Speech-MASSIVE

Dataset Description

Speech-MASSIVE is a multilingual Spoken Language Understanding (SLU) dataset comprising the speech counterpart for a portion of the MASSIVE textual corpus. Speech-MASSIVE covers 12 languages (Arabic, German, Spanish, French, Hungarian, Korean, Dutch, Polish, European Portuguese, Russian, Turkish, and Vietnamese) from different families and inherits from MASSIVE the annotations for the intent prediction and slot-filling tasks. MASSIVE utterances' labels span 18 domains, with 60 intents and 55 slots. Full train split is provided for French and German, and for all the 12 languages (including French and German), we provide few-shot train, validation, test splits. Few-shot train (115 examples) covers all 18 domains, 60 intents, and 55 slots (including empty slots).

Our extension is prompted by the scarcity of massively multilingual SLU datasets and the growing need for versatile speech datasets to assess foundation models (LLMs, speech encoders) across diverse languages and tasks. To facilitate speech technology advancements, we release Speech-MASSIVE publicly available with CC-BY-NC-SA-4.0 license.

Speech-MASSIVE is accepted at INTERSPEECH 2024 (Kos, GREECE).

Dataset Summary

• validation: validation split available for all the 12 languages
• test: test split available for all the 12 languages
• train_115: few-shot split available for all the 12 languages (all 115 samples are cross-lingually aligned)
• train: train split available for French (fr-FR) and German (de-DE)

lang	split	# sample	# hrs	total # spk (Male/Female/Unidentified)
ar-SA	validation	2033	2.12	36 (22/14/0)
	test	2974	3.23	37 (15/17/5)
	train_115	115	0.14	8 (4/4/0)
de-DE	validation	2033	2.33	68 (35/32/1)
	test	2974	3.41	82 (36/36/10)
	train	11514	12.61	117 (50/63/4)
	train_115	115	0.15	7 (3/4/0)
es-ES	validation	2033	2.53	109 (51/53/5)
	test	2974	3.61	85 (37/33/15)
	train_115	115	0.13	7 (3/4/0)
fr-FR	validation	2033	2.20	55 (26/26/3)
	test	2974	2.65	75 (31/35/9)
	train	11514	12.42	103 (50/52/1)
	train_115	115	0.12	103 (50/52/1)
hu-HU	validation	2033	2.27	69 (33/33/3)
	test	2974	3.30	55 (25/24/6)
	train_115	115	0.12	8 (3/4/1)
ko-KR	validation	2033	2.12	21 (8/13/0)
	test	2974	2.66	31 (10/18/3)
	train_115	115	0.14	8 (4/4/0)
nl-NL	validation	2033	2.14	37 (17/19/1)
	test	2974	3.30	100 (48/49/3)
	train_115	115	0.12	7 (3/4/0)
pl-PL	validation	2033	2.24	105 (50/52/3)
	test	2974	3.21	151 (73/71/7)
	train_115	115	0.10	7 (3/4/0)
pt-PT	validation	2033	2.20	107 (51/53/3)
	test	2974	3.25	102 (48/50/4)
	train_115	115	0.12	8 (4/4/0)
ru-RU	validation	2033	2.25	40 (7/31/2)
	test	2974	3.44	51 (25/23/3)
	train_115	115	0.12	7 (3/4/0)
tr-TR	validation	2033	2.17	71 (36/34/1)
	test	2974	3.00	42 (17/18/7)
	train_115	115	0.11	6 (3/3/0)
vi-VN	validation	2033	2.10	28 (13/14/1)
	test	2974	3.23	30 (11/14/5)
	train_115	115	0.11	7 (2/4/1)

How to use

How to use

The datasets library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the load_dataset function.

For example, to download the French config, simply specify the corresponding language config name (i.e., "fr-FR" for French):

from datasets import load_dataset

speech_massive_fr_train = load_dataset("FBK-MT/Speech-MASSIVE", "fr-FR", split="train")

In case you don't have enough space in the machine, you can stream dataset by adding a streaming=True argument to the load_dataset function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.

from datasets import load_dataset

speech_massive_de_train = load_dataset("FBK-MT/Speech-MASSIVE", "de-DE", split="train", streaming=True)
list(speech_massive_de_train.take(2))

You can also load all the available languages and splits at once. And then access each split.

from datasets import load_dataset

speech_massive = load_dataset("FBK-MT/Speech-MASSIVE", "all")
multilingual_validation = speech_massive['validation']

Or you can load dataset's all the splits per language to separate languages more easily.

from datasets import load_dataset, interleave_datasets, concatenate_datasets

# creating full train set by interleaving between German and French
speech_massive_de = load_dataset("FBK-MT/Speech-MASSIVE", "de-DE")
speech_massive_fr = load_dataset("FBK-MT/Speech-MASSIVE", "fr-FR")
speech_massive_train_de_fr = interleave_datasets([speech_massive_de['train'], speech_massive_fr['train']])

# creating train_115 few-shot set by concatenating Korean and Russian
speech_massive_ko = load_dataset("FBK-MT/Speech-MASSIVE", "ko-KR")
speech_massive_ru = load_dataset("FBK-MT/Speech-MASSIVE", "ru-RU")
speech_massive_train_115_ko_ru = concatenate_datasets([speech_massive_ko['train_115'], speech_massive_ru['train_115']])

Dataset Structure

Data configs

• all: load all the 12 languages in one single dataset instance
• lang: load only lang in the dataset instance, by specifying one of below languages
  • ar-SA, de-DE, es-ES, fr-FR, hu-HU, ko-KR, nl-NL, pl-PL, pt-PT, ru-RU, tr-TR, vi-VN

Data Splits

• validation: validation(dev) split available for all the 12 languages
• train_115: few-shot (115 samples) split available for all the 12 languages
• train: train split available for French (fr-FR) and German (de-DE)

test split is uploaded as a separate dataset on HF to prevent possible data contamination

• ⚠️ test: available only in the separate HF dataset repository. ⚠️
• https://huggingface.co/datasets/FBK-MT/Speech-MASSIVE-test

Data Instances

{
  // Start of the data collected in Speech-MASSIVE
  'audio': {
    'path': 'train/2b12a21ca64a729ccdabbde76a8f8d56.wav', 
    'array': array([-7.80913979e-...7259e-03]),
    'sampling_rate': 16000},
  'path': '/path/to/wav/file.wav',
  'is_transcript_reported': False,
  'is_validated': True,
  'speaker_id': '60fcc09cb546eee814672f44',
  'speaker_sex': 'Female',
  'speaker_age': '25',
  'speaker_ethnicity_simple': 'White',
  'speaker_country_of_birth': 'France',
  'speaker_country_of_residence': 'Ireland',
  'speaker_nationality': 'France',
  'speaker_first_language': 'French',
  // End of the data collected in Speech-MASSIVE

  // Start of the data extracted from MASSIVE 
  // (https://huggingface.co/datasets/AmazonScience/massive/blob/main/README.md#data-instances)
  'id': '7509',
  'locale': 'fr-FR',
  'partition': 'train',
  'scenario': 2,
  'scenario_str': 'calendar',
  'intent_idx': 32,
  'intent_str': 'calendar_query',
  'utt': 'après les cours de natation quoi d autre sur mon calendrier mardi',
  'annot_utt': 'après les cours de natation quoi d autre sur mon calendrier [date : mardi]',
  'worker_id': '22',
  'slot_method': {'slot': ['date'], 'method': ['translation']},
  'judgments': {
    'worker_id': ['22', '19', '0'], 
    'intent_score': [1, 2, 1],
    'slots_score': [1, 1, 1],
    'grammar_score': [4, 4, 4],
    'spelling_score': [2, 1, 2],
    'language_identification': ['target', 'target', 'target']
    },
  'tokens': ['après', 'les', 'cours', 'de', 'natation', 'quoi', 'd', 'autre', 'sur', 'mon', 'calendrier', 'mardi'], 
  'labels': ['Other', 'Other', 'Other', 'Other', 'Other', 'Other', 'Other', 'Other', 'Other', 'Other', 'Other', 'date'],
  // End of the data extracted from MASSIVE
}

Data Fields

audio.path: Original audio file name

audio.array: Read audio file with the sampling rate of 16,000

audio.sampling_rate: Sampling rate

path: Original audio file full path

is_transcript_reported: Whether the transcript is reported as 'syntatically wrong' by crowd-source worker

is_validated: Whether the recorded audio has been validated to check if the audio matches transcript exactly by crowd-source worker

speaker_id: Unique hash id of the crowd source speaker

speaker_sex: Speaker's sex information provided by the crowd-source platform (Prolific)

• Male
• Female
• Unidentified : Information not available from Prolific

speaker_age: Speaker's age information provided by Prolific

• age value (str)
• Unidentified : Information not available from Prolific

speaker_ethnicity_simple: Speaker's ethnicity information provided by Prolific

• ethnicity value (str)
• Unidentified : Information not available from Prolific

speaker_country_of_birth: Speaker's country of birth information provided by Prolific

• country value (str)
• Unidentified : Information not available from Prolific

speaker_country_of_residence: Speaker's country of residence information provided by Prolific

• country value (str)
• Unidentified : Information not available from Prolific

speaker_nationality: Speaker's nationality information provided by Prolific

• nationality value (str)
• Unidentified : Information not available from Prolific

speaker_first_language: Speaker's first language information provided by Prolific

• language value (str)
• Unidentified : Information not available from Prolific

Limitations

As Speech-MASSIVE is constructed based on the MASSIVE dataset, it inherently retains certain grammatical errors present in the original MASSIVE text. Correcting these errors was outside the scope of our project. However, by providing the is_transcripted_reported attribute in Speech-MASSIVE, we enable users of the dataset to be aware of these errors.

License

All datasets are licensed under the CC-BY-NC-SA-4.0 license.

Citation Information

Speech-MASSIVE is accepted at INTERSPEECH 2024 (Kos, Greece). You can access the Speech-MASSIVE paper on arXiv. Please cite the paper when referencing the Speech-MASSIVE corpus as:

@misc{lee2024speechmassivemultilingualspeechdataset,
      title={Speech-MASSIVE: A Multilingual Speech Dataset for SLU and Beyond}, 
      author={Beomseok Lee and Ioan Calapodescu and Marco Gaido and Matteo Negri and Laurent Besacier},
      year={2024},
      eprint={2408.03900},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2408.03900}, 
}