ariffleng / malaya-speech

Speech Toolkit for bahasa Malaysia, https://malaya-speech.readthedocs.io/

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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 3.6.0 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, 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.
• 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 and Singlish) and Singlish using RNN-Transducer and Wav2Vec2 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.

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

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 sponsoring private cloud to train Malaya-Speech models, without it, this library will collapse entirely.