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import datasets
_DATA_URL = "data/kscg_small_20v50_16k.tar.gz"
_PROMPTS_URLS = {
"train": "data/prompts-train.txt.gz",
"test": "data/prompts-test.txt.gz",
}
class KscgSmall25(datasets.GeneratorBasedBuilder):
"""KSCG Small 20v50"""
def _info(self):
features = datasets.Features(
{
'path': datasets.Value('string'),
'audio': datasets.Audio(sampling_rate=16_000),
'gender': datasets.ClassLabel(
num_classes=2,
names=[
'M',
'F',
]
),
'age': datasets.ClassLabel(
num_classes=2,
names=[
'20s',
'50s'
]
)
}
)
return datasets.DatasetInfo(
features=features,
supervised_keys=None,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
prompts_paths = dl_manager.download_and_extract(_PROMPTS_URLS)
archive = dl_manager.download(_DATA_URL)
train_dir = "kscg_small_20v50/train"
test_dir = "kscg_small_20v50/test"
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"prompts_path": prompts_paths["train"],
"path_to_clips": train_dir,
"audio_files": dl_manager.iter_archive(archive)
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"prompts_path": prompts_paths["test"],
"path_to_clips": test_dir,
"audio_files": dl_manager.iter_archive(archive)
},
),
]
def _generate_examples(self, prompts_path, path_to_clips, audio_files):
examples = {}
with open(prompts_path, encoding='utf-8') as f:
for row in f:
data = row.strip().split(",")
audio_path = data[0] + ".wav"
examples[audio_path] = {
'path': audio_path,
# 'phone_transcription': data[1],
'gender': data[1],
'age': data[2]
}
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:
audio = {"path": path, "bytes": f.read()}
yield id_, {**examples[path], "audio": audio}
id_ += 1
elif inside_clips_dir:
break
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