🐸STT integration examples

Overview

🐸 STT 0.9.x Examples

These are various examples on how to use or integrate 🐸 STT using our packages.

It is a good way to just try out 🐸 STT before learning how it works in detail, as well as a source of inspiration for ways you can integrate it into your application or solve common tasks like voice activity detection (VAD) or microphone streaming.

Contributions are welcome!

Note: These examples target 🐸 STT 0.9.x only. If you're using a different release, you need to go to the corresponding branch for the release:

List of examples

Python:

JavaScript:

Windows/C#:

Java/Android:

Nim:

Help + Community

  1. Github Discussions - find and join open-ended community discussions related to 🐸 STT-examples
  2. Matrix Chat - the 🐸 Matrix channel where you can chat with folks about 🐸 STT-examples, get help, share ideas :)
  3. Github Issues - If you ran into a specific error/problem, feel free to open an issue in the repo.
Owner
coqui
Coqui, a startup providing open speech tech for everyone 🐸
coqui
Re-implementation of the vector capsule with dynamic routing

VectorCapsule Re-implementation of the vector capsule with dynamic routing We implement the vector capsule and dynamic routing via graph neural networ

ZhenchaoTang 10 Feb 10, 2022
"SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Image", Dejia Xu, Yifan Jiang, Peihao Wang, Zhiwen Fan, Humphrey Shi, Zhangyang Wang

SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Image [Paper] [Website] Pipeline Code Environment pip install -r requirements

VITA 250 Jan 05, 2023
Unsupervised Representation Learning by Invariance Propagation

Unsupervised Learning by Invariance Propagation This repository is the official implementation of Unsupervised Learning by Invariance Propagation. Pre

FengWang 15 Jul 06, 2022
Contrastively Disentangled Sequential Variational Audoencoder

Contrastively Disentangled Sequential Variational Audoencoder (C-DSVAE) Overview This is the implementation for our C-DSVAE, a novel self-supervised d

Junwen Bai 35 Dec 24, 2022
Satellite labelling tool for manual labelling of storm top features such as overshooting tops, above-anvil plumes, cold U/Vs, rings etc.

Satellite labelling tool About this app A tool for manual labelling of storm top features such as overshooting tops, above-anvil plumes, cold U/Vs, ri

Czech Hydrometeorological Institute - Satellite Department 10 Sep 14, 2022
Minimal PyTorch implementation of Generative Latent Optimization from the paper "Optimizing the Latent Space of Generative Networks"

Minimal PyTorch implementation of Generative Latent Optimization This is a reimplementation of the paper Piotr Bojanowski, Armand Joulin, David Lopez-

Thomas Neumann 117 Nov 27, 2022
This Deep Learning Model Predicts that from which disease you are suffering.

Deep-Learning-Project This Deep Learning Model Predicts that from which disease you are suffering. This Project Covers the Topics of Deep Learning Int

Jai Viral Doshi 0 Jan 20, 2022
Distributional Sliced-Wasserstein distance code

Distributional Sliced Wasserstein distance This is a pytorch implementation of the paper "Distributional Sliced-Wasserstein and Applications to Genera

VinAI Research 39 Jan 01, 2023
GeneralOCR is open source Optical Character Recognition based on PyTorch.

Introduction GeneralOCR is open source Optical Character Recognition based on PyTorch. It makes a fidelity and useful tool to implement SOTA models on

57 Dec 29, 2022
This repo will contain code to reproduce and build upon understanding transfer learning

What is being transferred in transfer learning? This repo contains the code for the following paper: Behnam Neyshabur*, Hanie Sedghi*, Chiyuan Zhang*.

4 Jun 16, 2021
All course materials for the Zero to Mastery Machine Learning and Data Science course.

Zero to Mastery Machine Learning Welcome! This repository contains all of the code, notebooks, images and other materials related to the Zero to Maste

Daniel Bourke 1.6k Jan 08, 2023
This is the official implement of paper "ActionCLIP: A New Paradigm for Action Recognition"

This is an official pytorch implementation of ActionCLIP: A New Paradigm for Video Action Recognition [arXiv] Overview Content Prerequisites Data Prep

268 Jan 09, 2023
A colab notebook for training Stylegan2-ada on colab, transfer learning onto your own dataset.

Stylegan2-Ada-Google-Colab-Starter-Notebook A no thrills colab notebook for training Stylegan2-ada on colab. transfer learning onto your own dataset h

Harnick Khera 66 Dec 16, 2022
MemStream: Memory-Based Anomaly Detection in Multi-Aspect Streams with Concept Drift

MemStream Implementation of MemStream: Memory-Based Anomaly Detection in Multi-Aspect Streams with Concept Drift . Siddharth Bhatia, Arjit Jain, Shivi

Stream-AD 61 Dec 02, 2022
Facial expression detector

A tensorflow convolutional neural network model to detect facial expressions.

Carlos Tardón Rubio 5 Apr 20, 2022
Pytorch Implementation of Various Point Transformers

Pytorch Implementation of Various Point Transformers Recently, various methods applied transformers to point clouds: PCT: Point Cloud Transformer (Men

Neil You 434 Dec 30, 2022
Hyperparameters tuning and features selection are two common steps in every machine learning pipeline.

shap-hypetune A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models. Overview Hyperparameters t

Marco Cerliani 422 Jan 08, 2023
Unofficial pytorch implementation for Self-critical Sequence Training for Image Captioning. and others.

An Image Captioning codebase This is a codebase for image captioning research. It supports: Self critical training from Self-critical Sequence Trainin

Ruotian(RT) Luo 906 Jan 03, 2023
ONNX Command-Line Toolbox

ONNX Command Line Toolbox Aims to improve your experience of investigating ONNX models. Use it like onnx infershape /path/to/model.onnx. (See the usag

黎明灰烬 (王振华 Zhenhua WANG) 23 Nov 13, 2022
Original Implementation of Prompt Tuning from Lester, et al, 2021

Prompt Tuning This is the code to reproduce the experiments from the EMNLP 2021 paper "The Power of Scale for Parameter-Efficient Prompt Tuning" (Lest

Google Research 282 Dec 28, 2022