Datasets:
File size: 3,533 Bytes
db8bd28 099c6e5 db8bd28 099c6e5 db8bd28 099c6e5 db8bd28 099c6e5 db8bd28 099c6e5 db8bd28 099c6e5 db8bd28 099c6e5 db8bd28 | 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 | import json
import os
import datasets
_CITATION = """\
@article{vidal2019epadb,
title={EpaDB: a database for development of pronunciation assessment systems},
author={Vidal, Jazmin and Ferrer, Luciana and Brambilla, Leonardo},
journal={Proc. Interspeech},
pages={589--593},
year={2019}
}
"""
_DESCRIPTION = """\
EPADB contains curated pronunciation assessment data collected from Spanish-speaking learners of English.
"""
class Epadb(datasets.GeneratorBasedBuilder):
"""EPADB dataset."""
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features({
"utt_id": datasets.Value("string"),
"speaker_id": datasets.Value("string"),
"sentence_id": datasets.Value("string"),
"phone_ids": datasets.Sequence(datasets.Value("string")),
"ref_phonemic_1": datasets.Sequence(datasets.Value("string")),
"annot_1": datasets.Sequence(datasets.Value("string")),
"lab_phonemic_1": datasets.Sequence(datasets.Value("string")),
"error_type": datasets.Sequence(datasets.Value("string")),
"start_mfa": datasets.Sequence(datasets.Value("float")),
"end_mfa": datasets.Sequence(datasets.Value("float")),
"global_1": datasets.Value("float"),
"level_1": datasets.Value("string"),
"gender": datasets.Value("string"),
"duration": datasets.Value("float"),
"sample_rate": datasets.Value("int32"),
"audio": datasets.Audio(sampling_rate=16000),
"transcription": datasets.Value("string"),
}),
citation=_CITATION,
)
def _split_generators(self, dl_manager):
# First, download the JSON files
train_path = dl_manager.download("train.json")
test_path = dl_manager.download("test.json")
# Read JSON to get list of all audio files
with open(train_path) as f:
train_data = json.load(f)
with open(test_path) as f:
test_data = json.load(f)
# Collect all unique audio files referenced
train_audio_files = [example["audio"] for example in train_data]
test_audio_files = [example["audio"] for example in test_data]
# Download all audio files
train_audio_paths = dl_manager.download(train_audio_files)
test_audio_paths = dl_manager.download(test_audio_files)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": train_path,
"audio_files": dict(zip(train_audio_files, train_audio_paths))
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": test_path,
"audio_files": dict(zip(test_audio_files, test_audio_paths))
},
),
]
def _generate_examples(self, filepath, audio_files):
with open(filepath, encoding="utf-8") as f:
data = json.load(f)
for idx, example in enumerate(data):
# Replace the audio path with the downloaded local path
audio_path = example["audio"]
example["audio"] = audio_files[audio_path]
yield idx, example |