README.md

metadata

task_categories:
  - automatic-speech-recognition
language:
  - ms
  - en
  - zh
  - ta
  - id

Malaysian STT Whisper format

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

Postprocessing

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

Post-translation

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

Dataset involved

1. Malaysian context v1
2. Malaysian context v2
3. Singaporean context
4. Indonesian context
5. Mandarin audio
6. Tamil audio
7. Science context
8. Malay sarawak
9. Scripted Malay Daily Use Speech Corpus
10. Malay Conversational Speech Corpus
11. Iban

how to prepare the dataset

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