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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.
"""Speech Dat dataset"""
import datasets
import json
import os
from pathlib import Path
_DESCRIPTION = """\
Speechdat dataset
"""
_HOMEPAGE = ""
_LICENSE = ""
class SpeechDat(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1000
VERSION = datasets.Version("1.1.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="audio", version=VERSION, description="SpeechDat dataset"),
]
def _info(self):
features = datasets.Features(
{
"path": datasets.Value("string"),
"audio": datasets.Audio(sampling_rate=16_000),
"sentence": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
manual_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
path_to_data = "/".join(["wav"])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data_dir": manual_dir
},
)
]
def _generate_examples(self, data_dir):
"""Yields examples."""
data_fields = list(self._info().features.keys())
def get_single_line(path):
lines = []
with open(path, 'r', encoding="utf-8") as f:
for line in f:
line = line.strip()
lines.append(line)
if len(lines) == 1:
return lines[0]
elif len(lines) == 0:
return None
else:
return " ".join(lines)
data_path = Path(data_dir)
for wav_file in data_path.glob("*.wav"):
text_file = Path(str(wav_file).replace(".wav", ".svo"))
if not text_file.is_file():
continue
text_line = get_single_line(text_file)
if text_line is None or text_line == "":
continue
size = os.path.getsize(wav_file)
if size > 1024:
with open(wav_file, "rb") as wav_data:
yield str(wav_file), {
"path": str(wav_file),
"sentence": text_line,
"audio": {
"path": str(wav_file),
"bytes": wav_data.read()
}
}
def normalize(text):
# remove ~
return text |