|
import os |
|
from collections import OrderedDict |
|
from pathlib import Path |
|
import datasets |
|
import os |
|
from .meta import lang2shard_cnt |
|
|
|
|
|
class YodasConfig(datasets.BuilderConfig): |
|
"""BuilderConfig for Yodas.""" |
|
|
|
def __init__(self, lang, version, **kwargs): |
|
self.language = lang |
|
self.base_data_path = f"data/{lang}" |
|
|
|
description = ( |
|
f"Youtube speech to text dataset in {self.language}." |
|
) |
|
super(YodasConfig, self).__init__( |
|
name=lang, |
|
version=datasets.Version(version), |
|
description=description, |
|
**kwargs, |
|
) |
|
|
|
|
|
DEFAULT_CONFIG_NAME = "all" |
|
LANGS = list(lang2shard_cnt.keys()) |
|
VERSION = "1.0.0" |
|
|
|
class Yodas(datasets.GeneratorBasedBuilder): |
|
"""Yodas dataset.""" |
|
|
|
BUILDER_CONFIGS = [ |
|
YodasConfig(lang, version=VERSION) for lang in LANGS |
|
] |
|
|
|
VERSION = datasets.Version("1.0.0") |
|
|
|
def _info(self): |
|
return datasets.DatasetInfo( |
|
description="Yodas", |
|
features=datasets.Features( |
|
OrderedDict( |
|
[ |
|
("id", datasets.Value("string")), |
|
("utt_id", datasets.Value("string")), |
|
("audio", datasets.Audio(sampling_rate=16_000)), |
|
("text", datasets.Value("string")), |
|
] |
|
) |
|
), |
|
supervised_keys=None, |
|
homepage="", |
|
citation="", |
|
) |
|
|
|
def _split_generators(self, dl_manager): |
|
"""Returns SplitGenerators.""" |
|
|
|
|
|
total_cnt = lang2shard_cnt[self.config.name] |
|
|
|
idx_lst = [f"{i:08d}" for i in range(total_cnt)] |
|
audio_tar_files = dl_manager.download([f"{self.config.base_data_path}/audio/{i:08d}.tar.gz" for i in range(total_cnt)]) |
|
text_files = dl_manager.download([f"{self.config.base_data_path}/text/{i:08d}.txt" for i in range(total_cnt)]) |
|
|
|
|
|
if dl_manager.is_streaming: |
|
audio_archives = [dl_manager.iter_archive(audio_tar_file) for audio_tar_file in audio_tar_files] |
|
text_archives = [dl_manager.extract(text_file) for text_file in text_files] |
|
|
|
else: |
|
print("extracting audio ...") |
|
extracted_audio_archives = dl_manager.extract(audio_tar_files) |
|
|
|
audio_archives = [] |
|
text_archives = [] |
|
for idx, audio_tar_file, extracted_dir, text_file in zip(idx_lst, audio_tar_files, extracted_audio_archives, text_files): |
|
audio_archives.append(str(extracted_dir)+'/'+idx) |
|
text_archives.append(text_file) |
|
|
|
|
|
return [ |
|
datasets.SplitGenerator( |
|
name=datasets.Split.TRAIN, |
|
gen_kwargs={ |
|
"is_streaming": dl_manager.is_streaming, |
|
"audio_archives": audio_archives, |
|
'text_archives': text_archives, |
|
}, |
|
), |
|
] |
|
|
|
def _generate_examples(self, is_streaming, audio_archives, text_archives): |
|
"""Yields examples.""" |
|
|
|
id_ = 0 |
|
|
|
if is_streaming: |
|
for tar_file, text_file in zip(audio_archives, text_archives): |
|
|
|
utt2text = {} |
|
|
|
with open(text_file) as f: |
|
for id_, row in enumerate(f): |
|
row = row.strip().split(maxsplit=1) |
|
utt2text[row[0]] = row[1] |
|
|
|
for path, audio_f in tar_file: |
|
|
|
path = Path(path) |
|
utt_id = path.stem |
|
|
|
if utt_id in utt2text: |
|
|
|
result = { |
|
'id': id_, |
|
'utt_id': utt_id, |
|
'audio': {"path": None, "bytes": audio_f.read()}, |
|
'text': utt2text[utt_id] |
|
} |
|
|
|
yield id_, result |
|
id_ += 1 |
|
else: |
|
for extracted_dir, text_file in zip(audio_archives, text_archives): |
|
|
|
utt2text = {} |
|
print(extracted_dir) |
|
|
|
with open(text_file) as f: |
|
for _, row in enumerate(f): |
|
row = row.strip().split(maxsplit=1) |
|
utt2text[row[0]] = row[1] |
|
|
|
for audio_file in list(Path(extracted_dir).glob('*')): |
|
|
|
utt_id = audio_file.stem |
|
if utt_id in utt2text: |
|
|
|
result = { |
|
'id': id_, |
|
'utt_id': utt_id, |
|
'audio': {"path": str(audio_file.absolute()), "bytes": open(audio_file, 'rb').read()}, |
|
'text': utt2text[utt_id] |
|
} |
|
|
|
yield id_, result |
|
id_ += 1 |
|
|