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---
license: cc-by-4.0
task_categories:
- automatic-speech-recognition
language:
- en
size_categories:
- 100K<n<1M
---
# Librispeech-PC (punctuation and capitalization restored)
### Original Dataset can be found in <https://www.openslr.org/145/>
- I made it for personal use, so the code might not be pefect.
### How to Use
- Almost the same with [Librispeech](https://huggingface.co/datasets/openslr/librispeech_asr) dataset module since i refered to its [source code](https://huggingface.co/datasets/openslr/librispeech_asr/blob/main/librispeech_asr.py).
- three types of transcripts are given
- `text_normalized` : the trascript from Librispeech ASR
- `text`, `text_raw` : the trascripts from Librispeech-PC
```python
from datasets import load_dataset
from pprint import pprint
from IPython.display import display, Audio
import aiohttp
import os
# set huggingface cache directory for the extracted raw files and huggingface-cli token
# if don't, you might download the raw tar.gz file in home cache dir even if you set `cache_dir` param
os.environ['HF_HOME'] = "/data_dir/to/download"
!export HF_HOME="/data_dir/to/download"
# download dataset
# if already have librispeech_asr in the cache_dir it will use the same audio files.
libripc = load_dataset("yoom618/librispeech_pc",
"all", # all, clean, other
cache_dir="/data_dir/to/download",
trust_remote_code=True,
# storage_options={
# # add if you need to increase the timeout for openslr download
# 'client_kwargs': {'timeout': aiohttp.ClientTimeout(total=7200)}
# },
)
# check dataset info
print(libripc)
display(Audio(libripc['train.clean.100'][0]['audio']['array'],
rate=libripc['train.clean.100'][0]['audio']['sampling_rate'],
autoplay=False))
pprint(libripc['train.clean.100'][0]['audio'])
```
- Data Sample
```raw
{'audio': {'array': array([ 7.01904297e-04, 7.32421875e-04, 7.32421875e-04, ...,
-2.74658203e-04, -1.83105469e-04, -3.05175781e-05]),
'path': '/data_dir/to/download/downloads/extracted/.../374-180298-0000.flac',
'sampling_rate': 16000},
'chapter_id': 180298,
'duration': 14.529999732971191,
'file': '/data_dir/to/download/downloads/extracted/.../374-180298-0000.flac',
'id': '374-180298-0000',
'speaker_id': 374,
'text': 'Chapter sixteen I might have told you of the beginning of this '
'liaison in a few lines, but I wanted you to see every step by which '
'we came, I to agree to whatever Marguerite wished,',
'text_normalized': 'CHAPTER SIXTEEN I MIGHT HAVE TOLD YOU OF THE BEGINNING OF '
'THIS LIAISON IN A FEW LINES BUT I WANTED YOU TO SEE EVERY '
'STEP BY WHICH WE CAME I TO AGREE TO WHATEVER MARGUERITE '
'WISHED',
'text_raw': 'Chapter sixteen I might have told you of the beginning of this '
'liaison in a few lines, but I wanted you to see every step by '
'which we came, I to agree to whatever Marguerite wished,'}
```
### The number of samples in Librispeech-PC
- train
- `train.clean.100` : 26,041 ( 2,498 out of 28,539 were dropped )
- `train.clean.360` : 95,404 ( 8,610 out of 104,014 were dropped )
- `train.other.500` : 134,679 ( 14,009 out of 148,688 were dropped )
- dev (validation)
- `dev.clean` : 2,530 ( 173 out of 2,703 were dropped )
- `dev.other` : 2,728 ( 136 out of 2,864 were dropped )
- test
- `test.clean` : 2,417 ( 203 out of 2,620 were dropped )
- `test.other` : 2,856 ( 83 out of 2,939 were dropped )
### Citation Information
```
@article{meister2023librispeechpc,
title={LibriSpeech-PC: Benchmark for Evaluation of Punctuation and Capitalization Capabilities of end-to-end ASR Models},
author={A. Meister and M. Novikov and N. Karpov and E. Bakhturina and V. Lavrukhin and B. Ginsburg},
journal={arXiv preprint arXiv:2310.02943},
year={2023},
}
```
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