| from collections import defaultdict |
| import os |
| import json |
| import csv |
| csv.field_size_limit(100000000) |
|
|
| import datasets |
|
|
| _NAME="annotated_catalan_common_voice_v17" |
| _VERSION="1.0.0" |
| _AUDIO_EXTENSIONS=".mp3" |
|
|
| _DESCRIPTION = """ |
| This version of the Catalan sentences of the Common Voice corpus v17 |
| includes metadata (gender and accent) for 263 speakers annotated by a team of experts. |
| """ |
|
|
| _CITATION = """ |
| @misc{armentanoannotated2024, |
| title={Annotated Catalan Common Voice v17}, |
| author={Armentano, Carme}, |
| publisher={Barcelona Supercomputing Center} |
| year={2024}, |
| url={https://huggingface.co/datasets/projecte-aina/annotated_catalan_common_voice_v17}, |
| } |
| """ |
|
|
| _HOMEPAGE = "https://huggingface.co/datasets/projecte-aina/annotated_catalan_common_voice_v17" |
|
|
| _LICENSE = "CC-BY-4.0, See https://creativecommons.org/licenses/by/4.0/" |
|
|
| _BASE_DATA_DIR = "corpus/" |
|
|
| _METADATA_DEV = os.path.join(_BASE_DATA_DIR,"files","annotated_dev.tsv") |
| _METADATA_INVALIDATED = os.path.join(_BASE_DATA_DIR,"files","annotated_invalidated.tsv") |
| _METADATA_OTHER = os.path.join(_BASE_DATA_DIR,"files","annotated_other.tsv") |
| _METADATA_TEST = os.path.join(_BASE_DATA_DIR,"files","annotated_test.tsv") |
| _METADATA_TRAIN = os.path.join(_BASE_DATA_DIR,"files","annotated_train.tsv") |
| _METADATA_VALIDATED = os.path.join(_BASE_DATA_DIR,"files","annotated_validated.tsv") |
|
|
| _TARS_DEV = os.path.join(_BASE_DATA_DIR,"files","annotated_dev.paths") |
| _TARS_INVALIDATED = os.path.join(_BASE_DATA_DIR,"files","annotated_invalidated.paths") |
| _TARS_OTHER = os.path.join(_BASE_DATA_DIR,"files","annotated_other.paths") |
| _TARS_TEST = os.path.join(_BASE_DATA_DIR,"files","annotated_test.paths") |
| _TARS_TRAIN = os.path.join(_BASE_DATA_DIR,"files","annotated_train.paths") |
| _TARS_VALIDATED = os.path.join(_BASE_DATA_DIR,"files","annotated_validated.paths") |
|
|
| class AnnotatedCatalanCommonVoicev17Config(datasets.BuilderConfig): |
| """BuilderConfig for The Annotated Catalan Common Voice v17""" |
|
|
| def __init__(self, name, **kwargs): |
| name=_NAME |
| super().__init__(name=name, **kwargs) |
|
|
| class AnnotatedCatalanCommonVoicev17(datasets.GeneratorBasedBuilder): |
| """Annotated Catalan Common Voice v17""" |
|
|
| VERSION = datasets.Version(_VERSION) |
| BUILDER_CONFIGS = [ |
| AnnotatedCatalanCommonVoicev17Config( |
| name=_NAME, |
| version=datasets.Version(_VERSION), |
| ) |
| ] |
|
|
| def _info(self): |
| features = datasets.Features( |
| { |
| "audio": datasets.Audio(sampling_rate=16000), |
| "client_id": datasets.Value("string"), |
| "path": datasets.Value("string"), |
| "sentence_id": datasets.Value("string"), |
| "sentence": datasets.Value("string"), |
| "sentence_domain": datasets.Value("string"), |
| "up_votes": datasets.Value("int32"), |
| "down_votes": datasets.Value("int32"), |
| "age": datasets.Value("string"), |
| "gender": datasets.Value("string"), |
| "accents": datasets.Value("string"), |
| "variant": datasets.Value("string"), |
| "locale": datasets.Value("string"), |
| "segment": datasets.Value("string"), |
| "mean quality": datasets.Value("string"), |
| "stdev quality": datasets.Value("string"), |
| "annotated_accent": datasets.Value("string"), |
| "annotated_accent_agreement": datasets.Value("string"), |
| "annotated_gender": datasets.Value("string"), |
| "annotated_gender_agreement": datasets.Value("string"), |
| "propagated_gender": datasets.Value("string"), |
| "propagated_accents": datasets.Value("string"), |
| "propagated_accents_norm": datasets.Value("string"), |
| "variant_norm": datasets.Value("string"), |
| "assigned_accent": datasets.Value("string"), |
| "assigned_gender": datasets.Value("string"), |
| } |
| ) |
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, |
| features=features, |
| homepage=_HOMEPAGE, |
| license=_LICENSE, |
| citation=_CITATION, |
| ) |
|
|
| def _split_generators(self, dl_manager): |
| |
| metadata_dev=dl_manager.download_and_extract(_METADATA_DEV) |
| metadata_invalidated=dl_manager.download_and_extract(_METADATA_INVALIDATED) |
| metadata_other=dl_manager.download_and_extract(_METADATA_OTHER) |
| metadata_test=dl_manager.download_and_extract(_METADATA_TEST) |
| metadata_train=dl_manager.download_and_extract(_METADATA_TRAIN) |
| metadata_validated=dl_manager.download_and_extract(_METADATA_VALIDATED) |
|
|
| tars_dev=dl_manager.download_and_extract(_TARS_DEV) |
| tars_invalidated=dl_manager.download_and_extract(_TARS_INVALIDATED) |
| tars_other=dl_manager.download_and_extract(_TARS_OTHER) |
| tars_test=dl_manager.download_and_extract(_TARS_TEST) |
| tars_train=dl_manager.download_and_extract(_TARS_TRAIN) |
| tars_validated=dl_manager.download_and_extract(_TARS_VALIDATED) |
|
|
| hash_tar_files=defaultdict(dict) |
| |
| with open(tars_dev,'r') as f: |
| hash_tar_files['validation']=[path.replace('\n','') for path in f] |
| with open(tars_invalidated,'r') as f: |
| hash_tar_files['invalidated']=[path.replace('\n','') for path in f] |
| with open(tars_other,'r') as f: |
| hash_tar_files['other']=[path.replace('\n','') for path in f] |
| with open(tars_test,'r') as f: |
| hash_tar_files['test']=[path.replace('\n','') for path in f] |
| with open(tars_train,'r') as f: |
| hash_tar_files['train']=[path.replace('\n','') for path in f] |
| with open(tars_validated,'r') as f: |
| hash_tar_files['validated']=[path.replace('\n','') for path in f] |
| |
| hash_meta_paths={"validation":metadata_dev, |
| "invalidated":metadata_invalidated, |
| "other":metadata_other, |
| "test":metadata_test, |
| "train":metadata_train, |
| "validated":metadata_validated} |
| |
| audio_paths = dl_manager.download(hash_tar_files) |
| |
| splits=["validation","invalidated","other","test","train","validated"] |
| local_extracted_audio_paths = ( |
| dl_manager.extract(audio_paths) if not dl_manager.is_streaming else |
| { |
| split:[None] * len(audio_paths[split]) for split in splits |
| } |
| ) |
| |
| return [ |
| datasets.SplitGenerator( |
| name="validation", |
| gen_kwargs={ |
| "audio_archives":[dl_manager.iter_archive(archive) for archive in audio_paths["validation"]], |
| "local_extracted_archives_paths": local_extracted_audio_paths["validation"], |
| "metadata_paths": hash_meta_paths["validation"], |
| } |
| ), |
| datasets.SplitGenerator( |
| name="invalidated", |
| gen_kwargs={ |
| "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["invalidated"]], |
| "local_extracted_archives_paths": local_extracted_audio_paths["invalidated"], |
| "metadata_paths": hash_meta_paths["invalidated"], |
| } |
| ), |
| datasets.SplitGenerator( |
| name="other", |
| gen_kwargs={ |
| "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["other"]], |
| "local_extracted_archives_paths": local_extracted_audio_paths["other"], |
| "metadata_paths": hash_meta_paths["other"], |
| } |
| ), |
| datasets.SplitGenerator( |
| name="test", |
| gen_kwargs={ |
| "audio_archives":[dl_manager.iter_archive(archive) for archive in audio_paths["test"]], |
| "local_extracted_archives_paths": local_extracted_audio_paths["test"], |
| "metadata_paths": hash_meta_paths["test"], |
| } |
| ), |
| datasets.SplitGenerator( |
| name="train", |
| gen_kwargs={ |
| "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["train"]], |
| "local_extracted_archives_paths": local_extracted_audio_paths["train"], |
| "metadata_paths": hash_meta_paths["train"], |
| } |
| ), |
| datasets.SplitGenerator( |
| name="validated", |
| gen_kwargs={ |
| "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["validated"]], |
| "local_extracted_archives_paths": local_extracted_audio_paths["validated"], |
| "metadata_paths": hash_meta_paths["validated"], |
| } |
| ), |
| ] |
|
|
| def _generate_examples(self, audio_archives, local_extracted_archives_paths, metadata_paths): |
| |
| features = ["client_id","path","sentence_id","sentence","sentence_domain","up_votes", |
| "down_votes","age","gender","accents","variant","locale","segment", |
| "mean quality","stdev quality","annotated_accent","annotated_accent_agreement", |
| "annotated_gender","annotated_gender_agreement","propagated_gender", |
| "propagated_accents","propagated_accents_norm","variant_norm","assigned_accent", |
| "assigned_gender"] |
|
|
| with open(metadata_paths) as f: |
| metadata = {x["path"]: x for x in csv.DictReader(f, delimiter="\t")} |
|
|
| for audio_archive, local_extracted_archive_path in zip(audio_archives, local_extracted_archives_paths): |
| for audio_filename, audio_file in audio_archive: |
| audio_id =os.path.splitext(os.path.basename(audio_filename))[0] |
| audio_id=audio_id+_AUDIO_EXTENSIONS |
| path = os.path.join(local_extracted_archive_path, audio_filename) if local_extracted_archive_path else audio_filename |
| |
| try: |
| yield audio_id, { |
| "path": audio_id, |
| **{feature: metadata[audio_id][feature] for feature in features}, |
| "audio": {"path": path, "bytes": audio_file.read()}, |
| } |
| except: |
| continue |
|
|
|
|
