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lhoestq HF staff
mls_without_script (#15)
2e83e61 verified
---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- de
- nl
- fr
- it
- es
- pt
- pl
- en
license:
- cc-by-4.0
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- automatic-speech-recognition
- text-to-speech
- text-to-audio
paperswithcode_id: multilingual-librispeech
pretty_name: MultiLingual LibriSpeech
dataset_info:
- config_name: dutch
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: 3095
- name: test
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num_examples: 3075
- name: train
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num_examples: 374287
- name: 9_hours
num_bytes: 139884664.668
num_examples: 2153
- name: 1_hours
num_bytes: 15462181
num_examples: 234
download_size: 24376256629
dataset_size: 24486284437.668
- config_name: french
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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num_examples: 2167
- name: 1_hours
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num_examples: 241
download_size: 17381581776
dataset_size: 17459684482.927002
- config_name: german
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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num_examples: 2194
- name: 1_hours
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num_examples: 241
download_size: 31526161821
dataset_size: 31659423725.516
- config_name: italian
features:
- name: audio
dtype: audio
- name: original_path
dtype: string
- name: begin_time
dtype: float64
- name: end_time
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- name: transcript
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- name: audio_duration
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- name: speaker_id
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- 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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- 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
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dtype: string
- name: id
dtype: string
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num_examples: 826
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num_examples: 37533
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num_examples: 2116
- name: 1_hours
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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
splits:
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num_examples: 2408
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num_examples: 2385
- name: train
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num_examples: 2110
- name: 1_hours
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num_examples: 233
download_size: 14971394533
dataset_size: 15037091662.944
configs:
- config_name: dutch
data_files:
- split: dev
path: dutch/dev-*
- split: test
path: dutch/test-*
- split: train
path: dutch/train-*
- split: 9_hours
path: dutch/9_hours-*
- split: 1_hours
path: dutch/1_hours-*
- config_name: french
data_files:
- split: dev
path: french/dev-*
- split: test
path: french/test-*
- split: train
path: french/train-*
- split: 9_hours
path: french/9_hours-*
- split: 1_hours
path: french/1_hours-*
- config_name: german
data_files:
- split: dev
path: german/dev-*
- split: test
path: german/test-*
- split: train
path: german/train-*
- split: 9_hours
path: german/9_hours-*
- split: 1_hours
path: german/1_hours-*
- 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
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [MultiLingual LibriSpeech ASR corpus](http://www.openslr.org/94)
- **Repository:** [Needs More Information]
- **Paper:** [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://arxiv.org/abs/2012.03411)
- **Leaderboard:** [🤗 Autoevaluate Leaderboard](https://huggingface.co./spaces/autoevaluate/leaderboards?dataset=facebook%2Fmultilingual_librispeech&only_verified=0&task=automatic-speech-recognition&config=-unspecified-&split=-unspecified-&metric=wer)
### Dataset Summary
This is a streamable version of the Multilingual LibriSpeech (MLS) dataset.
The data archives were restructured from the original ones from [OpenSLR](http://www.openslr.org/94) 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. It includes about 44.5K hours of English and a total of about 6K hours for other languages.
### 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.
- `text-to-speech`, `text-to-audio`: The dataset can also be used to train a model for Text-To-Speech (TTS).
### 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):
```python
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.
```python
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](https://huggingface.co./docs/datasets/use_with_pytorch) directly with your own datasets (local/streamed).
Local:
```python
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:
```python
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](https://huggingface.co./blog/audio-datasets).
### Example scripts
Train your own CTC or Seq2Seq Automatic Speech Recognition models on MultiLingual Librispeech with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition).
## 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
| Number of samples | 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](https://creativecommons.org/licenses/by/4.0/legalcode))
### 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}
}
```
### Data Statistics
| Duration (h) | Train | Dev | Test |
|--------------|-----------|-------|-------|
| English | 44,659.74 | 15.75 | 15.55 |
| German | 1,966.51 | 14.28 | 14.29 |
| Dutch | 1,554.24 | 12.76 | 12.76 |
| French | 1,076.58 | 10.07 | 10.07 |
| Spanish | 917.68 | 9.99 | 10 |
| Italian | 247.38 | 5.18 | 5.27 |
| Portuguese | 160.96 | 3.64 | 3.74 |
| Polish | 103.65 | 2.08 | 2.14 |
| # Speakers | Train | | Dev | | Test | |
|------------|-------|------|-----|----|------|----|
| Gender | M | F | M | F | M | F |
| English | 2742 | 2748 | 21 | 21 | 21 | 21 |
| German | 81 | 95 | 15 | 15 | 15 | 15 |
| Dutch | 9 | 31 | 3 | 3 | 3 | 3 |
| French | 62 | 80 | 9 | 9 | 9 | 9 |
| Spanish | 36 | 50 | 10 | 10 | 10 | 10 |
| Italian | 22 | 43 | 5 | 5 | 5 | 5 |
| Portuguese | 26 | 16 | 5 | 5 | 5 | 5 |
| Polish | 6 | 5 | 2 | 2 | 2 | 2 |
| # Hours / Gender | Dev | | Test | |
|------------------|------|------|------|------|
| Gender | M | F | M | F |
| English | 7.76 | 7.99 | 7.62 | 7.93 |
| German | 7.06 | 7.22 | 7 | 7.29 |
| Dutch | 6.44 | 6.32 | 6.72 | 6.04 |
| French | 5.13 | 4.94 | 5.04 | 5.02 |
| Spanish | 4.91 | 5.08 | 4.78 | 5.23 |
| Italian | 2.5 | 2.68 | 2.38 | 2.9 |
| Portuguese | 1.84 | 1.81 | 1.83 | 1.9 |
| Polish | 1.12 | 0.95 | 1.09 | 1.05 |
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) and [@polinaeterna](https://github.com/polinaeterna) for adding this dataset.