| from collections import defaultdict | |
| import os | |
| import json | |
| import csv | |
| import datasets | |
| _NAME="chm150_asr" | |
| _VERSION="1.0.0" | |
| _DESCRIPTION = """ | |
| The CHM150 is a corpus of microphone speech of mexican Spanish taken from 75 male speakers and | |
| 75 female speakers in a noise environment of a "quiet office" with a total duration of 1.63 hours. | |
| """ | |
| _CITATION = """ | |
| @misc{menachm150asr2016, | |
| title={CHM150 CORPUS: Audio and Transcripts in Spanish of 150 speakers from Mexico City.}, | |
| ldc_catalog_no={LDC2016S04}, | |
| DOI={https://doi.org/10.35111/ygn0-wm25}, | |
| author={Hernandez Mena, Carlos Daniel and Herrera Camacho, Jose Abel}, | |
| journal={Linguistic Data Consortium, Philadelphia}, | |
| year={2016}, | |
| url={https://catalog.ldc.upenn.edu/LDC2016S04}, | |
| } | |
| """ | |
| _HOMEPAGE = "https://catalog.ldc.upenn.edu/LDC2016S04" | |
| _LICENSE = "CC-BY-SA-4.0, See http://creativecommons.org/licenses/by-sa/4.0/" | |
| _BASE_DATA_DIR = "corpus/" | |
| _METADATA_TRAIN = os.path.join(_BASE_DATA_DIR,"files", "metadata_train.tsv") | |
| _TARS_TRAIN = os.path.join(_BASE_DATA_DIR,"files", "tars_train.paths") | |
| class CHM150Config(datasets.BuilderConfig): | |
| """BuilderConfig for CHM150 CORPUS""" | |
| def __init__(self, name, **kwargs): | |
| name=_NAME | |
| super().__init__(name=name, **kwargs) | |
| class CHM150(datasets.GeneratorBasedBuilder): | |
| """CHM150 CORPUS""" | |
| VERSION = datasets.Version(_VERSION) | |
| BUILDER_CONFIGS = [ | |
| CHM150Config( | |
| 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"), | |
| "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) | |
| tars_train=dl_manager.download_and_extract(_TARS_TRAIN) | |
| hash_tar_files=defaultdict(dict) | |
| with open(tars_train,'r') as f: | |
| hash_tar_files['train']=[path.replace('\n','') for path in f] | |
| hash_meta_paths={"train":metadata_train} | |
| audio_paths = dl_manager.download(hash_tar_files) | |
| splits=["train"] | |
| 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"], | |
| } | |
| ), | |
| ] | |
| def _generate_examples(self, audio_archives, local_extracted_archives_paths, metadata_paths): | |
| features = ["speaker_id","gender","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()}, | |
| } | |