# coding=utf-8 # Copyright 2022 The PolyAI and HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ EVI is a challenging spoken multilingual dataset with 5,506 dialogues in English, Polish, and French that can be used for benchmarking and developing knowledge-based enrolment, identification, and identification for spoken dialogue systems. """ import csv from datetime import datetime import json import os import warnings import datasets logger = datasets.logging.get_logger(__name__) _CITATION = """\ @inproceedings{Spithourakis2022evi, author = {Georgios P. Spithourakis and Ivan Vuli\'{c} and Micha\l{} Lis and I\~{n}igo Casanueva and Pawe\l{} Budzianowski}, title = {{EVI}: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification}, year = {2022}, note = {Data available at https://github.com/PolyAI-LDN/evi-paper}, url = {https://arxiv.org/abs/2204.13496}, booktitle = {Findings of NAACL (publication pending)} } """ # noqa _ALL_CONFIGS = sorted([ "en-GB", "fr-FR", "pl-PL" ]) _LANGS = sorted(["en", "fr", "pl"]) _DESCRIPTION = """ EVI is a challenging spoken multilingual dataset with 5,506 dialogues in English, Polish, and French that can be used for benchmarking and developing knowledge-based enrolment, identification, and identification for spoken dialogue systems. """ # noqa _LICENSE = "CC-BY-4.0" _HOMEPAGE = "https://github.com/PolyAI-LDN/evi-paper" _BASE_URL = "https://huggingface.co/datasets/PolyAI/evi/resolve/main/data" _TEXTS_URL = { lang: os.path.join(_BASE_URL, f"dialogues.{lang.split('-')[0]}.tsv") for lang in _LANGS } _RECORDS_URL = { lang: os.path.join(_BASE_URL, f"records.{lang.split('-')[0]}.csv") for lang in _LANGS } _BROKEN_URL = { "en": os.path.join(_BASE_URL, "broken_en.txt") } _AUDIO_DATA_URL = "https://poly-public-data.s3.eu-west-2.amazonaws.com/evi-paper/audios.zip" # noqa _VERSION = datasets.Version("0.0.1", "") class EviConfig(datasets.BuilderConfig): """BuilderConfig for EVI""" def __init__( self, name, *args, **kwargs ): super().__init__(name=name, *args, **kwargs) self.languages = _LANGS if name == "all" else [name.split("-")[0]] # all langs if config == "all" class Evi(datasets.GeneratorBasedBuilder): DEFAULT_WRITER_BATCH_SIZE = 512 BUILDER_CONFIGS = [EviConfig(name) for name in _ALL_CONFIGS + ["all"]] def _info(self): features = datasets.Features( { "language": datasets.ClassLabel(names=_LANGS), "audio": datasets.Audio(sampling_rate=8_000), "asr_transcription": datasets.Value("string"), "dialogue_id": datasets.Value("string"), "speaker_id": datasets.Value("string"), "turn_id": datasets.Value("int32"), "target_profile_id": datasets.Value("string"), "asr_nbest": datasets.Sequence(datasets.Value("string")), "path": datasets.Value("string"), "postcode": datasets.Value("string"), "name": datasets.Value("string"), "dob": datasets.Value("date64"), "name_first": datasets.Value("string"), "name_last": datasets.Value("string"), "sex": datasets.ClassLabel(names=["F", "M"]), # TODO: are there other genders or Nones? "email": datasets.Value("string"), } ) return datasets.DatasetInfo( version=_VERSION, description=_DESCRIPTION, license=_LICENSE, citation=_CITATION, features=features, homepage=_HOMEPAGE ) def _split_generators(self, dl_manager): langs = self.config.languages lang2records_urls = { lang: _RECORDS_URL[lang] for lang in langs } lang2text_urls = { lang: _TEXTS_URL[lang] for lang in langs } records_paths = dl_manager.download_and_extract(lang2records_urls) text_paths = dl_manager.download_and_extract(lang2text_urls) audio_data_path = dl_manager.download_and_extract(_AUDIO_DATA_URL) broken_path = dl_manager.download_and_extract(_BROKEN_URL["en"]) if "en" in langs else None return [ datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "audio_data_path": audio_data_path, "text_paths": text_paths, "records_paths": records_paths, "broken_path": broken_path }, ) ] def _generate_examples(self, audio_data_path, text_paths, records_paths, broken_path=None): if broken_path: with open(broken_path, encoding="utf-8") as f: broken_samples = set([line.strip() for line in f]) else: broken_samples = None for lang, text_path in text_paths.items(): records_path = records_paths[lang] records = dict() with open(records_path, encoding="utf-8") as fin: records_reader = csv.DictReader( fin, delimiter=",", skipinitialspace=True ) for row in records_reader: records[row["scenario_id"]] = row records[row["scenario_id"]]["dob"] = datetime.strptime(row["dob"], "%Y-%m-%d") _ = records[row["scenario_id"]].pop("scenario_id") with open(text_path, encoding="utf-8") as fin: texts_reader = csv.DictReader( fin, delimiter="\t", skipinitialspace=True ) for dictrow in texts_reader: dialogue_id = dictrow["dialogue_id"] turn_id = dictrow["turn_num"] file_path = os.path.join( "audios", lang, dialogue_id, f'{turn_id}.wav' ) full_path = os.path.join(audio_data_path, file_path) if broken_samples and file_path in broken_samples: warnings.warn(f"{full_path} is broken, skipping it.") continue if not os.path.isfile(full_path): warnings.warn(f"{full_path} not found, skipping it.") continue target_profile_id = dictrow["scenario_id"] if target_profile_id not in records: warnings.warn( f""" Record with scenario_id {target_profile_id} not found, ignoring this dialogue. Full dialogue info: {dictrow} """ ) continue yield file_path, { "language": lang, "audio": str(full_path), "dialogue_id": dialogue_id, "speaker_id": dictrow["speaker_id"], "turn_id": turn_id, "target_profile_id": target_profile_id, "asr_transcription": dictrow["transcription"], "asr_nbest": json.loads(dictrow["nbest"]), "path": file_path, **records[target_profile_id] }