File size: 3,692 Bytes
ac91d29 |
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 |
# coding=utf-8
from pathlib import Path
from typing import Dict, List, Tuple
import datasets
import pandas as pd
from seacrowd.utils import schemas
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import Licenses, Tasks
# no bibtex citation
_CITATION = ""
_DATASETNAME = "kheng_info"
_DESCRIPTION = """\
The Kheng.info Speech dataset was derived from recordings of Khmer words on the Khmer dictionary website kheng.info.
The recordings were recorded by a native Khmer speaker.
The recordings are short, generally ranging between 1 to 2 seconds only.
"""
_HOMEPAGE = "https://huggingface.co/datasets/seanghay/khmer_kheng_info_speech"
_LANGUAGES = ["khm"]
_LICENSE = Licenses.UNKNOWN.value
_LOCAL = False
_URLS = {
_DATASETNAME: "https://huggingface.co/datasets/seanghay/khmer_kheng_info_speech/resolve/main/data/train-00000-of-00001-4e7ad082a34164d1.parquet",
}
_SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class KhengInfoDataset(datasets.GeneratorBasedBuilder):
"""This is the Kheng.info Speech dataset, which wasderived from recordings on the Khmer dictionary website kheng.info"""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
BUILDER_CONFIGS = [
SEACrowdConfig(
name=f"{_DATASETNAME}_source",
version=SOURCE_VERSION,
description=f"{_DATASETNAME} source schema",
schema="source",
subset_id=f"{_DATASETNAME}",
),
SEACrowdConfig(
name=f"{_DATASETNAME}_seacrowd_sptext",
version=SEACROWD_VERSION,
description=f"{_DATASETNAME} SEACrowd schema",
schema="seacrowd_sptext",
subset_id=f"{_DATASETNAME}",
),
]
DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features({"word": datasets.Value("string"), "duration_ms": datasets.Value("int64"), "audio": datasets.Audio(sampling_rate=16_000)})
elif self.config.schema == "seacrowd_sptext":
features = schemas.speech_text_features
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
urls = _URLS[_DATASETNAME]
data_dir = dl_manager.download_and_extract(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": data_dir,
},
)
]
def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
df = pd.read_parquet(filepath, engine="pyarrow")
if self.config.schema == "source":
for _id, row in df.iterrows():
yield _id, {"word": row["word"], "duration_ms": row["duration_ms"], "audio": row["audio"]}
elif self.config.schema == "seacrowd_sptext":
for _id, row in df.iterrows():
yield _id, {
"id": _id,
"path": row["audio"],
"audio": row["audio"],
"text": row["word"],
"speaker_id": None,
"metadata": {
"speaker_age": None,
"speaker_gender": None,
},
}
|