joaoantoniocn / AM-SincNetLinks
The Additive Margin SincNet (AM-SincNet) is a new approach for speaker recognition problems which is based in the neural network architecture SincNet and the additive margin softmax (AM-Softmax) loss function. It uses the architecture of the SincNet, but with an improved AM-Softmax layer.
☆45Updated last year
Alternatives and similar repositories for AM-SincNet
Users that are interested in AM-SincNet are comparing it to the libraries listed below
Sorting:
- Companion repository for the paper "A Comparison of Metric Learning Loss Functions for End-to-End Speaker Verification" published at SLSP…☆60Updated 4 years ago
- Development Toolkit for the VoxCeleb Speaker Recognition Challenge 2020☆42Updated 4 years ago
- VoxSRC Challenge☆31Updated 6 years ago
- Trained speaker embedding deep learning models and evaluation pipelines in pytorch and tesorflow for speaker recognition.☆36Updated 5 years ago
- WavEncoder is a Python library for encoding audio signals, transforms for audio augmentation, and training audio classification models wi…☆91Updated 4 years ago
- The Additive Margin MobileNet1D is a new light weight deep learning model for Speaker Recognition which is based on the MobileNetV2 archi…☆30Updated last year
- Mispronunciation detection code for jingju singing voice☆20Updated 6 years ago
- Spectra extraction tutorials based on torch and torchaudio.☆41Updated last year
- Deep multi-metric learning for text-independent speaker verification☆24Updated 5 years ago
- Transformer-based online speech recognition system with TensorFlow 2☆26Updated 4 years ago
- Pytorch implementation of "Generalized End-to-End Loss for Speaker Verification"☆103Updated 6 years ago
- PyTorch reimplementation of per-channel energy normalization for audio.☆99Updated 6 years ago
- Audio data augmentation examples☆34Updated 7 years ago
- implementation of "EFFICIENT KEYWORD SPOTTING USING DILATED CONVOLUTIONS AND GATING"☆36Updated 5 years ago
- ☆60Updated 4 years ago
- PyTorch implementation of a self-attentive speaker embedding☆17Updated 5 years ago
- Code and instruction on replicating the experiments done in paper: Unified Hypersphere Embedding for Speaker Recognition☆31Updated 6 years ago
- DropClass and DropAdapt - repository for the paper accepted to Speaker Odyssey 2020☆22Updated 4 years ago
- An implementation of RNN-Transducer loss in TF-2.0.☆45Updated 2 years ago
- Pytorch implementation of Meta-Learning for Short Utterance Speaker Recognition with Imbalance Length Pairs (Interspeech, 2020)☆74Updated 4 years ago
- Implementation for paper "iMetricGAN: Intelligibility Enhancement for Speech-in-Noise using Generative Adversarial Network-based Metric L…☆55Updated 2 years ago
- Robust Speech Activity Detection (SAD) in movie audio☆26Updated 4 years ago
- ☆16Updated 6 years ago
- Meta-embeddings are a probabilistic generalization of embeddings in machine learning.☆22Updated 6 years ago
- This python code performs an efficient speech reverberation starting from a dataset of close-talking speech signals and a collection of a…☆95Updated 5 years ago
- PyTorch implementation of a Time Delay Neural Network (TDNN)☆41Updated 6 years ago
- Speech command recognition with capsule network & various NNs / KWS on Google Speech Command Dataset.☆25Updated 6 years ago
- an Audio-Visual Voice Activity Detection using Deep Learning☆49Updated 6 years ago
- E2E-SincNet: Toward fully end-to-end speech recognition☆30Updated 5 years ago
- Pypi installable TDNN and TDNN-F layers for PyTorch based acoustic model training☆40Updated 4 years ago