| from collections import defaultdict |
| import os |
| import json |
| import csv |
|
|
| import datasets |
|
|
| _NAME="malromur_asr" |
| _VERSION="1.0.0" |
| _AUDIO_EXTENSIONS=".flac" |
|
|
| _DESCRIPTION = """ |
| The Málrómur corpus is an open source corpus of Icelandic voice samples. |
| """ |
|
|
| _CITATION = """ |
| @inproceedings{steingrimsson2017malromur, |
| title={Málrómur: A manually verified corpus of recorded Icelandic speech}, |
| author={Steingrímsson, Steinþór and Guðnason, Jón and Helgadóttir, Sigrún and Rögnvaldsson, Eiríkur}, |
| booktitle={Proceedings of the 21st Nordic Conference on Computational Linguistics}, |
| pages={237--240}, |
| year={2017} |
| } |
| """ |
|
|
| _HOMEPAGE = "https://clarin.is/en/resources/malromur/" |
|
|
| _LICENSE = "CC-BY-4.0, See https://creativecommons.org/licenses/by/4.0/" |
|
|
| _BASE_DATA_DIR = "corpus/" |
| _METADATA_TRAIN = os.path.join(_BASE_DATA_DIR,"files","metadata_train.tsv") |
| _METADATA_TEST = os.path.join(_BASE_DATA_DIR,"files", "metadata_test.tsv") |
| _METADATA_DEV = os.path.join(_BASE_DATA_DIR,"files", "metadata_dev.tsv") |
|
|
| _TARS_TRAIN = os.path.join(_BASE_DATA_DIR,"files","tars_train.paths") |
| _TARS_TEST = os.path.join(_BASE_DATA_DIR,"files", "tars_test.paths") |
| _TARS_DEV = os.path.join(_BASE_DATA_DIR,"files", "tars_dev.paths") |
|
|
| class MalromurAsrConfig(datasets.BuilderConfig): |
| """BuilderConfig for The Málrómur Corpus""" |
|
|
| def __init__(self, name, **kwargs): |
| name=_NAME |
| super().__init__(name=name, **kwargs) |
|
|
| class MalromurAsr(datasets.GeneratorBasedBuilder): |
| """The Málrómur Corpus""" |
|
|
| VERSION = datasets.Version(_VERSION) |
| BUILDER_CONFIGS = [ |
| MalromurAsrConfig( |
| name=_NAME, |
| version=datasets.Version(_VERSION), |
| ) |
| ] |
|
|
| def _info(self): |
| features = datasets.Features( |
| { |
| "audio_id": datasets.Value("string"), |
| "audio": datasets.Audio(sampling_rate=16000), |
| "speaker_id": datasets.Value("string"), |
| "gender": datasets.Value("string"), |
| "age": datasets.Value("string"), |
| "duration": datasets.Value("float32"), |
| "normalized_text": datasets.Value("string"), |
| } |
| ) |
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, |
| features=features, |
| homepage=_HOMEPAGE, |
| license=_LICENSE, |
| citation=_CITATION, |
| ) |
|
|
| def _split_generators(self, dl_manager): |
| |
| metadata_train=dl_manager.download_and_extract(_METADATA_TRAIN) |
| metadata_test=dl_manager.download_and_extract(_METADATA_TEST) |
| metadata_dev=dl_manager.download_and_extract(_METADATA_DEV) |
| |
| tars_train=dl_manager.download_and_extract(_TARS_TRAIN) |
| tars_test=dl_manager.download_and_extract(_TARS_TEST) |
| tars_dev=dl_manager.download_and_extract(_TARS_DEV) |
| |
| hash_tar_files=defaultdict(dict) |
| with open(tars_train,'r') as f: |
| hash_tar_files['train']=[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_dev,'r') as f: |
| hash_tar_files['dev']=[path.replace('\n','') for path in f] |
| |
| hash_meta_paths={"train":metadata_train,"test":metadata_test,"dev":metadata_dev} |
| audio_paths = dl_manager.download(hash_tar_files) |
| |
| splits=["train","dev","test"] |
| 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=datasets.Split.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=datasets.Split.VALIDATION, |
| gen_kwargs={ |
| "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["dev"]], |
| "local_extracted_archives_paths": local_extracted_audio_paths["dev"], |
| "metadata_paths": hash_meta_paths["dev"], |
| } |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.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"], |
| } |
| ), |
| ] |
|
|
| def _generate_examples(self, audio_archives, local_extracted_archives_paths, metadata_paths): |
|
|
| features = ["speaker_id","gender","age","duration","normalized_text"] |
| |
| with open(metadata_paths) as f: |
| metadata = {x["audio_id"]: 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] |
| path = os.path.join(local_extracted_archive_path, audio_filename) if local_extracted_archive_path else audio_filename |
| |
| yield audio_id, { |
| "audio_id": audio_id, |
| **{feature: metadata[audio_id][feature] for feature in features}, |
| "audio": {"path": path, "bytes": audio_file.read()}, |
| } |
|
|
