| from collections import defaultdict |
| import os |
| import json |
| import csv |
|
|
| import datasets |
|
|
| _NAME="samromur_milljon" |
| _VERSION="1.0.0" |
| _AUDIO_EXTENSIONS=".flac" |
|
|
| _DESCRIPTION = """ |
| Samrómur Milljón consists of approximately 1 million of speech recordings (967 hours) collected through the platform samromur.is; the transcripts accompanying these recordings were automatically verified using various ASR systems such as: Wav2Vec, Whisper and NeMo. |
| """ |
|
|
| _CITATION = """ |
| @misc{menasamromurmilljon2023, |
| title={Samrómur Milljón, Audio and Transcriptions}, |
| author={Hernández Mena, Carlos Daniel and Guðnason, Jón}, |
| publisher={Reykjavík University} |
| year={2023}, |
| url={https://huggingface.co/datasets/language-and-voice-lab/samromur_milljon}, |
| } |
| """ |
|
|
| _HOMEPAGE = "https://huggingface.co/datasets/language-and-voice-lab/samromur_milljon" |
|
|
| _LICENSE = "CC-BY-4.0, See https://creativecommons.org/licenses/by/4.0/" |
|
|
| _BASE_DATA_DIR = "corpus/" |
|
|
| _METADATA_FEM_LT_18_YRS = os.path.join(_BASE_DATA_DIR,"files","metadata_fem_lt_18_yrs.tsv") |
| _METADATA_FEM_18TO49_YRS = os.path.join(_BASE_DATA_DIR,"files","metadata_fem_18to49_yrs.tsv") |
| _METADATA_FEM_GT_49_YRS = os.path.join(_BASE_DATA_DIR,"files","metadata_fem_gt_49_yrs.tsv") |
|
|
| _METADATA_MALE_LT_18_YRS = os.path.join(_BASE_DATA_DIR,"files","metadata_male_lt_18_yrs.tsv") |
| _METADATA_MALE_18TO49_YRS = os.path.join(_BASE_DATA_DIR,"files","metadata_male_18to49_yrs.tsv") |
| _METADATA_MALE_GT_49_YRS = os.path.join(_BASE_DATA_DIR,"files","metadata_male_gt_49_yrs.tsv") |
|
|
| _METADATA_OTHER = os.path.join(_BASE_DATA_DIR,"files","metadata_other.tsv") |
|
|
| _TARS_FEM_LT_18_YRS = os.path.join(_BASE_DATA_DIR,"files","tars_fem_lt_18_yrs.paths") |
| _TARS_FEM_18TO49_YRS = os.path.join(_BASE_DATA_DIR,"files","tars_fem_18to49_yrs.paths") |
| _TARS_FEM_GT_49_YRS = os.path.join(_BASE_DATA_DIR,"files","tars_fem_gt_49_yrs.paths") |
|
|
| _TARS_MALE_LT_18_YRS = os.path.join(_BASE_DATA_DIR,"files","tars_male_lt_18_yrs.paths") |
| _TARS_MALE_18TO49_YRS = os.path.join(_BASE_DATA_DIR,"files","tars_male_18to49_yrs.paths") |
| _TARS_MALE_GT_49_YRS = os.path.join(_BASE_DATA_DIR,"files","tars_male_gt_49_yrs.paths") |
|
|
| _TARS_OTHER = os.path.join(_BASE_DATA_DIR,"files","tars_other.paths") |
|
|
| class SamromurMilljonConfig(datasets.BuilderConfig): |
| """BuilderConfig for The Samrómur Milljón""" |
|
|
| def __init__(self, name, **kwargs): |
| name=_NAME |
| super().__init__(name=name, **kwargs) |
|
|
| class SamromurMilljon(datasets.GeneratorBasedBuilder): |
| """Samrómur Milljón""" |
|
|
| VERSION = datasets.Version(_VERSION) |
| BUILDER_CONFIGS = [ |
| SamromurMilljonConfig( |
| 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"), |
| "verified_with": datasets.Value("string"), |
| "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_fem_lt_18_yrs=dl_manager.download_and_extract(_METADATA_FEM_LT_18_YRS) |
| metadata_fem_18to49_yrs=dl_manager.download_and_extract(_METADATA_FEM_18TO49_YRS) |
| metadata_fem_gt_49_yrs=dl_manager.download_and_extract(_METADATA_FEM_GT_49_YRS) |
|
|
| metadata_male_lt_18_yrs=dl_manager.download_and_extract(_METADATA_MALE_LT_18_YRS) |
| metadata_male_18to49_yrs=dl_manager.download_and_extract(_METADATA_MALE_18TO49_YRS) |
| metadata_male_gt_49_yrs=dl_manager.download_and_extract(_METADATA_MALE_GT_49_YRS) |
|
|
| metadata_other=dl_manager.download_and_extract(_METADATA_OTHER) |
| |
| tars_fem_lt_18_yrs=dl_manager.download_and_extract(_TARS_FEM_LT_18_YRS) |
| tars_fem_18to49_yrs=dl_manager.download_and_extract(_TARS_FEM_18TO49_YRS) |
| tars_fem_gt_49_yrs=dl_manager.download_and_extract(_TARS_FEM_GT_49_YRS) |
|
|
| tars_male_lt_18_yrs=dl_manager.download_and_extract(_TARS_MALE_LT_18_YRS) |
| tars_male_18to49_yrs=dl_manager.download_and_extract(_TARS_MALE_18TO49_YRS) |
| tars_male_gt_49_yrs=dl_manager.download_and_extract(_TARS_MALE_GT_49_YRS) |
|
|
| tars_other=dl_manager.download_and_extract(_TARS_OTHER) |
| |
| hash_tar_files=defaultdict(dict) |
| with open(tars_fem_lt_18_yrs,'r') as f: |
| hash_tar_files['fem_lt_18_yrs']=[path.replace('\n','') for path in f] |
| with open(tars_fem_18to49_yrs,'r') as f: |
| hash_tar_files['fem_18to49_yrs']=[path.replace('\n','') for path in f] |
| with open(tars_fem_gt_49_yrs,'r') as f: |
| hash_tar_files['fem_gt_49_yrs']=[path.replace('\n','') for path in f] |
| |
| with open(tars_male_lt_18_yrs,'r') as f: |
| hash_tar_files['male_lt_18_yrs']=[path.replace('\n','') for path in f] |
| with open(tars_male_18to49_yrs,'r') as f: |
| hash_tar_files['male_18to49_yrs']=[path.replace('\n','') for path in f] |
| with open(tars_male_gt_49_yrs,'r') as f: |
| hash_tar_files['male_gt_49_yrs']=[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] |
|
|
| hash_meta_paths={"fem_lt_18_yrs":metadata_fem_lt_18_yrs, |
| "fem_18to49_yrs":metadata_fem_18to49_yrs, |
| "fem_gt_49_yrs":metadata_fem_gt_49_yrs, |
| "male_lt_18_yrs":metadata_male_lt_18_yrs, |
| "male_18to49_yrs":metadata_male_18to49_yrs, |
| "male_gt_49_yrs":metadata_male_gt_49_yrs, |
| "other":metadata_other} |
|
|
| audio_paths = dl_manager.download(hash_tar_files) |
| |
| splits=["fem_lt_18_yrs","fem_18to49_yrs","fem_gt_49_yrs","male_lt_18_yrs","male_18to49_yrs","male_gt_49_yrs","other"] |
| 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="female_lt_18_yrs", |
| gen_kwargs={ |
| "audio_archives":[dl_manager.iter_archive(archive) for archive in audio_paths["fem_lt_18_yrs"]], |
| "local_extracted_archives_paths": local_extracted_audio_paths["fem_lt_18_yrs"], |
| "metadata_paths": hash_meta_paths["fem_lt_18_yrs"], |
| } |
| ), |
| datasets.SplitGenerator( |
| name="female_18to49_yrs", |
| gen_kwargs={ |
| "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["fem_18to49_yrs"]], |
| "local_extracted_archives_paths": local_extracted_audio_paths["fem_18to49_yrs"], |
| "metadata_paths": hash_meta_paths["fem_18to49_yrs"], |
| } |
| ), |
| datasets.SplitGenerator( |
| name="female_gt_49_yrs", |
| gen_kwargs={ |
| "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["fem_gt_49_yrs"]], |
| "local_extracted_archives_paths": local_extracted_audio_paths["fem_gt_49_yrs"], |
| "metadata_paths": hash_meta_paths["fem_gt_49_yrs"], |
| } |
| ), |
| datasets.SplitGenerator( |
| name="male_lt_18_yrs", |
| gen_kwargs={ |
| "audio_archives":[dl_manager.iter_archive(archive) for archive in audio_paths["male_lt_18_yrs"]], |
| "local_extracted_archives_paths": local_extracted_audio_paths["male_lt_18_yrs"], |
| "metadata_paths": hash_meta_paths["male_lt_18_yrs"], |
| } |
| ), |
| datasets.SplitGenerator( |
| name="male_18to49_yrs", |
| gen_kwargs={ |
| "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["male_18to49_yrs"]], |
| "local_extracted_archives_paths": local_extracted_audio_paths["male_18to49_yrs"], |
| "metadata_paths": hash_meta_paths["male_18to49_yrs"], |
| } |
| ), |
| datasets.SplitGenerator( |
| name="male_gt_49_yrs", |
| gen_kwargs={ |
| "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["male_gt_49_yrs"]], |
| "local_extracted_archives_paths": local_extracted_audio_paths["male_gt_49_yrs"], |
| "metadata_paths": hash_meta_paths["male_gt_49_yrs"], |
| } |
| ), |
| 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"], |
| } |
| ), |
| ] |
|
|
| def _generate_examples(self, audio_archives, local_extracted_archives_paths, metadata_paths): |
|
|
| features = ["speaker_id","gender","age","duration","verified_with","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()}, |
| } |
|
|
