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Datasets:
mesolitica
/
Malaysian-STT-Whisper

Tasks:
Automatic Speech Recognition
Languages:
Malay
English
Chinese
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Malaysian-STT-Whisper / README.md
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metadata
task_categories:
  - automatic-speech-recognition
language:
  - ms
  - en
  - zh
  - ta
  - id

Malaysian STT Whisper format

Up to 15k hours annotated, we done 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. Malay audiobook
  4. Singaporean context
  5. Indonesian context
  6. Mandarin audio
  7. Tamil audio
  8. Science context
  9. Malay sarawak
  10. Scripted Malay Daily Use Speech Corpus
  11. Malay Conversational Speech Corpus
  12. Iban
  13. Malay dialects

Word level timestamp

  1. Malaysian context v1, 658.54 hours.
{"audio_filename": "prepared-pseudolabel-malaya-chunks/2-0.mp3", "new_text": "<|startoftranscript|><|ms|><|transcribeprecise|><|0.00|> luar<|0.34|><|0.42|> kan<|0.60|><|1.78|> sebab<|2.06|><|2.24|> benda<|2.42|><|2.52|> ni<|2.58|><|2.70|> berlaku<|3.08|><|3.20|> contoh<|3.50|><|3.56|> kita<|3.72|><|3.80|> pergi<|3.98|><|4.10|> ke<|4.16|><|4.40|> ATM<|4.76|><|5.70|> yang<|5.80|><|5.84|> bukan<|6.02|><|6.10|> Islam,<|6.34|><|6.96|> siang<|7.12|><|7.18|> hari<|7.42|><|endoftext|>"}
  1. Malaysian context v2, 8058.17 hours.
{"audio_filename": "prepared-pseudolabel-chunks/0-0.mp3", "new_text": "<|startoftranscript|><|ms|><|transcribeprecise|><|0.00|> tu<|0.04|><|0.20|> So<|0.26|><|0.70|> gaji<|0.96|><|1.06|> berbeza<|1.42|><|2.46|> Gaji<|2.86|><|endoftext|>"}
  1. Singaporean context, 1829.21 hours.
{"audio_filename": "prepared-imda-chunks/0-0.mp3", "new_text": "<|startoftranscript|><|en|><|transcribeprecise|><|0.00|> Households<|0.58|><|0.64|> with<|0.76|><|0.86|> target<|1.16|><|1.24|> sets<|1.50|><|1.70|> were<|1.82|><|1.90|> encouraged<|2.40|><|2.44|> to<|2.48|><|2.62|> try<|2.80|><|2.90|> keeping<|3.24|><|endoftext|>"}
  1. Science context, 4992.42 hours.
{"audio_filename": "prepared-science-chunks/0-0.mp3", "new_text": "<|startoftranscript|><|en|><|transcribeprecise|><|0.00|> Visual<|0.24|><|0.30|> Studio<|0.60|><|0.76|> Code<|1.00|><|1.06|> integration.<|1.68|><|3.46|> Here's<|3.70|><|3.76|> what<|3.88|><|3.94|> will<|4.06|><|4.10|> be<|4.14|><|4.28|> new.<|4.44|><|5.36|> You<|5.42|><|5.46|> will<|5.58|><|5.62|> have<|5.74|><|5.82|> more<|5.96|><|6.08|> choice<|6.40|><|6.48|> on<|6.52|><|6.60|> runtime<|6.96|><|7.02|> experiences.<|7.82|><|8.78|> Java<|9.06|><|9.16|> interoperability<|10.04|><|10.22|> will<|10.32|><|10.36|> be<|10.40|><|10.48|> available<|10.90|><|10.96|> on<|11.00|><|11.08|> all<|11.14|><|11.22|> platforms.<|11.78|><|12.22|> Objective<|12.68|><|12.78|> C<|12.78|><|13.00|> and<|13.06|><|13.16|> Swift<|13.42|><|13.48|> interoperability<|14.38|><|14.92|> will<|15.04|><|15.08|> be<|15.12|><|15.26|> supported<|15.70|><|15.78|> on<|15.82|><|15.90|> multiple<|16.26|><|16.34|> operating<|16.78|><|16.86|> systems.<|17.28|><|18.28|> Core<|18.62|><|18.74|> FX<|18.98|><|19.20|> will<|19.30|><|19.34|> be<|19.38|><|19.46|> extended<|19.96|><|20.30|> to<|20.34|><|20.42|> support<|20.74|><|20.84|> static<|21.16|><|21.22|> compilation<|22.04|><|endoftext|>"}

how to prepare the dataset 

wget https://www.7-zip.org/a/7z2301-linux-x64.tar.xz
tar -xf 7z2301-linux-x64.tar.xz

# Malaysian context
wget https://huggingface.co/datasets/mesolitica/Malaysian-STT-Whisper/resolve/main/malaysian-stt.jsonl
huggingface-cli download --repo-type dataset \
--include 'output-audio-malaya.z*' \
--local-dir './' \
mesolitica/pseudolabel-malaya-speech-stt-train-whisper-large-v3-timestamp
./7zz x output-audio-malaya.zip -y -mmt40
huggingface-cli download --repo-type dataset \
--include 'output-audio.z*' \
--local-dir './' \
mesolitica/pseudolabel-malaysian-youtube-whisper-large-v3-timestamp
./7zz x output-audio.zip -y -mmt40

# Malay audiobook
wget https://huggingface.co/datasets/mesolitica/pseudolabel-nusantara-large-v3-timestamp/resolve/main/split-nusantara.zip
wget https://huggingface.co/datasets/mesolitica/pseudolabel-nusantara-large-v3-timestamp/resolve/main/prepared-nusantara.jsonl
unzip split-nusantara.zip

# Singaporean context
wget https://huggingface.co/datasets/mesolitica/pseudolabel-imda-large-v3-timestamp/resolve/main/prepared-imda.jsonl
wget https://huggingface.co/datasets/mesolitica/pseudolabel-imda-large-v3-timestamp/resolve/main/prepared-imda-ms.jsonl
huggingface-cli download --repo-type dataset \
--include '*.7z*' \
--local-dir './' \
mesolitica/IMDA-STT
./7zz x part1-mp3.7z.001 -y -mmt40
./7zz x part2-mp3.7z.001 -y -mmt40
./7zz x part3-same-audio-mp3.7z.001 -y -mmt40
./7zz x part3-separate-audio-mp3.7z.001 -y -mmt40
./7zz x part4-same-audio-mp3.7z.001 -y -mmt40
./7zz x part4-separate-audio-mp3.7z.001 -y -mmt40
./7zz x part5-same-audio-mp3.7z.001 -y -mmt40
./7zz x part5-separate-audio-mp3.7z.001 -y -mmt40
./7zz x part6-1-audio-mp3.7z.001 -y -mmt40
./7zz x part6-2-audio-mp3.7z.001 -y -mmt40
./7zz x part6-3-audio-mp3.7z.001 -y -mmt40

# Indonesian context
huggingface-cli download --repo-type dataset \
--include 'split-indonesian.z*' \
--local-dir './' \
mesolitica/pseudolabel-indonesian-large-v3-timestamp
./7zz x split-indonesian.zip -y -mmt40

# Science context
wget https://huggingface.co/datasets/mesolitica/Malaysian-STT-Whisper/resolve/main/science-en-stt.jsonl
wget https://huggingface.co/datasets/mesolitica/Malaysian-STT-Whisper/resolve/main/science-ms-stt.jsonl
huggingface-cli download --repo-type dataset \
--include 'audio-chunk.z*' \
--local-dir './' \
mesolitica/pseudolabel-science-large-v3-timestamp
./7zz x audio-chunk.zip -y -mmt40

# Malay sarawak
wget https://huggingface.co/datasets/malaysia-ai/sarawakmalay-whisper-format/resolve/main/sarawakmalay.zip
wget https://huggingface.co/datasets/malaysia-ai/sarawakmalay-whisper-format/resolve/main/dataset.json -O sarawakmalay.json
unzip sarawakmalay.zip

# for Scripted Malay Daily Use Speech Corpus
wget https://huggingface.co/datasets/malaysia-ai/scripted-malay-daily-use-speech-corpus-whisper-format/resolve/main/scripted-malay-daily-use-speech-corpus-whisper-format.zip
wget https://huggingface.co/datasets/malaysia-ai/scripted-malay-daily-use-speech-corpus-whisper-format/resolve/main/scripted-malay-daily-use-speech-corpus-whisper-format.json
unzip scripted-malay-daily-use-speech-corpus-whisper-format.zip

# Malay Conversational Speech Corpus
wget https://huggingface.co/datasets/malaysia-ai/malay-conversational-speech-corpus-whisper-format/resolve/main/malay-conversational-speech-corpus-whisper-format.zip
wget https://huggingface.co/datasets/malaysia-ai/malay-conversational-speech-corpus-whisper-format/resolve/main/malay-conversational-speech-corpus-whisper-format.json
unzip malay-conversational-speech-corpus-whisper-format.zip

# Iban
wget https://huggingface.co/datasets/malaysia-ai/iban-whisper-format/resolve/main/iban-wav.zip
wget https://huggingface.co/datasets/malaysia-ai/iban-whisper-format/resolve/main/iban-dataset.json
unzip iban-wav.zip

Source code

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