {**examples[path], "audio": audio}
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