metadata

pretty_name: MultiLingual LibriSpeech
annotations_creators:
  - expert-generated
language_creators:
  - crowdsourced
  - expert-generated
language:
  - de
  - es
  - fr
  - it
  - nl
  - pl
  - pt
license:
  - cc-by-4.0
multilinguality:
  - multilingual
paperswithcode_id: librispeech-1
size_categories:
  - 100K<n<1M
source_datasets:
  - original
task_categories:
  - automatic-speech-recognition
  - audio-classification
task_ids:
  - speaker-identification
dataset_info:
  - config_name: polish
    features:
      - name: file
        dtype: string
      - name: audio
        dtype:
          audio:
            sampling_rate: 16000
      - name: text
        dtype: string
      - name: speaker_id
        dtype: int64
      - name: chapter_id
        dtype: int64
      - name: id
        dtype: string
    splits:
      - name: train
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        num_examples: 25043
      - name: train.9h
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        num_examples: 2173
      - name: train.1h
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        num_examples: 238
      - name: validation
        num_bytes: 318964
        num_examples: 512
      - name: test
        num_bytes: 332317
        num_examples: 520
    download_size: 6609569551
    dataset_size: 18316354
  - config_name: german
    features:
      - name: file
        dtype: string
      - name: audio
        dtype:
          audio:
            sampling_rate: 16000
      - name: text
        dtype: string
      - name: speaker_id
        dtype: int64
      - name: chapter_id
        dtype: int64
      - name: id
        dtype: string
    splits:
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      - name: train.9h
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        num_examples: 2194
      - name: train.1h
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        num_examples: 241
      - name: validation
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        num_examples: 3469
      - name: test
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        num_examples: 3394
    download_size: 122944886305
    dataset_size: 282852748
  - config_name: dutch
    features:
      - name: file
        dtype: string
      - name: audio
        dtype:
          audio:
            sampling_rate: 16000
      - name: text
        dtype: string
      - name: speaker_id
        dtype: int64
      - name: chapter_id
        dtype: int64
      - name: id
        dtype: string
    splits:
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      - name: train.9h
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      - name: train.1h
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        num_examples: 234
      - name: validation
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      - name: test
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    download_size: 92158429530
    dataset_size: 224001789
  - config_name: french
    features:
      - name: file
        dtype: string
      - name: audio
        dtype:
          audio:
            sampling_rate: 16000
      - name: text
        dtype: string
      - name: speaker_id
        dtype: int64
      - name: chapter_id
        dtype: int64
      - name: id
        dtype: string
    splits:
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      - name: train.1h
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      - name: validation
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      - name: test
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        num_examples: 2426
    download_size: 64474642518
    dataset_size: 166526210
  - config_name: spanish
    features:
      - name: file
        dtype: string
      - name: audio
        dtype:
          audio:
            sampling_rate: 16000
      - name: text
        dtype: string
      - name: speaker_id
        dtype: int64
      - name: chapter_id
        dtype: int64
      - name: id
        dtype: string
    splits:
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      - name: train.9h
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      - name: train.1h
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      - name: validation
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      - name: test
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        num_examples: 2385
    download_size: 53296894035
    dataset_size: 141097756
  - config_name: italian
    features:
      - name: file
        dtype: string
      - name: audio
        dtype:
          audio:
            sampling_rate: 16000
      - name: text
        dtype: string
      - name: speaker_id
        dtype: int64
      - name: chapter_id
        dtype: int64
      - name: id
        dtype: string
    splits:
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      - name: train.9h
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      - name: train.1h
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      - name: validation
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      - name: test
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        num_examples: 1262
    download_size: 15395281399
    dataset_size: 38958224
  - config_name: portuguese
    features:
      - name: file
        dtype: string
      - name: audio
        dtype:
          audio:
            sampling_rate: 16000
      - name: text
        dtype: string
      - name: speaker_id
        dtype: int64
      - name: chapter_id
        dtype: int64
      - name: id
        dtype: string
    splits:
      - name: train
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      - name: train.9h
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      - name: train.1h
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      - name: validation
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        num_examples: 826
      - name: test
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        num_examples: 871
    download_size: 9982803818
    dataset_size: 25548322

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: Paperswithcode Leaderboard

Dataset Summary

Deprecated: This legacy dataset doesn't support streaming and is not updated. Use "facebook/multilingual_librispeech" instead.

Multilingual LibriSpeech (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, audio-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

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.

{'chapter_id': 141231,
 'file': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/dev_clean/1272/141231/1272-141231-0000.flac',
  'audio': {'path': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/dev_clean/1272/141231/1272-141231-0000.flac',
  'array': array([-0.00048828, -0.00018311, -0.00137329, ...,  0.00079346,
          0.00091553,  0.00085449], dtype=float32),
  'sampling_rate': 16000},
 'id': '1272-141231-0000',
 'speaker_id': 1272,
 'text': 'A MAN SAID TO THE UNIVERSE SIR I EXIST'}

Data Fields

file: A path to the downloaded audio file in .flac format.
audio: A dictionary containing the path to the downloaded audio file, 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 for adding this dataset.

Datasets:

legacy-datasets
/

multilingual_librispeech

Dataset Card for MultiLingual LibriSpeech

Table of Contents

Dataset Description

Dataset Summary

Supported Tasks and Leaderboards

Languages

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