spokestack-ios VS Serrano

Compare spokestack-ios vs Serrano and see what are their differences.

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spokestack-ios Serrano
1 0
27 50
- -
2.2 0.0
over 1 year ago over 4 years ago
Swift Swift
Apache License 2.0 MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

spokestack-ios

Posts with mentions or reviews of spokestack-ios. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning spokestack-ios yet.
Tracking mentions began in Dec 2020.

Serrano

Posts with mentions or reviews of Serrano. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning Serrano yet.
Tracking mentions began in Dec 2020.

What are some alternatives?

When comparing spokestack-ios and Serrano you can also consider the following projects:

SwiftSpeech - A speech recognition framework designed for SwiftUI.

CoreML-Models - Largest list of models for Core ML (for iOS 11+)

CoreML-samples - Sample code for Core ML using ResNet50 provided by Apple and a custom model generated by coremltools.

Tensorflow-iOS

SwiftCoreMLTools - A Swift library for creating and exporting CoreML Models in Swift

AIToolbox - A toolbox of AI modules written in Swift: Graphs/Trees, Support Vector Machines, Neural Networks, PCA, K-Means, Genetic Algorithms

DL4S - Accelerated tensor operations and dynamic neural networks based on reverse mode automatic differentiation for every device that can run Swift - from watchOS to Linux

Caffe2

Bender - Easily craft fast Neural Networks on iOS! Use TensorFlow models. Metal under the hood.