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Datasets:
mesolitica
/
Malaysian-STT-Whisper

Tasks:
Automatic Speech Recognition
Languages:
Malay
English
Chinese
Dataset card Files Community
2
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The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      JSON parse error: Invalid value. in row 0
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 160, in _generate_tables
                  df = pandas_read_json(f)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
                  return pd.read_json(path_or_buf, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 791, in read_json
                  json_reader = JsonReader(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 905, in __init__
                  self.data = self._preprocess_data(data)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 917, in _preprocess_data
                  data = data.read()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 826, in read_with_retries
                  out = read(*args, **kwargs)
                File "/usr/local/lib/python3.9/codecs.py", line 322, in decode
                  (result, consumed) = self._buffer_decode(data, self.errors, final)
              UnicodeDecodeError: 'utf-8' codec can't decode byte 0xce in position 4: invalid continuation byte
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 231, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2998, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1918, in _head
                  return _examples_to_batch(list(self.take(n)))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2093, in __iter__
                  for key, example in ex_iterable:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1576, in __iter__
                  for key_example in islice(self.ex_iterable, self.n - ex_iterable_num_taken):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 279, in __iter__
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 163, in _generate_tables
                  raise e
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 137, in _generate_tables
                  pa_table = paj.read_json(
                File "pyarrow/_json.pyx", line 308, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: JSON parse error: Invalid value. in row 0

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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|>"}
  1. IMDA Part 2, 931 hours.
{"text": "<|startoftranscript|><|en|><|transcribeprecise|><|0.54|> Mimosa<|1.00|><|1.14|> Walk<|1.36|><|1.72|> Mount<|2.00|><|2.06|> Sophia<|2.42|><|2.90|> and<|3.00|><|3.08|> Jalan<|3.38|><|3.50|> Ilmu.<|4.38|><|endoftext|>", "audio_filename": "filter-imda-part2/train-00387-of-00727-6.mp3"}

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

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