Speech Toolkit#

Malaya-Speech is a Speech-Toolkit library for bahasa Malaysia, powered by Deep Learning Tensorflow. We maintain it at separate repository, https://github.com/huseinzol05/malaya-speech

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 3.6.0 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.

Features#

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, FastPitch, GlowTTS, LightSpeech and VITS.
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.

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.
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/abs/2106.06103

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 KeyReply for private V100s cloud and Mesolitica for private RTXs cloud to train Malaya-Speech models,

Speech Toolkit

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