malaya-speech 0.0.1.6

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 Waveform UNET Malaya-Speech models.

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

• Super Resolution, Super Resolution 4x using Pretrained Super Resolution Malaya-Speech models.

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

• Vocoder, convert Mel to Waveform using Pretrained MelGAN, Multiband MelGAN and Universal MelGAN Vocoder Malaya-Speech models.

• Voice Activity Detection, detect voice activities using Finetuned Speaker Vector Malaya-Speech models.

• Voice Conversion, Many-to-One, One-to-Many, Many-to-Many, and Zero-shot Voice Conversion.

Pretrained Models

Malaya-Speech also released pretrained models, simply check at malaya-speech/pretrained-model

• Wave UNET, Multi-Scale Neural Network for End-to-End Audio Source Separation, https://arxiv.org/abs/1806.03185

• Wave ResNet UNET, added ResNet style into Wave UNET, no paper produced.

• Deep Speaker, An End-to-End Neural Speaker Embedding System, https://arxiv.org/pdf/1705.02304.pdf

• SpeakerNet, 1D Depth-wise Separable Convolutional Network for Text-Independent Speaker Recognition and Verification, https://arxiv.org/abs/2010.12653

• VGGVox, a large-scale speaker identification dataset, https://arxiv.org/pdf/1706.08612.pdf

• GhostVLAD, Utterance-level Aggregation For Speaker Recognition In The Wild, https://arxiv.org/abs/1902.10107

• Conformer, Convolution-augmented Transformer for Speech Recognition, https://arxiv.org/abs/2005.08100

• ALConformer, A lite Conformer, no paper produced.

• Jasper, An End-to-End Convolutional Neural Acoustic Model, https://arxiv.org/abs/1904.03288

• Tacotron2, Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions, https://arxiv.org/abs/1712.05884

• FastSpeech2, Fast and High-Quality End-to-End Text to Speech, https://arxiv.org/abs/2006.04558

• MelGAN, Generative Adversarial Networks for Conditional Waveform Synthesis, https://arxiv.org/abs/1910.06711

• Multi-band MelGAN, Faster Waveform Generation for High-Quality Text-to-Speech, https://arxiv.org/abs/2005.05106

• SRGAN, Modified version of SRGAN to do 1D Convolution, Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network, https://arxiv.org/abs/1609.04802

• Speech Enhancement UNET, https://github.com/haoxiangsnr/Wave-U-Net-for-Speech-Enhancement

• Universal MelGAN, Universal MelGAN: A Robust Neural Vocoder for High-Fidelity Waveform Generation in Multiple Domains, https://arxiv.org/abs/2011.09631

• FastVC, Faster and Accurate Voice Conversion using Transformer, no paper produced.

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.