File size: 5,690 Bytes
da80e9c |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 |
import datasets
import os
_DESCRIPTION = "MGB2 speech recognition dataset AR"
_HOMEPAGE = "https://arabicspeech.org/mgb2/"
_LICENSE = "MGB-2 License agreement"
_CITATION = """@misc{https://doi.org/10.48550/arxiv.1609.05625,
doi = {10.48550/ARXIV.1609.05625},
url = {https://arxiv.org/abs/1609.05625},
author = {Ali, Ahmed and Bell, Peter and Glass, James and Messaoui, Yacine and Mubarak, Hamdy and Renals, Steve and Zhang, Yifan},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {The MGB-2 Challenge: Arabic Multi-Dialect Broadcast Media Recognition},
publisher = {arXiv},
year = {2016},
copyright = {arXiv.org perpetual, non-exclusive license}
}
"""
_DATA_ARCHIVE_ROOT = "archives/"
_DATA_URL = {
"test": _DATA_ARCHIVE_ROOT + "mgb2_wav.test.tar.gz",
"dev": _DATA_ARCHIVE_ROOT + "mgb2_wav.dev.tar.gz",
"train": [_DATA_ARCHIVE_ROOT + f"mgb2_wav_{x}.train.tar.gz" for x in range(48)], # we have 48 archives
}
_TEXT_URL = {
"test": _DATA_ARCHIVE_ROOT + "mgb2_txt.test.tar.gz",
"dev": _DATA_ARCHIVE_ROOT + "mgb2_txt.dev.tar.gz",
"train": _DATA_ARCHIVE_ROOT + "mgb2_txt.train.tar.gz",
}
class MGDB2Dataset(datasets.GeneratorBasedBuilder):
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"path": datasets.Value("string"),
"audio": datasets.Audio(sampling_rate=16_000),
"text": datasets.Value("string"),
}
),
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
wav_archive = dl_manager.download(_DATA_URL)
txt_archive = dl_manager.download(_TEXT_URL)
test_dir = "dataset/test"
dev_dir = "dataset/dev"
train_dir = "dataset/train"
if dl_manager.is_streaming:
return [
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"path_to_txt": test_dir + "/txt",
"path_to_wav": test_dir + "/wav",
"wav_files": [dl_manager.iter_archive(wav_archive['test'])],
"txt_files": dl_manager.iter_archive(txt_archive['test']),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"path_to_txt": dev_dir + "/txt",
"path_to_wav": dev_dir + "/wav",
"wav_files": [dl_manager.iter_archive(wav_archive['dev'])],
"txt_files": dl_manager.iter_archive(txt_archive['dev']),
},
),
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"path_to_txt": train_dir + "/txt",
"path_to_wav": train_dir + "/wav",
"wav_files": [dl_manager.iter_archive(a) for a in wav_archive['train']],
"txt_files": dl_manager.iter_archive(txt_archive['train']),
},
),
]
else:
return [
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"path_to_txt": test_dir + "/txt",
"path_to_wav": test_dir + "/wav",
"wav_files": [dl_manager.extract(wav_archive['test'])],
"txt_files": dl_manager.extract(txt_archive['test']),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"path_to_txt": dev_dir + "/txt",
"path_to_wav": dev_dir + "/wav",
"wav_files": [dl_manager.extract(wav_archive['dev'])],
"txt_files": dl_manager.extract(txt_archive['dev']),
},
),
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"path_to_txt": train_dir + "/txt",
"path_to_wav": train_dir + "/wav",
"wav_files": [dl_manager.extract(a) for a in wav_archive['train']],
"txt_files": dl_manager.extract(txt_archive['train']),
},
),
]
def _generate_examples(self, path_to_txt, path_to_wav, wav_files, txt_files):
"""
This assumes that the text directory alphabetically precedes the wav dir
The file names for wav and text seem to match and are unique
We can use them for the dictionary matching them
"""
examples = {}
id_ = 0
# need to prepare the transcript - wave map
for path, f in txt_files:
if path.find(path_to_txt) > -1:
wav_path = os.path.split(path)[1].replace("_utf8", "").replace(".txt", ".wav").strip()
txt = f.read().decode(encoding="utf-8").strip()
examples[wav_path] = {
"text": txt,
"path": wav_path,
}
for wf in wav_files:
for path, f in wf:
if path.find(path_to_wav) > -1:
wav_path = os.path.split(path)[1].strip()
audio = {"path": path, "bytes": f.read()}
yield id_, {**examples[wav_path], "audio": audio}
id_ += 1
|