Adding the audio files to the repo

corpus/files/tars_train.paths

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+corpus/speech/train.tar.gz

corpus/speech/train.tar.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ebb1578e2217731d12167ffd60f6c491b41c8c57902d97a4a9c9effe33187bb8
+size 3238082167

raddromur_asr.py

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+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]
+                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()},
+                }