Malaya-Speech is a Speech-Toolkit library for bahasa Malaysia, powered by Deep Learning Tensorflow.
Proper documentation is available at https://malaya-speech.readthedocs.io/
CPU version
$ pip install malaya-speech
GPU version
$ pip install malaya-speech[gpu]
Only Python 3.6.0 and above and Tensorflow 1.15.0 and above are supported.
We recommend to use virtualenv for development. All examples tested on Tensorflow version 1.15.4, 1.15.5, 2.4.1 and 2.5.
- Age Detection, detect age in speech using Finetuned Speaker Vector.
- Speaker Diarization, diarizing speakers using Pretrained Speaker Vector.
- Emotion Detection, detect emotions in speech using Finetuned Speaker Vector.
- Force Alignment, generate a time-aligned transcription of an audio file using RNNT and CTC.
- Gender Detection, detect genders in speech using Finetuned Speaker Vector.
- Language Detection, detect hyperlocal languages in speech using Finetuned Speaker Vector.
- Multispeaker Separation, Multispeaker separation using FastSep on 8k Wav.
- Noise Reduction, reduce multilevel noises using STFT UNET.
- Speaker Change, detect changing speakers using Finetuned Speaker Vector.
- Speaker overlap, detect overlap speakers using Finetuned Speaker Vector.
- Speaker Vector, calculate similarity between speakers using Pretrained Speaker Vector.
- Speech Enhancement, enhance voice activities using Waveform UNET.
- SpeechSplit Conversion, detailed speaking style conversion by disentangling speech into content, timbre, rhythm and pitch using PyWorld and PySPTK.
- Speech-to-Text, End-to-End Speech to Text for Malay, Mixed (Malay, Singlish and Mandarin) and Singlish using RNNT, Wav2Vec2, HuBERT and BEST-RQ CTC.
- Super Resolution, Super Resolution 4x for Waveform.
- Text-to-Speech, Text to Speech for Malay and Singlish using Tacotron2, FastSpeech2 and FastPitch.
- Vocoder, convert Mel to Waveform using MelGAN, Multiband MelGAN and Universal MelGAN Vocoder.
- Voice Activity Detection, detect voice activities using Finetuned Speaker Vector.
- Voice Conversion, Many-to-One, One-to-Many, Many-to-Many, and Zero-shot Voice Conversion.
- Hybrid 8-bit Quantization, provide hybrid 8-bit quantization for all models to reduce inference time up to 2x and model size up to 4x.
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.
- Wave ResNext UNET, added ResNext 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
- Speech Enhancement ResNet UNET, Added ResNet style into Speech Enhancement UNET, no paper produced.
- Speech Enhancement ResNext UNET, Added ResNext style into Speech Enhancement UNET, no paper produced.
- 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.
- FastSep, Faster and Accurate Speech Separation using Transformer, no paper produced.
- wav2vec 2.0, A Framework for Self-Supervised Learning of Speech Representations, https://arxiv.org/abs/2006.11477
- FastSpeechSplit, Unsupervised Speech Decomposition Via Triple Information Bottleneck using Transformer, no paper produced.
- Sepformer, Attention is All You Need in Speech Separation, https://arxiv.org/abs/2010.13154
- FastSpeechSplit, Faster and Accurate Speech Split Conversion using Transformer, no paper produced.
- HuBERT, Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units, https://arxiv.org/pdf/2106.07447v1.pdf
- FastPitch, Parallel Text-to-speech with Pitch Prediction, https://arxiv.org/abs/2006.06873
- GlowTTS, A Generative Flow for Text-to-Speech via Monotonic Alignment Search, https://arxiv.org/abs/2005.11129
- BEST-RQ, Self-supervised learning with random-projection quantizer for speech recognition, https://arxiv.org/pdf/2202.01855.pdf
- LightSpeech, Lightweight and Fast Text to Speech with Neural Architecture Search, https://arxiv.org/abs/2102.04040
- VITS, Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech, https://arxiv.org/pdf/2106.06103.pdf
- UnivNet, A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation, https://arxiv.org/abs/2106.07889
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}}
}
Thanks to KeyReply for sponsoring private cloud to train Malaya-Speech models, without it, this library will collapse entirely.