Delete loading script

raddromur_asr.py

@@ -1,122 +0,0 @@
-from collections import defaultdict
-import os
-import json
-import csv
-
-import datasets
-
-_NAME="raddromur_asr"
-_VERSION="1.0.0"
-_AUDIO_EXTENSIONS=".flac"
-
-_DESCRIPTION = """
-The Raddrómur Corpus is intended for the speech recognition field and it is made out of radio podcasts mostly taken from RÚV (ruv.is). Such podcasts were selected because they contained a text script that matches with certain fidelity what is said during the show. After automatic segmentation of the episodes, the transcriptions were inferred using the scripts along with a forced alignment technique.
-"""
-
-_CITATION = """
-@misc{carlosmenaraddromur2022,
-      title={Raddrómur Icelandic Speech 22.09}, 
-      author={Hernández Mena, Carlos Daniel and Hedström, Staffan and Þórhallsdóttir, Ragnheiður and Fong, Judy Y. and Gunnarsson, Þorsteinn Daði and Sigurðardóttir, Helga Svala and Þorsteinsdóttir, Helga Lára and Guðnason, Jón},
-      year={2022},
-      url={http://hdl.handle.net/20.500.12537/286},
-}
-"""
-
-_HOMEPAGE = "http://hdl.handle.net/20.500.12537/286"
-
-_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")
-
-_TARS_TRAIN = os.path.join(_BASE_DATA_DIR,"files","tars_train.paths")
-
-class RaddromurAsrConfig(datasets.BuilderConfig):
-    """BuilderConfig for Raddrómur Corpus"""
-
-    def __init__(self, name, **kwargs):
-        name=_NAME
-        super().__init__(name=name, **kwargs)
-
-class RaddromurAsr(datasets.GeneratorBasedBuilder):
-    """Raddrómur Icelandic Speech 22.09"""
-
-    VERSION = datasets.Version(_VERSION)
-    BUILDER_CONFIGS = [
-        RaddromurAsrConfig(
-            name=_NAME,
-            version=datasets.Version(_VERSION),
-        )
-    ]
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "audio_id": datasets.Value("string"),
-                "audio": datasets.Audio(sampling_rate=16000),
-                "podcast_id": datasets.Value("string"),
-                "segment_num": datasets.Value("int32"),
-                "start_time": datasets.Value("string"),
-                "duration": datasets.Value("float32"),
-                "mafia_score": 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 = ["podcast_id","segment_num","start_time","duration","mafia_score","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 = audio_filename.split(os.sep)[-1].split(_AUDIO_EXTENSIONS)[0]
-                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()},
-                }