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Datasets:
taras-sereda
/
uk-pods

Tasks:
Automatic Speech Recognition
Modalities:
Audio
Tabular
Text
Formats:
json
Languages:
Ukrainian
Size:
10K - 100K
Tags:
podcasts
Libraries:
Datasets
Croissant
License:
Dataset card Viewer Files Community
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metadata
language:
  - uk
pretty_name: UK-PODS
tags:
  - podcasts
license: cc-by-nc-4.0
task_categories:
  - automatic-speech-recognition

uk-pods - speech datasets of Ukrainian podcasts.

Preparation

  1. Clone the dataset repository and extract the content of clips.tar.gz archive.
git clone https://huggingface.co/datasets/taras-sereda/uk-pods
cd uk-pods && tar -zxvf clips.tar.gz
  1. To use these manifests for training/inference with NeMo [1] modify audio_filepath to absolute locations of audio files extracted in previous step.
# data_root=<clonned_repo_dir> # /home/ubuntu/uk-pods
data_root=$(realpath .)
sed -i -e "s|\"audio_filepath\":\"|\"audio_filepath\":\"${data_root}\/|g" pods_train.json
sed -i -e "s|\"audio_filepath\":\"|\"audio_filepath\":\"${data_root}\/|g" pods_test.json

Usage

  1. Install NeMo toolkit
pip install nemo_toolkit['all']
  1. Run inference with uk-pods-conformer [2] on all files from pods_test.json manifest:
import json
from nemo.collections.asr.models import EncDecCTCModelBPE
asr_model = EncDecCTCModelBPE.from_pretrained("taras-sereda/uk-pods-conformer")

with open('pods_test.json') as fd:
    audio_paths = []
    for line in fd:
        audio_paths.append(json.loads(line)['audio_filepath'])

transcripts = asr_model.transcribe(audio_paths)

Dataset statistics 

Number of wav files: 34231
Total duration: 51.066 hours
MIN duration: 1.020 sec
MAX duration: 19.999 sec
MEAN duration: 5.370 sec
MEDIAN duration: 4.640 sec

References