233 Open source projects that are alternatives of or similar to AutoSpeech

Naszilla is a Python library for neural architecture search (NAS)

Code for the neural architecture search methods contained in the paper Efficient Forward Neural Architecture Search

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

Awesome Neural Architecture Search Papers

Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search https://arxiv.org/abs/1807.06906

Automated deep learning algorithms implemented in PyTorch.

a distributed Hyperband implementation on Steroids

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PyTorch implementation of PNASNet-5 on ImageNet

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(ICCV 2021) BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture Search

Fast and flexible AutoML with learning guarantees.

[CVPR 2020] MTL-NAS: Task-Agnostic Neural Architecture Search towards General-Purpose Multi-Task Learning

This repo is about NAS

AutoGluon: AutoML for Text, Image, and Tabular Data

A General Automated Machine Learning framework to simplify the development of End-to-end AutoML toolkits in specific domains.

AutoML frAmework for Neural Networks

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A curated list of automated machine learning papers, articles, tutorials, slides and projects

A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Lightweight Structures, 3.) Model Compression, Quantization and Acceleration, 4.) Hyperparameter Optimization, 5.) Automated Feature Engineering.

Differentiable architecture search for convolutional and recurrent networks

SGAS: Sequential Greedy Architecture Search (CVPR'2020) https://www.deepgcns.org/auto/sgas

TensorFlow implementation of PNASNet-5 on ImageNet

AutoML library for deep learning

DeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks

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Gradient based Hyperparameter Tuning library in PyTorch

a deep accent recognition network

A package that makes it trivial to create and evaluate machine learning pipeline architectures.

A Free Library for Speaker Recognition (Verification)，implemented by ncnn.

Meta-embeddings are a probabilistic generalization of embeddings in machine learning.

Automatically Build Multiple ML Models with a Single Line of Code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.

Simple, fast, and parallelized symbolic regression in Python/Julia via regularized evolution and simulated annealing

Trained speaker embedding deep learning models and evaluation pipelines in pytorch and tesorflow for speaker recognition.

OpenML AutoML Benchmarking Framework

A fast and lightweight AutoML library.

基于Kersa实现的声纹识别模型

Code for our ACML and INTERSPEECH papers: "Speaker Diarization as a Fully Online Bandit Learning Problem in MiniVox".

Keras Implementation of Deepmind's WaveNet for Supervised Learning Tasks

A machine learning tool for automated prediction engineering. It allows you to easily structure prediction problems and generate labels for supervised learning.

LAMA - automatic model creation framework

It is a complete project of voiceprint recognition or speaker recognition.

Library for Semi-Automated Data Science

Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Now updated with Dask to handle millions of rows.

Pytorch implementation of Meta-Learning for Short Utterance Speaker Recognition with Imbalance Length Pairs (Interspeech, 2020)

neural network based speaker embedder

Distributed DAG (Directed acyclic graph) framework for machine learning with UI

Speaker recognition library based on MARF for raspberry pi and other SBCs.

Linear Prediction Model with Automated Feature Engineering and Selection Capabilities

TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library for building modular, reusable, strongly typed machine learning workflows on Apache Spark with minimal hand-tuning

⇨ The Speaker Recognition System consists of two phases, Feature Extraction and Recognition. ⇨ In the Extraction phase, the Speaker's voice is recorded and typical number of features are extracted to form a model. ⇨ During the Recognition phase, a speech sample is compared against a previously created voice print stored in the database. ⇨ The hi…

speaker recognition using keras

Kaldi based speaker verification

Pytorch implementation of Generalized End-to-End Loss for speaker verification

A specially designed light version of Fast AutoAugment

An automatic ML model optimization tool.

This repo contains my attempt to create a Speaker Recognition and Verification system using SideKit-1.3.1

A simple, extensible library for developing AutoML systems