| from typing import List |
| import os |
| import csv |
| import ast |
| import gzip |
| import json |
|
|
| import datasets |
| from datasets.utils.logging import get_logger |
|
|
| logger = get_logger(__name__) |
|
|
| _URL = "https://asappresearch.github.io/slue-toolkit/" |
|
|
| _DL_URLS = { |
| "slue-hvb": "data/slue-hvb_blind.zip", |
| "slue-sqa5": "data/slue-sqa5_blind.zip", |
| "slue-vp_nel": "data/slue-vp_nel_blind.zip", |
| } |
|
|
| _LICENSE = """ |
| ======================================================= |
| The license of this script |
| MIT License |
| Copyright (c) 2023 ASAPP Inc. |
| Permission is hereby granted, free of charge, to any person obtaining a copy |
| of this software and associated documentation files (the "Software"), to deal |
| in the Software without restriction, including without limitation the rights |
| to use, copy, modify, merge, publish, distribute, sublicense, and/or sell |
| copies of the Software, and to permit persons to whom the Software is |
| furnished to do so, subject to the following conditions: |
| The above copyright notice and this permission notice shall be included in all |
| copies or substantial portions of the Software. |
| THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| SOFTWARE. |
| ======================================================= |
| SLUE-HVB dataset contains a subset of the Gridspace-Stanford Harper Valley speech dataset and the copyright of this subset remains the same with the original license, CC-BY-4.0. See also original license notice (https://github.com/cricketclub/gridspace-stanford-harper-valley/blob/master/LICENSE) |
| |
| Additionally, we provide dialog act classification annotation and it is covered with the same license as CC-BY-4.0. |
| ======================================================= |
| SLUE-SQA-5 Dataset |
| |
| SLUE-SQA-5 Dataset contains question texts and answer strings (question_text, normalized_question_text, and answer_spans column in .tsv files) from these datasets, |
| * SQuAD1.1 (for questions whose question_id starts with ‘squad-’) |
| * Natural Questions (for questions whose question_id starts with ‘nq-’) |
| * WebQuestions (for questions whose question_id starts with ‘wq-’) |
| * CuratedTREC (for questions whose question_id starts with ‘trec-’) |
| * TriviaQA (for questions whose question_id starts with ‘triviaqa-’) |
| Additionally, we provide audio recordings (.wav files in “question” directories) of these questions. |
| |
| For questions from TriviaQA (questions whose question_id starts with ‘triviaqa-’), their question texts, answer strings, and audio recordings are licensed with the same Apache License 2.0 as TriviaQA (for more detail, please refer to https://github.com/mandarjoshi90/triviaqa/blob/master/LICENSE). |
| For questions from the other 4 datasets, their question texts, answer strings, and audio recordings are licensed with Creative Commons Attribution-ShareAlike 4.0 International license. |
| |
| SLUE-SQA-5 also contains a subset of Spoken Wikipedia, including the audios placed in “document” directories and their transcripts (document_text and normalized_document_text column in .tsv files). Additionally, we provide the text-to-speech alignments (.txt files in “word2time” directories).These contents are licensed with the same Creative Commons (CC BY-SA 4.0) license as Spoken Wikipedia. |
| ======================================================= |
| SLUE-VP-NEL Dataset |
| |
| SLUE-VP-NEL includes word-level time stamps for dev and test splits of the SLUE-voxpopuli corpus. |
| For the dev split, the dataset also contains named entity annotations and corresponding time-stamps in a tsv format. |
| ======================================================= |
| |
| """ |
|
|
| _CITATION = """\ |
| @inproceedings{shon2023slue_phase2, |
| title={SLUE Phase-2: A Benchmark Suite of Diverse Spoken Language Understanding Tasks}, |
| author={Shon, Suwon and Arora, Siddhant and Lin, Chyi-Jiunn and Pasad, Ankita and Wu, Felix and Sharma, Roshan and Wu, Wei-Lun and Lee, Hung-Yi and Livescu, Karen and Watanabe, Shinji}, |
| booktitle={ACL}, |
| year={2023}, |
| } |
| """ |
|
|
| _DESCRIPTION = """\ |
| Spoken Language Understanding Evaluation (SLUE) benchmark Phase 2. |
| """ |
|
|
| def parse_qa_answer_spans(answer_spans): |
| answer_spans = ast.literal_eval(answer_spans) |
| return [{"answer": a, "start_second": s, "end_second": e} for a, s, e in answer_spans] |
|
|
| def load_word2time(word2time_file): |
| word2time = [] |
| with open(word2time_file, "r") as f: |
| for line in f.readlines(): |
| entity = line.strip().split('\t') |
| if len(entity)==1: |
| word = entity[0] |
| normalized_word, start_sec, end_sec = "", -1.0, -1.0 |
| else: |
| word, normalized_word, start_sec, end_sec = entity |
| start_sec, end_sec = float(start_sec), float(end_sec) |
| word2time.append( |
| { |
| "word": word, |
| "normalized_word": normalized_word, |
| "start_second": start_sec, |
| "end_second": end_sec, |
| } |
| ) |
| return word2time |
|
|
| def parse_nel_time_spans(nel_timestamps): |
| nel_timestamps = ast.literal_eval(nel_timestamps) |
| return [ |
| { |
| "ne_label": ne, |
| "start_char_idx": start, |
| "char_offset": off, |
| "start_sec": t0, |
| "end_sec": t1, |
| } |
| for ne, start, off, t0, t1 in nel_timestamps |
| ] |
|
|
| def read_word_timestamps(word_alignments_fn): |
| data = json.loads(open(word_alignments_fn).read()) |
| return [ |
| {"word": word, "start_sec": start, "end_sec": end} |
| for word, start, end in data["timestamps"] |
| ] |
|
|
| class SLUE2Config(datasets.BuilderConfig): |
| """BuilderConfig for SLUE.""" |
|
|
| def __init__(self, **kwargs): |
| """ |
| Args: |
| data_dir: `string`, the path to the folder containing the files in the |
| downloaded .tar |
| citation: `string`, citation for the data set |
| url: `string`, url for information about the data set |
| **kwargs: keyword arguments forwarded to super. |
| """ |
| super(SLUE2Config, self).__init__( |
| version=datasets.Version("2.4.0", ""), **kwargs |
| ) |
|
|
|
|
| class SLUE2(datasets.GeneratorBasedBuilder): |
| """Librispeech dataset.""" |
|
|
| DEFAULT_WRITER_BATCH_SIZE = 256 |
| DEFAULT_CONFIG_NAME = "hvb" |
| BUILDER_CONFIGS = [ |
| SLUE2Config( |
| name="hvb", |
| description="SLUE-HVB set.", |
| ), |
| SLUE2Config( |
| name="sqa5", |
| description="SLUE-SQA-5 set which includes Spoken Question Answering task.", |
| ), |
| SLUE2Config( |
| name="vp_nel", |
| description="SLUE-VP-NEL set with named entity labels and time-stamps.", |
| ), |
| ] |
|
|
| def _info(self): |
| if self.config.name == "hvb": |
| features = { |
| "issue_id": datasets.Value("string"), |
| "audio": datasets.Audio(sampling_rate=16_000), |
| "speaker_id": datasets.Value("string"), |
| "text": datasets.Value("string"), |
| "utt_index": datasets.Value("int32"), |
| "channel": datasets.Value("int32"), |
| "role": datasets.Value("string"), |
| "start_ms": datasets.Value("int32"), |
| "duration_ms": datasets.Value("int32"), |
| "intent": datasets.Value("string"), |
| "dialog_acts": datasets.Sequence( |
| datasets.Value("string"), |
| ), |
| } |
| elif self.config.name == "sqa5": |
| features = { |
| "question_id": datasets.Value("string"), |
| "question_audio": datasets.Audio(sampling_rate=16_000), |
| "question_speaker_id": datasets.Value("string"), |
| "raw_question_text": datasets.Value("string"), |
| "normalized_question_text": datasets.Value("string"), |
| "document_id": datasets.Value("string"), |
| "document_audio": datasets.Audio(sampling_rate=16_000), |
| "document_speaker_id": datasets.Value("string"), |
| "raw_document_text": datasets.Value("string"), |
| "normalized_document_text": datasets.Value("string"), |
| "word2time": datasets.Sequence( |
| { |
| "word": datasets.Value("string"), |
| "normalized_word": datasets.Value("string"), |
| "start_second": datasets.Value("float64"), |
| "end_second": datasets.Value("float64"), |
| } |
| ), |
| "answer_spans": datasets.Sequence( |
| { |
| "answer": datasets.Value("string"), |
| "start_second": datasets.Value("float64"), |
| "end_second": datasets.Value("float64"), |
| } |
| ), |
| } |
| elif self.config.name == "vp_nel": |
| features = { |
| "id": datasets.Value("string"), |
| "split": datasets.Value("string"), |
| "audio": datasets.Audio(sampling_rate=16_000), |
| "speaker_id": datasets.Value("string"), |
| "normalized_text": datasets.Value("string"), |
| "word_timestamps": datasets.Sequence( |
| { |
| "word": datasets.Value("string"), |
| "start_sec": datasets.Value("float64"), |
| "end_sec": datasets.Value("float64"), |
| } |
| ), |
| "normalized_nel": datasets.Sequence( |
| { |
| "ne_label": datasets.Value("string"), |
| "start_char_idx": datasets.Value("int32"), |
| "char_offset": datasets.Value("int32"), |
| "start_sec": datasets.Value("float64"), |
| "end_sec": datasets.Value("float64"), |
| } |
| ), |
| } |
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, |
| features=datasets.Features(features), |
| supervised_keys=("file", "text"), |
| homepage=_URL, |
| citation=_CITATION, |
| license=_LICENSE, |
| ) |
|
|
| def _split_generators( |
| self, dl_manager: datasets.DownloadManager |
| ) -> List[datasets.SplitGenerator]: |
|
|
| config_name = f"slue-{self.config.name}" |
|
|
| dl_dir = dl_manager.download_and_extract(_DL_URLS[config_name]) |
| data_dir = os.path.join(dl_dir, config_name) |
| print(data_dir) |
|
|
| splits = [] |
| if self.config.name in ["hvb", "sqa5"]: |
| splits.append( |
| datasets.SplitGenerator( |
| name=datasets.Split.TRAIN, |
| gen_kwargs={ |
| "filepath": os.path.join( |
| data_dir or "", f"{config_name}_fine-tune.tsv" |
| ), |
| "data_dir": data_dir, |
| }, |
| ) |
| ) |
| if self.config.name in ["hvb", "sqa5", "vp_nel"]: |
| splits.append( |
| datasets.SplitGenerator( |
| name=datasets.Split.VALIDATION, |
| gen_kwargs={ |
| "filepath": os.path.join( |
| data_dir or "", f"{config_name}_dev.tsv" |
| ), |
| "data_dir": data_dir, |
| }, |
| ), |
| ) |
| splits.append( |
| datasets.SplitGenerator( |
| name=datasets.Split.TEST, |
| gen_kwargs={ |
| "filepath": os.path.join( |
| data_dir or "", f"{config_name}_test_blind.tsv" |
| ), |
| "data_dir": data_dir, |
| }, |
| ), |
| ) |
| if self.config.name == "sqa5": |
| splits.append( |
| datasets.SplitGenerator( |
| name="verified_test", |
| gen_kwargs={ |
| "filepath": os.path.join( |
| data_dir or "", f"{config_name}_verified-test_blind.tsv" |
| ), |
| "data_dir": data_dir, |
| }, |
| ) |
| ) |
| return splits |
|
|
| def _generate_examples(self, filepath, data_dir): |
| logger.info(f"generating examples from = {filepath}") |
|
|
| with open(filepath) as f: |
| if self.config.name == "sqa5": |
| reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE) |
| else: |
| reader = csv.DictReader(f, delimiter="\t") |
|
|
| for idx, row in enumerate(reader): |
| if self.config.name == "hvb": |
| split = "test" if "test" in filepath else "dev" if "dev" in filepath else "fine-tune" |
| audio_file = os.path.join( |
| data_dir, split, |
| f'{row["issue_id"]}_{row["start_ms"]}_{int(row["start_ms"]) + int(row["duration_ms"])}.wav' |
| ) |
| example = { |
| "issue_id": row["issue_id"], |
| "audio": audio_file, |
| "speaker_id": row["speaker_id"], |
| "text": row["text"], |
| "utt_index": int(row["utt_index"]), |
| "channel": int(row["channel"]), |
| "role": row["role"], |
| "start_ms": int(row["start_ms"]), |
| "duration_ms": int(row["duration_ms"]), |
| "intent": row["intent"], |
| "dialog_acts": eval(row.get("dialog_acts", "[]")), |
| } |
| elif self.config.name == "sqa5": |
| question_audio_file = os.path.join( |
| data_dir, row["split"], "question", row["question_id"] + ".wav" |
| ) |
| document_audio_file = os.path.join( |
| data_dir, row["split"], "document", row["document_id"] + ".wav" |
| ) |
| word2time_file = os.path.join( |
| data_dir, row["split"], "word2time", row["document_id"] + ".txt" |
| ) |
| example = { |
| "question_id": row["question_id"], |
| "question_audio": question_audio_file, |
| "question_speaker_id": row["question_speaker_id"], |
| "raw_question_text": row["question_text"], |
| "normalized_question_text": row["normalized_question_text"], |
| "document_id": row["document_id"], |
| "document_audio": document_audio_file, |
| "document_speaker_id": row["document_speaker_id"], |
| "raw_document_text": row["document_text"], |
| "normalized_document_text": row["normalized_document_text"], |
| "word2time": load_word2time(word2time_file), |
| "answer_spans": parse_qa_answer_spans(row.get("answer_spans", "[]")), |
| } |
| elif self.config.name == "slue_nel": |
| split = "test" if "test" in filepath else "dev" |
| utt_id = row["id"] |
| word_alignments_fn = os.path.join( |
| data_dir, "word_timestamps", split, f"{utt_id}.json" |
| ) |
| audio_file = os.path.join( |
| data_dir, |
| split, |
| f"{utt_id}.ogg", |
| ) |
| example = { |
| "id": utt_id, |
| "audio": audio_file, |
| "speaker_id": row["speaker_id"], |
| "text": row["normalized_text"], |
| "ne_timestamps": parse_nel_time_spans( |
| row.get("normalized_nel", "[]") |
| ), |
| "word_timestamps": read_word_timestamps(word_alignments_fn), |
| } |
| yield idx, example |
|
|
