metadata

pretty_name: LibriSpeech
annotations_creators:
  - expert-generated
language_creators:
  - crowdsourced
  - expert-generated
language:
  - en
license:
  - cc-by-4.0
multilinguality:
  - monolingual
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: clean
    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.100
        num_bytes: 6619683041
        num_examples: 28539
      - name: train.360
        num_bytes: 23898214592
        num_examples: 104014
      - name: validation
        num_bytes: 359572231
        num_examples: 2703
      - name: test
        num_bytes: 367705423
        num_examples: 2620
    download_size: 30121377654
    dataset_size: 31245175287
  - config_name: other
    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.500
        num_bytes: 31810256902
        num_examples: 148688
      - name: validation
        num_bytes: 337283304
        num_examples: 2864
      - name: test
        num_bytes: 352396474
        num_examples: 2939
    download_size: 31236565377
    dataset_size: 32499936680
  - config_name: all
    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.clean.100
        num_bytes: 6627791685
        num_examples: 28539
      - name: train.clean.360
        num_bytes: 23927767570
        num_examples: 104014
      - name: train.other.500
        num_bytes: 31852502880
        num_examples: 148688
      - name: validation.clean
        num_bytes: 359505691
        num_examples: 2703
      - name: validation.other
        num_bytes: 337213112
        num_examples: 2864
      - name: test.clean
        num_bytes: 368449831
        num_examples: 2620
      - name: test.other
        num_bytes: 353231518
        num_examples: 2939
    download_size: 61357943031
    dataset_size: 63826462287

Dataset Card for librispeech_asr

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

Dataset Description

Homepage: LibriSpeech ASR corpus
Repository: [Needs More Information]
Paper: LibriSpeech: An ASR Corpus Based On Public Domain Audio Books
Leaderboard: The 🤗 Speech Bench
Point of Contact: Daniel Povey

Dataset Summary

LibriSpeech is a corpus of approximately 1000 hours of 16kHz read English speech, prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read audiobooks from the LibriVox project, and has been carefully segmented and aligned.

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 Hugging Face leaderboard which can be found at https://huggingface.co/spaces/huggingface/hf-speech-bench. The leaderboard ranks models uploaded to the Hub based on their WER. An external leaderboard at https://paperswithcode.com/sota/speech-recognition-on-librispeech-test-clean ranks the latest models from research and academia.

Languages

The audio is in English. There are two configurations: clean and other. The speakers in the corpus were ranked according to the WER of the transcripts of a model trained on a different dataset, and were divided roughly in the middle, with the lower-WER speakers designated as "clean" and the higher WER speakers designated as "other".

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

The size of the corpus makes it impractical, or at least inconvenient for some users, to distribute it as a single large archive. Thus the training portion of the corpus is split into three subsets, with approximate size 100, 360 and 500 hours respectively. A simple automatic procedure was used to select the audio in the first two sets to be, on average, of higher recording quality and with accents closer to US English. An acoustic model was trained on WSJ’s si-84 data subset and was used to recognize the audio in the corpus, using a bigram LM estimated on the text of the respective books. We computed the Word Error Rate (WER) of this automatic transcript relative to our reference transcripts obtained from the book texts. The speakers in the corpus were ranked according to the WER of the WSJ model’s transcripts, and were divided roughly in the middle, with the lower-WER speakers designated as "clean" and the higher-WER speakers designated as "other".

For "clean", the data is split into train, validation, and test set. The train set is further split into train.100 and train.360 respectively accounting for 100h and 360h of the training data. For "other", the data is split into train, validation, and test set. The train set contains approximately 500h of recorded speech.

	Train.500	Train.360	Train.100	Valid	Test
clean	-	104014	28539	2703	2620
other	148688	-	-	2864	2939

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

The dataset was initially created by Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur.

Licensing Information

CC BY 4.0

Citation Information

@inproceedings{panayotov2015librispeech,
  title={Librispeech: an ASR corpus based on public domain audio books},
  author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
  booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},
  pages={5206--5210},
  year={2015},
  organization={IEEE}
}

Contributions

Thanks to @patrickvonplaten for adding this dataset.

Datasets:

openslr
/

librispeech_asr

Dataset Card for librispeech_asr

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