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mesolitica/Malaysian-STT-Whisper

Malaysian STT Whisper format Heavy postprocessing and post-translation to improve pseudolabeled Whisper Large V3. Also include word level timestamp. Postprocessing Check repetitive trigrams. Verify Voice Activity using Silero-VAD. Verify scores using Force Alignment. Post-translation We use mesolitica/nanot5-base-malaysian-translation-v2.1. Dataset involved Malaysian context v2 Singaporean context Indonesian context Mandarin audio… See the full description on the dataset page: https://huggingface.co/datasets/mesolitica/Malaysian-STT-Whisper.

sourceHugging Faceupdated 1y agoView on Hugging Face
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Dataset Card

Malaysian STT Whisper format

Heavy postprocessing and post-translation to improve pseudolabeled Whisper Large V3. Also include word level timestamp.

Postprocessing

  1. 1.Check repetitive trigrams.
  2. 2.Verify Voice Activity using Silero-VAD.
  3. 3.Verify scores using Force Alignment.

Post-translation

We use mesolitica/nanot5-base-malaysian-translation-v2.1.

Dataset involved

  1. 1.Malaysian context v2
  2. 2.Singaporean context
  3. 3.Indonesian context
  4. 4.Mandarin audio
  5. 5.Tamil audio
  6. 6.Science context
  7. 7.Malay sarawak
  8. 8.Scripted Malay Daily Use Speech Corpus
  9. 9.Malay Conversational Speech Corpus
  10. 10.Iban

how to prepare the dataset

bash
huggingface-cli download --repo-type dataset \
--include '*.zip' \
--local-dir './' \
--max-workers 20 \
mesolitica/Malaysian-STT-Whisper

wget https://gist.githubusercontent.com/huseinzol05/2e26de4f3b29d99e993b349864ab6c10/raw/9b2251f3ff958770215d70c8d82d311f82791b78/unzip.py
python3 unzip.py

Source code

Source code at https://github.com/mesolitica/malaysian-dataset/tree/master/speech-to-text-semisupervised/distilled-malaysian-whisper