Mikolaj Pudo
commited on
Commit
·
67ea4e6
1
Parent(s):
13497d8
MOCKS.py refactoring
Browse files
MOCKS.py
CHANGED
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@@ -41,401 +41,164 @@ audio testset for evaluation and benchmarking Open-Vocabulary Keyword Spotting (
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_BASE_URL = "https://huggingface.co/datasets/voiceintelligenceresearch/MOCKS/tree/main"
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"
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"
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"online": "en/LS-clean/test/online/data.tar.gz",
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"offline_transcription" : "en/LS-clean/test/data_offline_transcription.tsv",
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"online_transcription" : "en/LS-clean/test/data_online_transcription.tsv",
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},
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"en.LS-other": {
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"offline": "en/LS-other/test/offline/data.tar.gz",
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"online": "en/LS-other/test/online/data.tar.gz",
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"offline_transcription" : "en/LS-other/test/data_offline_transcription.tsv",
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"online_transcription" : "en/LS-other/test/data_online_transcription.tsv",
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},
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"en.MCV": {
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"offline": "en/MCV/test/offline/data.tar.gz",
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"online": "en/MCV/test/online/data.tar.gz",
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"offline_transcription" : "en/MCV/test/data_offline_transcription.tsv",
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"online_transcription" : "en/MCV/test/data_online_transcription.tsv",
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},
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"es.MCV": {
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"offline": "es/MCV/test/offline/data.tar.gz",
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"online": "es/MCV/test/online/data.tar.gz",
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"offline_transcription" : "es/MCV/test/data_offline_transcription.tsv",
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"online_transcription" : "es/MCV/test/data_online_transcription.tsv",
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},
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"fr.MCV": {
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"offline": "fr/MCV/test/offline/data.tar.gz",
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"online": "fr/MCV/test/online/data.tar.gz",
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"offline_transcription": "fr/MCV/test/data_offline_transcription.tsv",
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"online_transcription": "fr/MCV/test/data_online_transcription.tsv",
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},
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"it.MCV": {
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"offline": "it/MCV/test/offline/data.tar.gz",
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"online": "it/MCV/test/online/data.tar.gz",
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"offline_transcription": "it/MCV/test/data_offline_transcription.tsv",
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"online_transcription": "it/MCV/test/data_online_transcription.tsv",
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},
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"all": {
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"de.MCV.offline": "de/MCV/test/offline/data.tar.gz",
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"de.MCV.online": "de/MCV/test/online/data.tar.gz",
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"en.LS-clean.offline": "en/LS-clean/test/offline/data.tar.gz",
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"en.LS-clean.online": "en/LS-clean/test/online/data.tar.gz",
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"en.LS-other.offline": "en/LS-other/test/offline/data.tar.gz",
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"en.LS-other.online": "en/LS-other/test/online/data.tar.gz",
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"en.MCV.offline": "en/MCV/test/offline/data.tar.gz",
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"en.MCV.online": "en/MCV/test/online/data.tar.gz",
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"es.MCV.offline": "es/MCV/test/offline/data.tar.gz",
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"es.MCV.online": "es/MCV/test/online/data.tar.gz",
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"fr.MCV.offline": "fr/MCV/test/offline/data.tar.gz",
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"fr.MCV.online": "fr/MCV/test/online/data.tar.gz",
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"it.MCV.offline": "it/MCV/test/offline/data.tar.gz",
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"it.MCV.online": "it/MCV/test/online/data.tar.gz",
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"de.MCV.offline_transcription": "de/MCV/test/data_offline_transcription.tsv",
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"de.MCV.online_transcription": "de/MCV/test/data_online_transcription.tsv",
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"en.LS-clean.offline_transcription": "en/LS-clean/test/data_offline_transcription.tsv",
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"en.LS-clean.online_transcription": "en/LS-clean/test/data_online_transcription.tsv",
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"en.LS-other.offline_transcription": "en/LS-other/test/data_offline_transcription.tsv",
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"en.LS-other.online_transcription": "en/LS-other/test/data_online_transcription.tsv",
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"en.MCV.offline_transcription": "en/MCV/test/data_offline_transcription.tsv",
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"en.MCV.online_transcription": "en/MCV/test/data_online_transcription.tsv",
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"es.MCV.offline_transcription": "es/MCV/test/data_offline_transcription.tsv",
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"es.MCV.online_transcription": "es/MCV/test/data_online_transcription.tsv",
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"fr.MCV.offline_transcription": "fr/MCV/test/data_offline_transcription.tsv",
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"fr.MCV.online_transcription": "fr/MCV/test/data_online_transcription.tsv",
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"it.MCV.offline_transcription": "it/MCV/test/data_offline_transcription.tsv",
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"it.MCV.online_transcription": "it/MCV/test/data_online_transcription.tsv",
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}
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}
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class Mocks(datasets.GeneratorBasedBuilder):
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"""Mocks Dataset."""
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DEFAULT_CONFIG_NAME = "
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(name="en.LS-clean", description="English LibriSpeech 'Clean'."),
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datasets.BuilderConfig(name="en.LS-other", description="English LibriSpeech 'Other'."),
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datasets.BuilderConfig(name="en.MCV", description="English Mozilla Common Voice."),
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datasets.BuilderConfig(name="es.MCV", description="Spanish Mozilla Common Voice."),
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datasets.BuilderConfig(name="fr.MCV", description="French Mozilla Common Voice."),
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datasets.BuilderConfig(name="it.MCV", description="Italian Mozilla Common Voice."),
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datasets.BuilderConfig(name="all", description="All test set."),
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]
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features(
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"
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"
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"
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}
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),
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homepage=_BASE_URL,
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citation=_CITATION
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)
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def _split_generators(self, dl_manager):
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elif self.config.name == "es.MCV":
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offline_split = [
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datasets.SplitGenerator(
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name="offline",
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gen_kwargs={
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"audio_files": dl_manager.iter_archive(archive_path["offline"]),
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"transcription": archive_path["offline_transcription"],
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"s_type": "offline"
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}
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)
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]
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online_split = [
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datasets.SplitGenerator(
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name="online",
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gen_kwargs={
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"audio_files": dl_manager.iter_archive(archive_path["online"]),
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"transcription": archive_path["online_transcription"],
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"s_type": "online"
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}
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)
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]
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elif self.config.name == "fr.MCV":
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offline_split = [
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datasets.SplitGenerator(
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name="offline",
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gen_kwargs={
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"audio_files": dl_manager.iter_archive(archive_path["offline"]),
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"transcription": archive_path["offline_transcription"],
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"s_type": "offline"
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}
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)
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]
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online_split = [
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datasets.SplitGenerator(
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name="online",
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gen_kwargs={
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"audio_files": dl_manager.iter_archive(archive_path["online"]),
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"transcription": archive_path["online_transcription"],
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"s_type": "online"
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}
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)
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]
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elif self.config.name == "it.MCV":
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offline_split = [
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datasets.SplitGenerator(
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name="offline",
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gen_kwargs={
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"audio_files": dl_manager.iter_archive(archive_path["offline"]),
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"transcription": archive_path["offline_transcription"],
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"s_type": "offline"
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}
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)
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]
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online_split = [
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datasets.SplitGenerator(
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name="online",
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gen_kwargs={
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"audio_files": dl_manager.iter_archive(archive_path["online"]),
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"transcription": archive_path["online_transcription"],
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"s_type": "online"
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}
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)
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]
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elif self.config.name == "all":
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offline_split = [
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datasets.SplitGenerator(
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name="de.MCV.offline",
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gen_kwargs={
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"audio_files": dl_manager.iter_archive(archive_path["de.MCV.offline"]),
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"transcription": archive_path["de.MCV.offline_transcription"],
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"s_type": "offline"
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}
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),
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datasets.SplitGenerator(
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name="en.LS-clean.offline",
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gen_kwargs={
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"audio_files": dl_manager.iter_archive(archive_path["en.LS-clean.offline"]),
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"transcription": archive_path["en.LS-clean.offline_transcription"],
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"s_type": "offline"
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}
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),
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datasets.SplitGenerator(
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name="en.LS-other.offline",
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gen_kwargs={
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"audio_files": dl_manager.iter_archive(archive_path["en.LS-other.offline"]),
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"transcription": archive_path["en.LS-other.offline_transcription"],
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"s_type": "offline"
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}
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),
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datasets.SplitGenerator(
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name="en.MCV.offline",
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gen_kwargs={
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"audio_files": dl_manager.iter_archive(archive_path["en.MCV.offline"]),
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"transcription": archive_path["en.MCV.offline_transcription"],
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"s_type": "offline"
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}
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),
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datasets.SplitGenerator(
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name="es.MCV.offline",
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gen_kwargs={
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"audio_files": dl_manager.iter_archive(archive_path["es.MCV.offline"]),
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"transcription": archive_path["es.MCV.offline_transcription"],
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"s_type": "offline"
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}
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),
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datasets.SplitGenerator(
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name="fr.MCV.offline",
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gen_kwargs={
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"audio_files": dl_manager.iter_archive(archive_path["fr.MCV.offline"]),
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"transcription": archive_path["fr.MCV.offline_transcription"],
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"s_type": "offline"
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}
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),
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datasets.SplitGenerator(
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name="it.MCV.offline",
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gen_kwargs={
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"audio_files": dl_manager.iter_archive(archive_path["it.MCV.offline"]),
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"transcription": archive_path["it.MCV.offline_transcription"],
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"s_type": "offline"
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}
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)
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]
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online_split = [
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datasets.SplitGenerator(
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name="de.MCV.online",
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gen_kwargs={
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"transcription": archive_path["de.MCV.offline_transconline"],
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"s_type": "online"
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}
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),
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datasets.SplitGenerator(
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name="en.LS-clean.online",
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gen_kwargs={
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"audio_files": dl_manager.iter_archive(archive_path["en.LS-clean.online"]),
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"transcription": archive_path["en.LS-clean.online_transcription"],
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"s_type": "online"
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}
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),
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datasets.SplitGenerator(
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name="en.LS-other.online",
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gen_kwargs={
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"audio_files": dl_manager.iter_archive(archive_path["en.LS-other.online"]),
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"transcription": archive_path["en.LS-other.online_transcription"],
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"s_type": "online"
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}
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),
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datasets.SplitGenerator(
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name="en.MCV.online",
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gen_kwargs={
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"audio_files": dl_manager.iter_archive(archive_path["en.MCV.online"]),
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"transcription": archive_path["en.MCV.online_transcription"],
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"s_type": "online"
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}
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),
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datasets.SplitGenerator(
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name="es.MCV.online",
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gen_kwargs={
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"audio_files": dl_manager.iter_archive(archive_path["es.MCV.online"]),
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"transcription": archive_path["es.MCV.online_transcription"],
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"s_type": "online"
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}
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),
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datasets.SplitGenerator(
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name="fr.MCV.online",
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gen_kwargs={
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"audio_files": dl_manager.iter_archive(archive_path["fr.MCV.online"]),
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"transcription": archive_path["fr.MCV.online_transcription"],
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"s_type": "online"
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}
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),
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datasets.SplitGenerator(
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name="it.MCV.online",
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gen_kwargs={
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"audio_files": dl_manager.iter_archive(archive_path["it.MCV.online"]),
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"transcription": archive_path["it.MCV.online_transcription"],
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"s_type": "online"
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}
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)
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]
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return online_split + offline_split
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def _generate_examples(self, audio_files, transcription, s_type):
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| 426 |
-
"""Lorem ipsum."""
|
| 427 |
-
metadata = {}
|
| 428 |
-
with open(transcription, encoding="utf-8") as f:
|
| 429 |
-
f = csv.reader(f, delimiter="\t")
|
| 430 |
-
for row in f:
|
| 431 |
-
audio_id = row[0].split("/")[-1]
|
| 432 |
-
keyword_transcription = row[1]
|
| 433 |
-
metadata[audio_id] = {"audio_id": audio_id, "transcription": keyword_transcription}
|
| 434 |
|
| 435 |
id_ = 0
|
| 436 |
-
for
|
| 437 |
-
_,
|
| 438 |
-
if
|
| 439 |
-
|
| 440 |
-
|
| 441 |
-
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|
| 41 |
|
| 42 |
|
| 43 |
_BASE_URL = "https://huggingface.co/datasets/voiceintelligenceresearch/MOCKS/tree/main"
|
| 44 |
+
_DL_URLS_TEMPLATE = {
|
| 45 |
+
"data": "%s/%s/test/%s/data.tar.gz",
|
| 46 |
+
"transcription" : "%s/%s/test/data_%s_transcription.tsv",
|
| 47 |
+
"positive" : "%s/%s/test/%s/all.pair.positive.tsv",
|
| 48 |
+
"similar" : "%s/%s/test/%s/all.pair.similar.tsv",
|
| 49 |
+
"different" : "%s/%s/test/%s/all.pair.different.tsv",
|
| 50 |
+
"positive_subset" : "%s/%s/test/%s/subset.pair.positive.tsv",
|
| 51 |
+
"similar_subset" : "%s/%s/test/%s/subset.pair.similar.tsv",
|
| 52 |
+
"different_subset" : "%s/%s/test/%s/subset.pair.different.tsv",
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|
| 53 |
}
|
| 54 |
|
| 55 |
+
_MOCKS_SETS = [
|
| 56 |
+
"en.LS-clean",
|
| 57 |
+
"en.LS-other",
|
| 58 |
+
"en.MCV",
|
| 59 |
+
"de.MCV",
|
| 60 |
+
"es.MCV",
|
| 61 |
+
"fr.MCV",
|
| 62 |
+
"it.MCV"]
|
| 63 |
+
|
| 64 |
+
_MOCKS_SUFFIXES = [
|
| 65 |
+
"",
|
| 66 |
+
".positive",
|
| 67 |
+
".similar",
|
| 68 |
+
".different",
|
| 69 |
+
".subset",
|
| 70 |
+
".positive_subset",
|
| 71 |
+
".similar_subset",
|
| 72 |
+
".different_subset"]
|
| 73 |
+
|
| 74 |
|
| 75 |
class Mocks(datasets.GeneratorBasedBuilder):
|
| 76 |
"""Mocks Dataset."""
|
| 77 |
+
DEFAULT_CONFIG_NAME = "en.LS-clean"
|
| 78 |
+
|
| 79 |
+
BUILDER_CONFIGS = [datasets.BuilderConfig(name=subset+suffix, description=subset+suffix)
|
| 80 |
+
for subset in _MOCKS_SETS for suffix in _MOCKS_SUFFIXES]
|
|
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|
| 81 |
|
| 82 |
def _info(self):
|
| 83 |
return datasets.DatasetInfo(
|
| 84 |
description=_DESCRIPTION,
|
| 85 |
+
features=datasets.Features({
|
| 86 |
+
"keyword_id": datasets.Value("string"),
|
| 87 |
+
"keyword_transcription": datasets.Value("string"),
|
| 88 |
+
"test_id": datasets.Value("string"),
|
| 89 |
+
"test_transcription": datasets.Value("string"),
|
| 90 |
+
"test_audio": datasets.Audio(sampling_rate=16000),
|
| 91 |
+
"label": datasets.Value("bool"),
|
| 92 |
}
|
| 93 |
),
|
| 94 |
homepage=_BASE_URL,
|
| 95 |
citation=_CITATION
|
| 96 |
)
|
| 97 |
|
| 98 |
+
|
| 99 |
def _split_generators(self, dl_manager):
|
| 100 |
+
logger.info("split_generator")
|
| 101 |
+
name_split = self.config.name.split(".")
|
| 102 |
+
subset_lang = name_split[0]
|
| 103 |
+
subset_name = name_split[1]
|
| 104 |
+
|
| 105 |
+
if len(name_split) == 2:
|
| 106 |
+
pairs_types = ["positive", "similar", "different"]
|
| 107 |
+
elif name_split[2] == "subset":
|
| 108 |
+
pairs_types = ["positive_subset", "similar_subset", "different_subset"]
|
| 109 |
+
else:
|
| 110 |
+
pairs_types = [name_split[2]]
|
| 111 |
+
|
| 112 |
+
offline_archive_path = dl_manager.download({
|
| 113 |
+
k: v%(subset_lang, subset_name, "offline")
|
| 114 |
+
for k, v in _DL_URLS_TEMPLATE.items()
|
| 115 |
+
})
|
| 116 |
+
online_archive_path = dl_manager.download({
|
| 117 |
+
k: v%(subset_lang, subset_name, "online")
|
| 118 |
+
for k, v in _DL_URLS_TEMPLATE.items()
|
| 119 |
+
})
|
| 120 |
+
|
| 121 |
+
split_offline = [datasets.SplitGenerator(
|
| 122 |
+
name="offline",
|
| 123 |
+
gen_kwargs={
|
| 124 |
+
"audio_files": dl_manager.iter_archive(offline_archive_path["data"]),
|
| 125 |
+
"transcription_keyword": offline_archive_path["transcription"],
|
| 126 |
+
"transcription_test": offline_archive_path["transcription"],
|
| 127 |
+
"pairs": [offline_archive_path[pair_type] for pair_type in pairs_types],
|
| 128 |
+
}
|
| 129 |
+
)
|
| 130 |
+
]
|
| 131 |
+
|
| 132 |
+
split_online = [datasets.SplitGenerator(
|
| 133 |
+
name="online",
|
| 134 |
+
gen_kwargs={
|
| 135 |
+
"audio_files": dl_manager.iter_archive(online_archive_path["data"]),
|
| 136 |
+
"transcription_keyword": offline_archive_path["transcription"],
|
| 137 |
+
"transcription_test": online_archive_path["transcription"],
|
| 138 |
+
"pairs": [online_archive_path[pair_type] for pair_type in pairs_types],
|
| 139 |
+
}
|
| 140 |
+
)
|
| 141 |
+
]
|
| 142 |
+
|
| 143 |
+
return split_offline + split_online
|
| 144 |
+
|
| 145 |
+
|
| 146 |
+
def _read_transcription(self, transcription_path):
|
| 147 |
+
transcription_metadata = {}
|
| 148 |
+
|
| 149 |
+
with open(transcription_path, encoding="utf-8") as f:
|
| 150 |
+
reader = csv.reader(f, delimiter="\t")
|
| 151 |
+
next(reader, None)
|
| 152 |
+
|
| 153 |
+
for row in reader:
|
| 154 |
+
_, audio_id = os.path.split(row[0])
|
| 155 |
+
transcription = row[1]
|
| 156 |
+
transcription_metadata[audio_id] = {
|
| 157 |
+
"audio_id": audio_id,
|
| 158 |
+
"transcription": transcription}
|
| 159 |
+
|
| 160 |
+
return transcription_metadata
|
| 161 |
+
|
| 162 |
+
|
| 163 |
+
def _generate_examples(self, audio_files, transcription_keyword, transcription_test, pairs):
|
| 164 |
+
transcription_keyword_metadata = self._read_transcription(transcription_keyword)
|
| 165 |
+
|
| 166 |
+
transcription_test_metadata = self._read_transcription(transcription_test)
|
| 167 |
+
|
| 168 |
+
pair_metadata = {}
|
| 169 |
+
for pair in pairs:
|
| 170 |
+
with open(pair, encoding="utf-8") as f:
|
| 171 |
+
reader = csv.reader(f, delimiter="\t")
|
| 172 |
+
next(reader, None)
|
| 173 |
+
|
| 174 |
+
for row in reader:
|
| 175 |
+
_, keyword_id = os.path.split(row[0])
|
| 176 |
+
_, test_id = os.path.split(row[1])
|
| 177 |
+
|
| 178 |
+
if keyword_id not in transcription_keyword_metadata:
|
| 179 |
+
logger.error("No transcription and audio for keyword %s"%(keyword_id))
|
| 180 |
+
continue
|
| 181 |
+
if test_id not in transcription_test_metadata:
|
| 182 |
+
logger.error("No transcription and audio for test case %s"%(test_id))
|
| 183 |
+
continue
|
| 184 |
+
|
| 185 |
+
if test_id not in pair_metadata:
|
| 186 |
+
pair_metadata[test_id] = []
|
| 187 |
+
|
| 188 |
+
pair_metadata[test_id].append([keyword_id, int(row[-1])])
|
|
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|
| 189 |
|
| 190 |
id_ = 0
|
| 191 |
+
for test_path, test_f in audio_files:
|
| 192 |
+
_, test_id = os.path.split(test_path)
|
| 193 |
+
if test_id in pair_metadata:
|
| 194 |
+
test_audio = {"bytes": test_f.read()}
|
| 195 |
+
for keyword_id, label in pair_metadata[test_id]:
|
| 196 |
+
yield id_, {
|
| 197 |
+
"keyword_id": keyword_id,
|
| 198 |
+
"keyword_transcription": transcription_keyword_metadata[keyword_id]["transcription"],
|
| 199 |
+
"test_id": test_id,
|
| 200 |
+
"test_transcription": transcription_test_metadata[test_id]["transcription"],
|
| 201 |
+
"test_audio": test_audio,
|
| 202 |
+
"label": label}
|
| 203 |
+
id_ += 1
|
| 204 |
+
|