malaya-speech 0.0.1.4

Speech-Toolkit for bahasa Malaysia, powered by Deep Learning Tensorflow.

Malaya-Speech is a Speech-Toolkit library for bahasa Malaysia, powered by Deep Learning Tensorflow.

Documentation

Proper documentation is available at https://malaya-speech.readthedocs.io/

Installing from the PyPI

CPU version

$ pip install malaya-speech

GPU version

$ pip install malaya-speech-gpu

Only Python 3.6.x and above and Tensorflow 1.10 and above but not 2.0 are supported.

Features

• Age Detection

  Detect age in speech using Finetuned Speaker Vector Malaya-Speech models.

• Speaker Diarization

  Diarizing speakers using Pretrained Speaker Vector Malaya-Speech models.

• Emotion Detection

  Detect emotions in speech using Finetuned Speaker Vector Malaya-Speech models.

• Gender Detection

  Detect genders in speech using Finetuned Speaker Vector Malaya-Speech models.

• Language Detection

  Detect hyperlocal languages in speech using Finetuned Speaker Vector Malaya-Speech models.

• Noise Reduction

  Reduce multilevel noises using Pretrained STFT UNET Malaya-Speech models.

• Speaker Change

  Detect changing speakers using Finetuned Speaker Vector Malaya-Speech models.

• Speaker overlap

  Detect overlap speakers using Finetuned Speaker Vector Malaya-Speech models.

• Speaker Vector

  Calculate similarity between speakers using Pretrained Malaya-Speech models.

• Speech Enhancement

  Enhance voice activities using Pretrained STFT UNET Malaya-Speech models.

• Speech-to-Text

  End-to-End Speech to Text using Pretrained CTC and RNN Transducer Malaya-Speech models.

• Text-to-Speech

  Text to Speech using Pretrained Tacotron2 and FastSpeech2 Malaya-Speech models.

• Vocoder

  Convert Mel to Waveform using Pretrained MelGAN and Multiband MelGAN Vocoder Malaya-Speech models.

• Voice Activity Detection

  Detect voice activities using Finetuned Speaker Vector Malaya-Speech models.

References

If you use our software for research, please cite:

@misc{Malaya, Speech-Toolkit library for bahasa Malaysia, powered by Deep Learning Tensorflow,
  author = {Husein, Zolkepli},
  title = {Malaya-Speech},
  year = {2020},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/huseinzol05/malaya-speech}}
}

Acknowledgement

Thanks to Mesolitica and KeyReply for sponsoring GCP and private cloud to train Malaya models.