| import os |
|
|
| import datasets |
| import pandas as pd |
| import regex as re |
|
|
|
|
| class ERRNewsConfig(datasets.BuilderConfig): |
| def __init__(self, data_url, features, recordings_url, **kwargs): |
| super().__init__(version=datasets.Version("1.1.0"), **kwargs) |
| self.data_url = data_url |
| self.recordings_url = recordings_url |
| self.features = features |
|
|
|
|
| class ERRNews(datasets.GeneratorBasedBuilder): |
| data_url = "https://cs.taltech.ee/staff/heharm/ERRnews/data.zip" |
| recordings_url = "https://cs.taltech.ee/staff/heharm/ERRnews/recordings.tar" |
| features = ["name", "summary", "transcript", "url", "meta"] |
|
|
| BUILDER_CONFIGS = [ |
| ERRNewsConfig( |
| name="et", |
| data_url=data_url, |
| recordings_url=None, |
| features=features |
| ), |
| ERRNewsConfig( |
| name="audio", |
| data_url=data_url, |
| recordings_url=recordings_url, |
| features=features + ["audio", "recording_id"] |
| ), |
| ERRNewsConfig( |
| name="et_en", |
| data_url=data_url, |
| recordings_url=None, |
| features=features + ["en_summary", "en_transcript"] |
| ), |
| ERRNewsConfig( |
| name="full", |
| data_url=data_url, |
| recordings_url=recordings_url, |
| features=features + ["audio", "recording_id", "en_summary", "en_transcript"] |
| ) |
| ] |
|
|
| DEFAULT_CONFIG_NAME = "et" |
|
|
| def _info(self): |
| description = ( |
| "ERRnews is an estonian language summaryzation dataset of ERR News broadcasts scraped from the ERR " |
| "Archive (https://arhiiv.err.ee/err-audioarhiiv). The dataset consists of news story transcripts " |
| "generated by an ASR pipeline paired with the human written summary from the archive. For leveraging " |
| "larger english models the dataset includes machine translated (https://neurotolge.ee/) transcript and " |
| "summary pairs." |
| ) |
|
|
| citation = """\ |
| @article{henryabstractive, |
| title={Abstractive Summarization of Broadcast News Stories for {Estonian}}, |
| author={Henry, H{\"a}rm and Tanel, Alum{\"a}e}, |
| journal={Baltic J. Modern Computing}, |
| volume={10}, |
| number={3}, |
| pages={511-524}, |
| year={2022} |
| } |
| """ |
|
|
| features = datasets.Features( |
| { |
| "name": datasets.Value("string"), |
| "summary": datasets.Value("string"), |
| "transcript": datasets.Value("string"), |
| "url": datasets.Value("string"), |
| "meta": datasets.Value("string"), |
| }) |
|
|
| if self.config.name == "audio": |
| features["audio"] = datasets.features.Audio(sampling_rate=16_000) |
| features["recording_id"] = datasets.Value("int32") |
|
|
| if self.config.name == "et_en": |
| features["en_summary"] = datasets.Value("string") |
| features["en_transcript"] = datasets.Value("string") |
|
|
| if self.config.name == "full": |
| features["en_summary"] = datasets.Value("string") |
| features["en_transcript"] = datasets.Value("string") |
| features["audio"] = datasets.features.Audio(sampling_rate=16_000) |
| features["recording_id"] = datasets.Value("int32") |
|
|
| return datasets.DatasetInfo( |
| description=description, |
| citation=citation, |
| features=features, |
| supervised_keys=None, |
| version=self.config.version, |
| ) |
|
|
| def _split_generators(self, dl_manager): |
| """Returns SplitGenerators.""" |
| train = "data/train.csv" |
| test = "data/test.csv" |
| val = "data/val.csv" |
|
|
| data_archive = dl_manager.download_and_extract(self.config.data_url) |
|
|
| if self.config.recordings_url: |
| recordings = dl_manager.download(self.config.recordings_url) |
| recordings_archive = dl_manager.extract(recordings) if not dl_manager.is_streaming else None |
| audio_files = dl_manager.iter_archive(recordings) |
| else: |
| audio_files = None |
| recordings_archive = None |
|
|
| return [ |
| datasets.SplitGenerator( |
| name=datasets.Split.TRAIN, |
| gen_kwargs={ |
| "file_path": train, |
| "audio_files": audio_files, |
| "recordings_archive": recordings_archive, |
| "data_archive": data_archive |
| }, |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.VALIDATION, |
| gen_kwargs={ |
| "file_path": val, |
| "audio_files": audio_files, |
| "recordings_archive": recordings_archive, |
| "data_archive": data_archive |
| }, |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.TEST, |
| gen_kwargs={ |
| "file_path": test, |
| "audio_files": audio_files, |
| "recordings_archive": recordings_archive, |
| "data_archive": data_archive |
| }, |
| ), |
| ] |
|
|
| def create_dict(self, data): |
| res = dict() |
| for key in self.config.features: |
| res[key] = data[key] |
| return res |
|
|
| def _generate_examples(self, file_path, audio_files, recordings_archive, data_archive): |
| data = pd.read_csv(os.path.join(data_archive, file_path)) |
|
|
| if audio_files: |
| for path, f in audio_files: |
| id = re.sub("^recordings\/", "", re.sub(".ogv$", "", path)) |
| row = data.loc[data['recording_id'] == int(id)] |
| if len(row) > 0: |
| result = row.to_dict('records')[0] |
| |
| path = os.path.join(recordings_archive, path) if recordings_archive else path |
| result["audio"] = {"path": path, "bytes": f.read()} |
| yield row.index[0].item(), self.create_dict(result) |
| else: |
| for row in data.iterrows(): |
| result = row[1].to_dict() |
| yield row[0], self.create_dict(result) |
|
|
