Datasets:
carlosdanielhernandezmena
commited on
Commit
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6d1c490
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Parent(s):
db99c2d
Convert dataset to Parquet (#1)
Browse files- Convert dataset to Parquet (47ad3e4bf4f8b594a4aced2a4626913b9bf452b4)
- Delete data file (5e45d1996107372d1362d47dbacbf0942a3ec5c8)
- Delete data file (fbedac378fa6aa28cb9b222dacf05e79f880385e)
- Delete loading script (312c544c04ecf935cca892a5b3865223a35cd2a9)
- Delete data file (81d675ff439dd952a5fbdeb5677aefe56063c227)
- README.md +39 -6
- corpus/files/metadata_train.tsv +0 -0
- corpus/files/tars_train.paths +0 -1
- raddromur_asr.py +0 -122
- corpus/speech/train.tar.gz → raddromur_asr/train-00000-of-00007.parquet +2 -2
- raddromur_asr/train-00001-of-00007.parquet +3 -0
- raddromur_asr/train-00002-of-00007.parquet +3 -0
- raddromur_asr/train-00003-of-00007.parquet +3 -0
- raddromur_asr/train-00004-of-00007.parquet +3 -0
- raddromur_asr/train-00005-of-00007.parquet +3 -0
- raddromur_asr/train-00006-of-00007.parquet +3 -0
README.md
CHANGED
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---
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annotations_creators:
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- machine-generated
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-
language:
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- is
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language_creators:
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- machine-generated
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license:
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- cc-by-4.0
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multilinguality:
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- monolingual
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pretty_name: "Raddr\xF3mur Icelandic Speech 22.09"
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size_categories:
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- 10K<n<100K
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source_datasets:
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- original
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tags:
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- icelandic podcasts
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- spontaneous icelandic
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- forced-aligned
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- ruv.is
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- mafia aligner
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-
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-
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---
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---
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annotations_creators:
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- machine-generated
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language_creators:
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- machine-generated
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+
language:
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- is
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license:
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- cc-by-4.0
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multilinguality:
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- monolingual
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size_categories:
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- 10K<n<100K
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source_datasets:
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- original
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task_categories:
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- automatic-speech-recognition
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task_ids: []
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pretty_name: Raddrómur Icelandic Speech 22.09
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tags:
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- icelandic podcasts
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- spontaneous icelandic
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- forced-aligned
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- ruv.is
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- mafia aligner
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dataset_info:
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config_name: raddromur_asr
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features:
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- name: audio_id
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dtype: string
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- name: audio
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dtype:
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audio:
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sampling_rate: 16000
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- name: podcast_id
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dtype: string
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- name: segment_num
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dtype: int32
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- name: start_time
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dtype: string
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- name: duration
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dtype: float32
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- name: mafia_score
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dtype: float32
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- name: normalized_text
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dtype: string
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splits:
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- name: train
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num_bytes: 3253319536.0
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num_examples: 13030
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download_size: 3238416999
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dataset_size: 3253319536.0
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configs:
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- config_name: raddromur_asr
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data_files:
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- split: train
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path: raddromur_asr/train-*
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default: true
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---
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corpus/files/metadata_train.tsv
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The diff for this file is too large to render.
See raw diff
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corpus/files/tars_train.paths
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corpus/speech/train.tar.gz
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raddromur_asr.py
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from collections import defaultdict
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import os
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import json
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import csv
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-
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import datasets
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_NAME="raddromur_asr"
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_VERSION="1.0.0"
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_AUDIO_EXTENSIONS=".flac"
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_DESCRIPTION = """
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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.
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"""
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_CITATION = """
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@misc{carlosmenaraddromur2022,
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title={Raddrómur Icelandic Speech 22.09},
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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},
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year={2022},
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url={http://hdl.handle.net/20.500.12537/286},
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}
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"""
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_HOMEPAGE = "http://hdl.handle.net/20.500.12537/286"
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_LICENSE = "CC-BY-4.0, See https://creativecommons.org/licenses/by/4.0/"
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-
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_BASE_DATA_DIR = "corpus/"
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_METADATA_TRAIN = os.path.join(_BASE_DATA_DIR,"files","metadata_train.tsv")
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_TARS_TRAIN = os.path.join(_BASE_DATA_DIR,"files","tars_train.paths")
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class RaddromurAsrConfig(datasets.BuilderConfig):
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"""BuilderConfig for Raddrómur Corpus"""
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def __init__(self, name, **kwargs):
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name=_NAME
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super().__init__(name=name, **kwargs)
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class RaddromurAsr(datasets.GeneratorBasedBuilder):
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"""Raddrómur Icelandic Speech 22.09"""
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VERSION = datasets.Version(_VERSION)
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BUILDER_CONFIGS = [
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RaddromurAsrConfig(
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name=_NAME,
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version=datasets.Version(_VERSION),
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)
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]
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def _info(self):
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features = datasets.Features(
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{
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"audio_id": datasets.Value("string"),
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"audio": datasets.Audio(sampling_rate=16000),
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"podcast_id": datasets.Value("string"),
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"segment_num": datasets.Value("int32"),
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"start_time": datasets.Value("string"),
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"duration": datasets.Value("float32"),
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"mafia_score": datasets.Value("float32"),
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"normalized_text": datasets.Value("string"),
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}
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)
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=features,
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homepage=_HOMEPAGE,
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license=_LICENSE,
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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metadata_train=dl_manager.download_and_extract(_METADATA_TRAIN)
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tars_train=dl_manager.download_and_extract(_TARS_TRAIN)
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hash_tar_files=defaultdict(dict)
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with open(tars_train,'r') as f:
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hash_tar_files['train']=[path.replace('\n','') for path in f]
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hash_meta_paths={"train":metadata_train}
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audio_paths = dl_manager.download(hash_tar_files)
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splits=["train"]
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local_extracted_audio_paths = (
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dl_manager.extract(audio_paths) if not dl_manager.is_streaming else
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{
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split:[None] * len(audio_paths[split]) for split in splits
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}
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)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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"audio_archives":[dl_manager.iter_archive(archive) for archive in audio_paths["train"]],
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"local_extracted_archives_paths": local_extracted_audio_paths["train"],
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"metadata_paths": hash_meta_paths["train"],
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}
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),
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]
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def _generate_examples(self, audio_archives, local_extracted_archives_paths, metadata_paths):
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features = ["podcast_id","segment_num","start_time","duration","mafia_score","normalized_text"]
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with open(metadata_paths) as f:
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metadata = {x["audio_id"]: x for x in csv.DictReader(f, delimiter="\t")}
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for audio_archive, local_extracted_archive_path in zip(audio_archives, local_extracted_archives_paths):
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for audio_filename, audio_file in audio_archive:
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#audio_id = audio_filename.split(os.sep)[-1].split(_AUDIO_EXTENSIONS)[0]
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audio_id =os.path.splitext(os.path.basename(audio_filename))[0]
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path = os.path.join(local_extracted_archive_path, audio_filename) if local_extracted_archive_path else audio_filename
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yield audio_id, {
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"audio_id": audio_id,
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**{feature: metadata[audio_id][feature] for feature in features},
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"audio": {"path": path, "bytes": audio_file.read()},
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}
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corpus/speech/train.tar.gz → raddromur_asr/train-00000-of-00007.parquet
RENAMED
@@ -1,3 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:e227184f23db2a53320e2ed673da83ede5b36557a405e3134c19b0cf206689ff
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size 457859891
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raddromur_asr/train-00001-of-00007.parquet
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version https://git-lfs.github.com/spec/v1
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size 462726400
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raddromur_asr/train-00002-of-00007.parquet
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version https://git-lfs.github.com/spec/v1
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size 472400148
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raddromur_asr/train-00003-of-00007.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:ed1f1dd010a992aeed1d6513e6fa99af61ce9fd42d520b51b83fae07133a78f3
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size 462928880
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raddromur_asr/train-00004-of-00007.parquet
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version https://git-lfs.github.com/spec/v1
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size 457570018
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raddromur_asr/train-00005-of-00007.parquet
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+
version https://git-lfs.github.com/spec/v1
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size 466461671
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raddromur_asr/train-00006-of-00007.parquet
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+
version https://git-lfs.github.com/spec/v1
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size 458469991
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