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