metadata

annotations_creators:
  - expert-generated
language_creators:
  - crowdsourced
  - expert-generated
language:
  - de
  - nl
  - fr
  - it
  - es
  - pt
  - pl
license:
  - cc-by-4.0
multilinguality:
  - multilingual
size_categories:
  - 100K<n<1M
source_datasets:
  - original
task_categories:
  - automatic-speech-recognition
paperswithcode_id: multilingual-librispeech
pretty_name: MultiLingual LibriSpeech
dataset_info:
  - config_name: italian
    features:
      - name: audio
        dtype: audio
      - name: original_path
        dtype: string
      - name: begin_time
        dtype: float64
      - name: end_time
        dtype: float64
      - name: transcript
        dtype: string
      - name: audio_duration
        dtype: float64
      - name: speaker_id
        dtype: string
      - name: chapter_id
        dtype: string
      - name: file
        dtype: string
      - name: id
        dtype: string
    splits:
      - name: dev
        num_bytes: 81607596.048
        num_examples: 1248
      - name: test
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        num_examples: 1262
      - name: train
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        num_examples: 59623
      - name: 9_hours
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        num_examples: 2173
      - name: 1_hours
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        num_examples: 240
    download_size: 4200633596
    dataset_size: 4218799275.522
  - config_name: polish
    features:
      - name: audio
        dtype: audio
      - name: original_path
        dtype: string
      - name: begin_time
        dtype: float64
      - name: end_time
        dtype: float64
      - name: transcript
        dtype: string
      - name: audio_duration
        dtype: float64
      - name: speaker_id
        dtype: string
      - name: chapter_id
        dtype: string
      - name: file
        dtype: string
      - name: id
        dtype: string
    splits:
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        num_examples: 512
      - name: test
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        num_examples: 520
      - name: train
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        num_examples: 25043
      - name: 9_hours
        num_bytes: 142005461
        num_examples: 2173
      - name: 1_hours
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        num_examples: 238
    download_size: 1855342312
    dataset_size: 1863058292
  - config_name: portuguese
    features:
      - name: audio
        dtype: audio
      - name: original_path
        dtype: string
      - name: begin_time
        dtype: float64
      - name: end_time
        dtype: float64
      - name: transcript
        dtype: string
      - name: audio_duration
        dtype: float64
      - name: speaker_id
        dtype: string
      - name: chapter_id
        dtype: string
      - name: file
        dtype: string
      - name: id
        dtype: string
    splits:
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        num_examples: 826
      - name: test
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      - name: train
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      - name: 9_hours
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        num_examples: 2116
      - name: 1_hours
        num_bytes: 15697139
        num_examples: 236
    download_size: 2780836500
    dataset_size: 2792568207.366
  - config_name: spanish
    features:
      - name: audio
        dtype: audio
      - name: original_path
        dtype: string
      - name: begin_time
        dtype: float64
      - name: end_time
        dtype: float64
      - name: transcript
        dtype: string
      - name: audio_duration
        dtype: float64
      - name: speaker_id
        dtype: string
      - name: chapter_id
        dtype: string
      - name: file
        dtype: string
      - name: id
        dtype: string
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      - name: test
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      - name: train
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      - name: 9_hours
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        num_examples: 2110
      - name: 1_hours
        num_bytes: 15702048
        num_examples: 233
    download_size: 14971394533
    dataset_size: 15037091662.944
configs:
  - config_name: italian
    data_files:
      - split: dev
        path: italian/dev-*
      - split: test
        path: italian/test-*
      - split: train
        path: italian/train-*
      - split: 9_hours
        path: italian/9_hours-*
      - split: 1_hours
        path: italian/1_hours-*
  - config_name: polish
    data_files:
      - split: dev
        path: polish/dev-*
      - split: test
        path: polish/test-*
      - split: train
        path: polish/train-*
      - split: 9_hours
        path: polish/9_hours-*
      - split: 1_hours
        path: polish/1_hours-*
  - config_name: portuguese
    data_files:
      - split: dev
        path: portuguese/dev-*
      - split: test
        path: portuguese/test-*
      - split: train
        path: portuguese/train-*
      - split: 9_hours
        path: portuguese/9_hours-*
      - split: 1_hours
        path: portuguese/1_hours-*
  - config_name: spanish
    data_files:
      - split: dev
        path: spanish/dev-*
      - split: test
        path: spanish/test-*
      - split: train
        path: spanish/train-*
      - split: 9_hours
        path: spanish/9_hours-*
      - split: 1_hours
        path: spanish/1_hours-*

Dataset Card for MultiLingual LibriSpeech

Dataset Description
Dataset Structure
Dataset Creation
Considerations for Using the Data
Additional Information

Dataset Description

Homepage: MultiLingual LibriSpeech ASR corpus
Repository: [Needs More Information]
Paper: MLS: A Large-Scale Multilingual Dataset for Speech Research
Leaderboard: 🤗 Autoevaluate Leaderboard

Dataset Summary

This is a streamable version of the Multilingual LibriSpeech (MLS) dataset. The data archives were restructured from the original ones from OpenSLR to make it easier to stream.

MLS dataset is a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish.

Supported Tasks and Leaderboards

automatic-speech-recognition, speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active leaderboard which can be found at https://paperswithcode.com/dataset/multilingual-librispeech and ranks models based on their WER.

Languages

The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish

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 German config, simply specify the corresponding language config name (i.e., "german" for German):

from datasets import load_dataset

mls = load_dataset("facebook/multilingual_librispeech", "german", split="train")

Using the datasets library, you can also stream the dataset on-the-fly 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

mls = load_dataset("facebook/multilingual_librispeech", "german", split="train", streaming=True)

print(next(iter(mls)))

Bonus: create a PyTorch dataloader directly with your own datasets (local/streamed).

Local:

from datasets import load_dataset
from torch.utils.data.sampler import BatchSampler, RandomSampler

mls = load_dataset("facebook/multilingual_librispeech", "german", split="train")
batch_sampler = BatchSampler(RandomSampler(mls), batch_size=32, drop_last=False)
dataloader = DataLoader(mls, batch_sampler=batch_sampler)

Streaming:

from datasets import load_dataset
from torch.utils.data import DataLoader

mls = load_dataset("facebook/multilingual_librispeech", "german", split="train", streaming=True)
dataloader = DataLoader(mls, batch_size=32)

To find out more about loading and preparing audio datasets, head over to hf.co/blog/audio-datasets.

Example scripts

Train your own CTC or Seq2Seq Automatic Speech Recognition models on MultiLingual Librispeech with transformers - here.

Dataset Structure

Data Instances

A typical data point comprises the path to the audio file, usually called file and its transcription, called text. Some additional information about the speaker and the passage which contains the transcription is provided.

{'file': '10900_6473_000030.flac',
 'audio': {'path': '10900_6473_000030.flac',
  'array': array([-1.52587891e-04,  6.10351562e-05,  0.00000000e+00, ...,
          4.27246094e-04,  5.49316406e-04,  4.57763672e-04]),
  'sampling_rate': 16000},
 'text': 'więc czego chcecie odemnie spytałem wysłuchawszy tego zadziwiającego opowiadania broń nas stary człowieku broń zakrzyknęli równocześnie obaj posłowie\n',
 'speaker_id': 10900,
 'chapter_id': 6473,
 'id': '10900_6473_000030'}

Data Fields

file: A filename .flac format.
audio: A dictionary containing the audio filename, the decoded audio array, and the sampling rate. Note that when accessing the audio column: dataset[0]["audio"] the audio file is automatically decoded and resampled to dataset.features["audio"].sampling_rate. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the "audio" column, i.e. dataset[0]["audio"] should always be preferred over dataset["audio"][0].
text: the transcription of the audio file.
id: unique id of the data sample.
speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples.
chapter_id: id of the audiobook chapter which includes the transcription.

Data Splits

	Train	Train.9h	Train.1h	Dev	Test
german	469942	2194	241	3469	3394
dutch	374287	2153	234	3095	3075
french	258213	2167	241	2416	2426
spanish	220701	2110	233	2408	2385
italian	59623	2173	240	1248	1262
portuguese	37533	2116	236	826	871
polish	25043	2173	238	512	520

Dataset Creation

Curation Rationale

[Needs More Information]

Source Data

Initial Data Collection and Normalization

[Needs More Information]

Who are the source language producers?

[Needs More Information]

Annotations

Annotation process

[Needs More Information]

Who are the annotators?

[Needs More Information]

Personal and Sensitive Information

The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[Needs More Information]

Additional Information

Dataset Curators

[Needs More Information]

Licensing Information

Public Domain, Creative Commons Attribution 4.0 International Public License (CC-BY-4.0)

Citation Information

@article{Pratap2020MLSAL,
  title={MLS: A Large-Scale Multilingual Dataset for Speech Research},
  author={Vineel Pratap and Qiantong Xu and Anuroop Sriram and Gabriel Synnaeve and Ronan Collobert},
  journal={ArXiv},
  year={2020},
  volume={abs/2012.03411}
}

Contributions

Thanks to @patrickvonplaten and @polinaeterna for adding this dataset.

Datasets:

facebook
/

multilingual_librispeech

Dataset Card for MultiLingual LibriSpeech

Table of Contents

Dataset Description

Dataset Summary

Supported Tasks and Leaderboards

Languages

How to use

Example scripts

Dataset Structure

Data Instances

Data Fields

Data Splits

Dataset Creation

Curation Rationale

Source Data

Initial Data Collection and Normalization

Who are the source language producers?

Annotations

Annotation process

Who are the annotators?

Personal and Sensitive Information

Considerations for Using the Data

Social Impact of Dataset

Discussion of Biases

Other Known Limitations

Additional Information

Dataset Curators

Licensing Information

Citation Information

Contributions