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import csv
import datasets
from datasets import BuilderConfig, GeneratorBasedBuilder, DatasetInfo, SplitGenerator, Split
from pathlib import Path
_PROMPTS_PROSODIC_URLS = {
"dev": "prosodic/validation.csv",
"train": "prosodic/train.csv",
}
_PROMPTS_AUDIO_CORPUS_URLS = {
"dev": "audioCorpus/validation.csv",
"train": "audioCorpus/train.csv",
}
_PROMPTS_AUTOMATIC_URLS = {
"train": "automatic/nurc_cm_automatic_all_segments.csv",
}
_PROMPTS_TEST_URLS = {
"test": "test/test.csv",
}
_ARCHIVES_PROSODIC = {
"dev": "prosodic/audios.tar.gz",
"train": "prosodic/audios.tar.gz",
}
_ARCHIVES_AUDIO_CORPUS = {
"dev": "audioCorpus/audios.tar.gz",
"train": "audioCorpus/audios.tar.gz",
}
_ARCHIVES_AUTOMATIC = {
"train": "automatic/nurc_cm_automatic_segmented_audios.zip",
}
_ARCHIVES_TEST = {
"test": "test/test.zip",
}
_PATH_TO_CLIPS = {
"dev": "",
"train": "",
"test": "",
}
class NurcSPConfig(BuilderConfig):
def __init__(self, prompts_type, **kwargs):
super().__init__(**kwargs)
self.prompts_type = prompts_type
class NurcSPDataset(GeneratorBasedBuilder):
BUILDER_CONFIGS = [
NurcSPConfig(name="audioCorpus", description="Audio Corpus audio prompts", prompts_type="audioCorpus"),
NurcSPConfig(name="prosodic", description="Prosodic audio prompts", prompts_type="prosodic"),
NurcSPConfig(name="automatic", description="Automatic audio prompts", prompts_type="automatic"),
NurcSPConfig(name="test", description="Test audio prompts", prompts_type="test"),
]
def _info(self):
if self.config.name == "prosodic":
return DatasetInfo(
features=datasets.Features(
{
"path": datasets.Value("string"),
"name": datasets.Value("string"),
"speaker": datasets.Value("string"),
"start_time": datasets.Value("string"),
"end_time": datasets.Value("string"),
"normalized_text": datasets.Value("string"),
"text": datasets.Value("string"),
"duration": datasets.Value("string"),
"type": datasets.Value("string"),
"year": datasets.Value("string"),
"gender": datasets.Value("string"),
"age_range": datasets.Value("string"),
"total_duration": datasets.Value("string"),
"quality": datasets.Value("string"),
"theme": datasets.Value("string"),
"audio": datasets.Audio(sampling_rate=16_000, mono=True),
}
)
)
elif self.config.name == "audioCorpus":
return DatasetInfo(
features=datasets.Features(
{
"audio_name": datasets.Value("string"),
"file_path": datasets.Value("string"),
"text": datasets.Value("string"),
"start_time": datasets.Value("string"),
"end_time": datasets.Value("string"),
"duration": datasets.Value("string"),
"quality": datasets.Value("string"),
"speech_genre": datasets.Value("string"),
"speech_style": datasets.Value("string"),
"variety": datasets.Value("string"),
"accent": datasets.Value("string"),
"sex": datasets.Value("string"),
"age_range": datasets.Value("string"),
"num_speakers": datasets.Value("string"),
"speaker_id": datasets.Value("string"),
"audio": datasets.Audio(sampling_rate=16_000, mono=True),
}
)
)
elif self.config.name == "automatic":
return DatasetInfo(
features=datasets.Features(
{
"path": datasets.Value("string"),
"name": datasets.Value("string"),
"speaker": datasets.Value("string"),
"start_time": datasets.Value("string"),
"end_time": datasets.Value("string"),
"text": datasets.Value("string"),
"duration": datasets.Value("string"),
"audio": datasets.Audio(sampling_rate=16_000, mono=True),
}
)
)
elif self.config.name == "test":
return DatasetInfo(
features=datasets.Features(
{
"path": datasets.Value("string"),
"name": datasets.Value("string"),
"speaker": datasets.Value("string"),
"start_time": datasets.Value("string"),
"end_time": datasets.Value("string"),
"text": datasets.Value("string"),
"duration": datasets.Value("string"),
"audio": datasets.Audio(sampling_rate=16_000, mono=True),
}
)
)
def _split_generators(self, dl_manager):
if self.config.prompts_type == "prosodic":
prompts_urls = _PROMPTS_PROSODIC_URLS
archive_link = _ARCHIVES_PROSODIC
elif self.config.prompts_type == "audioCorpus":
prompts_urls = _PROMPTS_AUDIO_CORPUS_URLS
archive_link = _ARCHIVES_AUDIO_CORPUS
elif self.config.prompts_type == "automatic":
prompts_urls = _PROMPTS_AUTOMATIC_URLS
archive_link = _ARCHIVES_AUTOMATIC
elif self.config.prompts_type == "test":
prompts_urls = _PROMPTS_TEST_URLS
archive_link = _ARCHIVES_TEST
else:
return
prompts_path = dl_manager.download(prompts_urls)
archive = dl_manager.download(archive_link)
if self.config.prompts_type == "prosodic" or self.config.prompts_type == "audioCorpus":
return [
SplitGenerator(
name=Split.VALIDATION,
gen_kwargs={
"prompts_path": prompts_path["dev"],
"path_to_clips": _PATH_TO_CLIPS["dev"],
"audio_files": dl_manager.iter_archive(archive["dev"]),
"split_name": "validation"
}
),
SplitGenerator(
name=Split.TRAIN,
gen_kwargs={
"prompts_path": prompts_path["train"],
"path_to_clips": _PATH_TO_CLIPS["train"],
"audio_files": dl_manager.iter_archive(archive["train"]),
"split_name": "train"
}
),
]
elif self.config.prompts_type == "automatic":
return [
SplitGenerator(
name=Split.TRAIN,
gen_kwargs={
"prompts_path": prompts_path["train"],
"path_to_clips": _PATH_TO_CLIPS["train"],
"audio_files": dl_manager.iter_archive(archive["train"]),
"split_name": "train"
}
),
]
elif self.config.prompts_type == "test":
return[
SplitGenerator(
name=Split.TEST,
gen_kwargs={
"prompts_path": prompts_path["test"],
"path_to_clips": _PATH_TO_CLIPS["test"],
"audio_files": dl_manager.iter_archive(archive["test"]),
"split_name": "test"
}
),
]
def _generate_examples(self, prompts_path, path_to_clips, audio_files, split_name):
examples = {}
csv_paths = []
with open(prompts_path, "r", encoding="utf-8") as f:
if self.config.prompts_type == "test":
csv_reader = csv.DictReader(f, delimiter=";") # Explicitly set delimiter
else:
csv_reader = csv.DictReader(f)
if self.config.prompts_type == "prosodic":
for row in csv_reader:
file_path = Path(row['path']).as_posix()
examples[file_path] = {
"path": row['path'],
"name": row['name'],
"speaker": row['speaker'],
"start_time": row['start_time'],
"end_time": row['end_time'],
"normalized_text": row['normalized_text'],
"text": row['text'],
"duration": row['duration'],
"type": row['type'],
"year": row['year'],
"gender": row['gender'],
"age_range": row['age_range'],
"total_duration": row['total_duration'],
"quality": row['quality'],
"theme": row['theme'],
}
csv_paths.append(file_path)
elif self.config.prompts_type == "audioCorpus":
for row in csv_reader:
file_path = Path(row['file_path']).as_posix()
examples[file_path] = {
"audio_name": row['audio_name'],
"file_path": row['file_path'],
"text": row['text'],
"start_time": row['start_time'],
"end_time": row['end_time'],
"duration": row['duration'],
"quality": row['quality'],
"speech_genre": row['speech_genre'],
"speech_style": row['speech_style'],
"variety": row['variety'],
"accent": row['accent'],
"sex": row['sex'],
"age_range": row['age_range'],
"num_speakers": row['num_speakers'],
"speaker_id": row['speaker_id'],
}
csv_paths.append(file_path)
elif self.config.prompts_type == "automatic":
for row in csv_reader:
file_path = Path(row['path']).as_posix()
examples[file_path] = {
"path": row['path'],
"name": row['name'],
"speaker": row['speaker'],
"start_time": row['start_time'],
"end_time": row['end_time'],
"text": row['text'],
"duration": row['duration'],
}
csv_paths.append(file_path)
elif self.config.prompts_type == "test":
for row in csv_reader:
file_path = Path(row['path']).as_posix()
examples[file_path] = {
"path": row['path'],
"name": row['name'],
"speaker": row['speaker'],
"start_time": row['start_time'],
"end_time": row['end_time'],
"text": row['text'],
"duration": row['duration'],
}
csv_paths.append(file_path)
id_ = 0
for path, f in audio_files:
path = Path(path).as_posix()
if path.startswith(path_to_clips) and path in examples:
audio = {"path": path, "bytes": f.read()}
yield id_, {**examples[path], "audio": audio}
id_ += 1
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