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Trần Nguyễn Đức Thọ's Projects

best-of-ml-python icon best-of-ml-python

🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.

bigvgan icon bigvgan

Official implementation of BigVGAN in PyTorch

cheetah icon cheetah

On-device streaming speech-to-text engine powered by deep learning

comprehensive-e2e-tts icon comprehensive-e2e-tts

A Non-Autoregressive End-to-End Text-to-Speech (text-to-wav), supporting a family of SOTA unsupervised duration modelings. This project grows with the research community, aiming to achieve the ultimate E2E-TTS

comprehensive-transformer-tts icon comprehensive-transformer-tts

A Non-Autoregressive Transformer based TTS, supporting a family of SOTA transformers with supervised and unsupervised duration modelings. This project grows with the research community, aiming to achieve the ultimate TTS.

contextnet icon contextnet

Tensorflow2 based implementation of ContextNet, an improved convolutional rnn-transducer-based architecture for end-to-end speech recognition using global context

coqui-tts icon coqui-tts

🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production

cross-lingual-voice-cloning icon cross-lingual-voice-cloning

Tacotron 2 - PyTorch implementation with faster-than-realtime inference modified to enable cross lingual voice cloning.

crunker icon crunker

Simple way to merge or concatenate audio files with the Web Audio API.

deepbeat icon deepbeat

DeepBeat: Multi-task deep learning for cardiac rhythm detection in wearable devices

deepecg icon deepecg

ECG classification programs based on ML/DL methods

denoiser icon denoiser

Real Time Speech Enhancement in the Waveform Domain (Interspeech 2020)We provide a PyTorch implementation of the paper Real Time Speech Enhancement in the Waveform Domain. In which, we present a causal speech enhancement model working on the raw waveform that runs in real-time on a laptop CPU. The proposed model is based on an encoder-decoder architecture with skip-connections. It is optimized on both time and frequency domains, using multiple loss functions. Empirical evidence shows that it is capable of removing various kinds of background noise including stationary and non-stationary noises, as well as room reverb. Additionally, we suggest a set of data augmentation techniques applied directly on the raw waveform which further improve model performance and its generalization abilities.

diagrams icon diagrams

:art: Diagram as Code for prototyping cloud system architectures

dl-for-emo-tts icon dl-for-emo-tts

:computer: :robot: A summary on our attempts at using Deep Learning approaches for Emotional Text to Speech :speaker:

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