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import datasets
import os
_DESCRIPTION = "MGB2 speech recognition dataset AR"
_HOMEPAGE = "https://arabicspeech.org/mgb2/"
_LICENSE = "MGB-2 License agreement"
_DATA_ARCHIVE_ROOT = "https://huggingface.co/datasets/taqwa92/mg.trial4/tree/main/audio/ar/test"
_DATA_ARCHIVE_ROOT1 ="https://huggingface.co/datasets/taqwa92/mg.trial4/tree/main/sentence/ar/test"
_DATA_URL = {
"test": _DATA_ARCHIVE_ROOT + "test.zip",
}
_TEXT_URL = {
"test": _DATA_ARCHIVE_ROOT1 + "text.tsv",
class MGDB2Dataset(datasets.GeneratorBasedBuilder):
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"audio": datasets.Audio(sampling_rate=16_000),
"sentence": datasets.Value("string"),
}
),
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
)
def _split_generators(self, dl_manager):
wav_archive = dl_manager.download(_DATA_URL)
txt_archive = dl_manager.download(_TEXT_URL)
test_dir = "audio/ar/test"
print("Starting write datasets.........................................................")
if dl_manager.is_streaming:
print("from 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(wav_archive['train']),
"txt_files": dl_manager.iter_archive(txt_archive['train']),
},
),
]
else:
print("from non streaming.........................................................")
test_txt_files=dl_manager.extract(txt_archive['test']);
print("txt file list .....................................",txt_archive['test'])
print("txt file names .....................................",test_txt_files)
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": test_txt_files,
},
),
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(wav_archive['train']),
"txt_files": dl_manager.extract(txt_archive['train']),
},
),
]
print("end of generation.........................................................")
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
"""
print("start of generate examples.........................................................")
print("txt file names............................",txt_files)
print("wav_files names....................................",wav_files)
examples = {}
id_ = 0
# need to prepare the transcript - wave map
for item in txt_files:
print("copying txt file...............",item)
if type(item) is tuple:
# iter_archive will return path and file
path, f = item
txt = f.read().decode(encoding="utf-8").strip()
else:
# extract will return path only
path = item
with open(path, encoding="utf-8") as f:
txt = f.read().strip()
if path.find(path_to_txt) > -1:
# construct the wav path
# which is used as an identifier
wav_path = os.path.split(path)[1].replace("_utf8", "").replace(".txt", ".wav").strip()
examples[wav_path] = {
"text": txt,
"path": wav_path,
}
for wf in wav_files:
for item in wf:
if type(item) is tuple:
path, f = item
wav_data = f.read()
else:
path = item
with open(path, "rb") as f:
wav_data = f.read()
if path.find(path_to_wav) > -1:
wav_path = os.path.split(path)[1].strip()
audio = {"path": path, "bytes": wav_data}
yield id_, {**examples[wav_path], "audio": audio}
id_ += 1 |