Datasets:
Tasks:
Translation
Size:
1K - 10K
File size: 4,390 Bytes
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# Copyright 2020 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.
"""Simple sentences Dataset - contains 90 mins of speech data"""
import csv
import json
import os
import datasets
_CITATION = """\
@misc{simpledata_1,
title = {Whisper model for tamil-to-eng translation},
publisher = {Achitha},
year = {2022},
}
@misc{simpledata_2,
title = {Fine-tuning whisper model},
publisher = {Achitha},
year = {2022},
}
"""
_DESCRIPTION = """\
The data contains roughly one and half hours of audio and transcripts in Tamil language.
"""
_HOMEPAGE = ""
_LICENSE = "MIT"
_METADATA_URLS = {
"train": "data/train.jsonl",
"test": "data/test.jsonl"
}
_URLS = {
"train": "data/train.tar.gz",
"test": "data/test.tar.gz",
}
class simple_data(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.1.0")
def _info(self):
features = datasets.Features(
{
"audio": datasets.Audio(sampling_rate=16_000),
"path": datasets.Value("string"),
"sentence": datasets.Value("string"),
"length": datasets.Value("float")
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=("sentence", "label"),
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
metadata_paths = dl_manager.download(_METADATA_URLS)
train_archive = dl_manager.download(_URLS["train"])
test_archive = dl_manager.download(_URLS["test"])
local_extracted_train_archive = dl_manager.extract(train_archive) if not dl_manager.is_streaming else None
local_extracted_test_archive = dl_manager.extract(test_archive) if not dl_manager.is_streaming else None
test_archive = dl_manager.download(_URLS["test"])
train_dir = "train"
test_dir = "test"
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"metadata_path": metadata_paths["train"],
"local_extracted_archive": local_extracted_train_archive,
"path_to_clips": train_dir,
"audio_files": dl_manager.iter_archive(train_archive),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"metadata_path": metadata_paths["test"],
"local_extracted_archive": local_extracted_test_archive,
"path_to_clips": test_dir,
"audio_files": dl_manager.iter_archive(test_archive),
},
),
]
def _generate_examples(self, metadata_path, local_extracted_archive, path_to_clips, audio_files):
"""Yields examples as (key, example) tuples."""
examples = {}
with open(metadata_path, encoding="utf-8") as f:
for key, row in enumerate(f):
data = json.loads(row)
examples[data["path"]] = data
inside_clips_dir = False
id_ = 0
for path, f in audio_files:
if path.startswith(path_to_clips):
inside_clips_dir = True
if path in examples:
result = examples[path]
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
elif inside_clips_dir:
break |