---
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](https://huggingface.co/mesolitica/nanot5-base-malaysian-translation-v2.1).

## Dataset involved

1. [Malaysian context v1](https://huggingface.co/datasets/mesolitica/pseudolabel-malaya-speech-stt-train-whisper-large-v3-timestamp)
2. [Malaysian context v2](https://huggingface.co/datasets/mesolitica/pseudolabel-malaysian-youtube-whisper-large-v3-timestamp)
3. [Malay audiobook](https://huggingface.co/datasets/mesolitica/pseudolabel-nusantara-large-v3-timestamp)
4. [Singaporean context](https://huggingface.co/datasets/mesolitica/pseudolabel-imda-large-v3-timestamp)
5. [Indonesian context](https://huggingface.co/datasets/mesolitica/pseudolabel-indonesian-large-v3-timestamp)
6. [Mandarin audio](https://huggingface.co/datasets/mesolitica/pseudolabel-mandarin-large-v3-timestamp)
7. [Tamil audio](https://huggingface.co/datasets/mesolitica/pseudolabel-tamil-large-v3-timestamp)
8. [Science context](https://huggingface.co/datasets/mesolitica/pseudolabel-science-large-v3-timestamp)
9. [Malay sarawak](https://huggingface.co/datasets/malaysia-ai/sarawakmalay-whisper-format)
10. [Scripted Malay Daily Use Speech Corpus](https://huggingface.co/datasets/malaysia-ai/scripted-malay-daily-use-speech-corpus-whisper-format)
11. [Malay Conversational Speech Corpus](https://huggingface.co/datasets/malaysia-ai/malay-conversational-speech-corpus-whisper-format)
12. [Iban](https://huggingface.co/datasets/malaysia-ai/iban-whisper-format)
13. [Malay dialects](https://huggingface.co/datasets/mesolitica/pseudolabel-malay-dialects-large-v3-timestamp)

### 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|>"}
```

- [Label](pseudolabel-malaya-whisper-word-timestamp.jsonl)
- [Audio](prepared-pseudolabel-malaya-chunks.zip)
  
2. 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|>"}
```

- [Label](pseudolabel-whisper-word-timestamp.jsonl)
- [Audio](prepared-pseudolabel_alignment)
  
3. 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|>"}
```

- [Label](imda-whisper-word-timestamp.jsonl)
- [Audio](prepared-imda_alignment)
  
4. 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|>"}
```
   
- [Label](prepared-science-word-timestamp.jsonl)
- [Audio](prepared-science_alignment)

## how to prepare the dataset

```bash
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