README.md

metadata

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 dataset module since i refered to its source code.

• three types of transcripts are given

  • text_normalized : the trascript from Librispeech ASR
  • text, text_raw : the trascripts from Librispeech-PC

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

  {'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},
        }