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huseinzol05/Malay-TTS-Osman

Malay-TTS-Osman All notebooks and code related at https://github.com/huseinzol05/malaya-speech/tree/master/data/azure-tts Attributes Wiki and News 24000 sample rate, super clean. narrator ms-MY-OsmanNeural. approximate 94.5 hours Texts from Malay Wikipedia and News. Sentences between 2 words and 20 words. Parliament 24000 sample rate, super clean. narrator ms-MY-OsmanNeural. approximate 133.2 hours. Texts from Malaysia Malay… See the full description on the dataset page: https://huggingface.co/datasets/huseinzol05/Malay-TTS-Osman.

sourceHugging Faceupdated 4y agoView on Hugging Face
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Dataset Card

Malay-TTS-Osman

All notebooks and code related at https://github.com/huseinzol05/malaya-speech/tree/master/data/azure-tts

Attributes

Wiki and News

  • —24000 sample rate, super clean.
  • —narrator ms-MY-OsmanNeural.
  • —approximate 94.5 hours
  • —Texts from Malay Wikipedia and News.
  • —Sentences between 2 words and 20 words.

Parliament

  • —24000 sample rate, super clean.
  • —narrator ms-MY-OsmanNeural.
  • —approximate 133.2 hours.
  • —Texts from Malaysia Malay Parliament.
  • —Sentences between 2 words and 25 words.

how-to

Wiki and News

  1. 1.Download populated-text.json and tts-malay-osman.tar.gz.
  1. 1.To get wav and transcript,
python
import json
import soundfile as sf

with open('populated-text.json') as fopen:
    texts = json.load(fopen)

index = 0
text = texts[index]
y, sr = sf.read(f'male/{index}.wav')

Parliament

  1. 1.Download populated-parliament.json and tts-malay-osman-parliament.tar.gz.
  1. 1.To get wav and transcript,
python
import json
import soundfile as sf

with open('populated-parliament.json') as fopen:
    texts = json.load(fopen)

index = 0
text = texts[index]
y, sr = sf.read(f'male-parliament/{index}.wav')