rafaelvalle / Asrgen
Attacking Speaker Recognition with Deep Generative Models
Stars: ✭ 31
Projects that are alternatives of or similar to Asrgen
Hands On Deep Learning Algorithms With Python
Master Deep Learning Algorithms with Extensive Math by Implementing them using TensorFlow
Stars: ✭ 272 (+777.42%)
Mutual labels: jupyter-notebook, gans
Tts
🤖 💬 Deep learning for Text to Speech (Discussion forum: https://discourse.mozilla.org/c/tts)
Stars: ✭ 5,427 (+17406.45%)
Mutual labels: jupyter-notebook, text-to-speech
Generative models tutorial with demo
Generative Models Tutorial with Demo: Bayesian Classifier Sampling, Variational Auto Encoder (VAE), Generative Adversial Networks (GANs), Popular GANs Architectures, Auto-Regressive Models, Important Generative Model Papers, Courses, etc..
Stars: ✭ 276 (+790.32%)
Mutual labels: jupyter-notebook, gans
myG2P
Myanmar (Burmese) Language Grapheme to Phoneme (myG2P) Conversion Dictionary for speech recognition (ASR) and speech synthesis (TTS).
Stars: ✭ 43 (+38.71%)
Mutual labels: text-to-speech, asr
Silero Models
Silero Models: pre-trained STT models and benchmarks made embarrassingly simple
Stars: ✭ 522 (+1583.87%)
Mutual labels: jupyter-notebook, asr
spokestack-ios
Spokestack: give your iOS app a voice interface!
Stars: ✭ 27 (-12.9%)
Mutual labels: text-to-speech, asr
Shapematchinggan
[ICCV 2019, Oral] Controllable Artistic Text Style Transfer via Shape-Matching GAN
Stars: ✭ 315 (+916.13%)
Mutual labels: jupyter-notebook, gans
Nemo
NeMo: a toolkit for conversational AI
Stars: ✭ 3,685 (+11787.1%)
Mutual labels: jupyter-notebook, text-to-speech
Nmtpytorch
Sequence-to-Sequence Framework in PyTorch
Stars: ✭ 392 (+1164.52%)
Mutual labels: jupyter-notebook, asr
Tts
🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
Stars: ✭ 305 (+883.87%)
Mutual labels: jupyter-notebook, text-to-speech
react-native-spokestack
Spokestack: give your React Native app a voice interface!
Stars: ✭ 53 (+70.97%)
Mutual labels: text-to-speech, asr
Gans
Generative Adversarial Networks implemented in PyTorch and Tensorflow
Stars: ✭ 714 (+2203.23%)
Mutual labels: jupyter-notebook, gans
Wavegrad
Implementation of Google Brain's WaveGrad high-fidelity vocoder (paper: https://arxiv.org/pdf/2009.00713.pdf). First implementation on GitHub.
Stars: ✭ 245 (+690.32%)
Mutual labels: jupyter-notebook, text-to-speech
spokestack-android
Extensible Android mobile voice framework: wakeword, ASR, NLU, and TTS. Easily add voice to any Android app!
Stars: ✭ 52 (+67.74%)
Mutual labels: text-to-speech, asr
Faceswap Gan
A denoising autoencoder + adversarial losses and attention mechanisms for face swapping.
Stars: ✭ 3,099 (+9896.77%)
Mutual labels: jupyter-notebook, gans
Aulas
Aulas da Escola de Inteligência Artificial de São Paulo
Stars: ✭ 166 (+435.48%)
Mutual labels: jupyter-notebook, gans
Deep Math Machine Learning.ai
A blog which talks about machine learning, deep learning algorithms and the Math. and Machine learning algorithms written from scratch.
Stars: ✭ 173 (+458.06%)
Mutual labels: jupyter-notebook, gans
Sdv
Synthetic Data Generation for tabular, relational and time series data.
Stars: ✭ 360 (+1061.29%)
Mutual labels: jupyter-notebook, gans
Parallelwavegan
Unofficial Parallel WaveGAN (+ MelGAN & Multi-band MelGAN) with Pytorch
Stars: ✭ 682 (+2100%)
Mutual labels: jupyter-notebook, text-to-speech
Attacking Speaker Recognition Systems with Deep Generative Models
PyTorch implementation of Attacking Speaker Recognition Systems with Deep Generative Models.
Pre-requisites
- NVIDIA GPU + CUDA cuDNN
Data and pre-trained models:
Setup
- Clone this repo:
git clone https://github.com/rafaelvalle/asrgen.git - CD into this repo:
cd asrgen - Download and unzip audio data into this repo
- Install python requirements:
pip install -r requirements.txt
Training
python gan_train.py- (OPTIONAL)
tensorboard --logdir=./
Synthesize audio samples with a Generator
jupyter notebook --ip=127.0.0.1 --port=31337- load
gan_synthesis.ipynb
Acknowledgements
This implementation uses code from the following repos: [NVIDIA's Tacotron 2] (https://github.com/nvidia/tacotron2), Martin Arjovsky and Prem Seetharaman.
We are thankful to Prem Seetharaman and Markus Rabe for their feedback on the early draft of this paper.
We are grateful to NVIDIA for donating the Titan X used in this research.
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].