malaya-speech 1.3.0

Speech-Toolkit for bahasa Malaysia, powered by Tensorflow and PyTorch.

Malaya-Speech is a Speech-Toolkit library for bahasa Malaysia, powered by Tensorflow and PyTorch.

Documentation

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

Installing from the PyPI

$ pip install malaya-speech

It will automatically install all dependencies except for Tensorflow and PyTorch. So you can choose your own Tensorflow CPU / GPU version and PyTorch CPU / GPU version.

Only Python >= 3.6.0, Tensorflow >= 1.15.0, and PyTorch >= 1.10 are supported.

Development Release

Install from master branch,

$ pip install git+https://github.com/huseinzol05/malaya-speech.git

We recommend to use virtualenv for development.

Documentation at https://malaya-speech.readthedocs.io/en/latest/

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.

• Language Model, using KenLM, Masked language model using BERT and RoBERTa, and GPT2 to do ASR decoder scoring.

• 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 using ResNet UNET and Neural Vocoder.

• 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.