---
dataset_info:
  features:
  - name: audio
    dtype:
      audio:
        sampling_rate: 16000
  - name: transcription
    dtype: string
  splits:
  - name: train
    num_bytes: 3125353264.6964455
    num_examples: 5778
  - name: test
    num_bytes: 1004055850.0756147
    num_examples: 1683
  download_size: 3490774262
  dataset_size: 4129409114.7720604
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
task_categories:
- automatic-speech-recognition
- text-to-speech
language:
- km
tags:
- openslr42
- fleurs
- asr
---

__NOTE:__ If your colab crashes, please use `pip install --upgrade --quiet datasets[audio]==3.6.0` to install `datasets[audio]` version `3.6.0`.


This dataset combined [google/fleurs](https://huggingface.co/datasets/google/fleurs), [openslr/openslr42](https://huggingface.co/datasets/openslr/openslr), and cleaned [seanghay/khmer_mpwt_speech](https://huggingface.co/datasets/seanghay/khmer_mpwt_speech).
Severals processes are executed:
1. clean up [seanghay/khmer_mpwt_speech](https://huggingface.co/datasets/seanghay/khmer_mpwt_speech): manually correct wrong transcriptions over 2058 rows
2. normalize transcription: remove invisible white space; process `ៗ`, numbers, currencies, date into khmer text; and separate each word by space
3. filter out texts whose number of token ids are more than 448: use tokenizer of Whisper-Small to encode text and filter out sequences longer than 448
4. filter out audio with length longer than 30 seconds
5. resample audio to 16000kHz

__Disclaimer__ I do not own any of these datasets.