HappyScribe vs. Keras

Happy Scribe saves us a lot of time in generating transcripts and subtitles. We have used other services, including human transcription, and really find Happy Scribe to be adequate for a starting point. Even human transcription isn't perfect when the transcriptionist isn't familiar with the domain and specific vocabulary used.

Keras is quite perfect, if the aim is to build the standard Deep Learning model, and materialize it to serve the real business use case, while it is not suitable if the purpose is for research and a lot of non-standard try out and customization are required, in that case either directly goes to low level TensorFlow API or Pytorch

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• Machine translation is remarkably accurate, though of course not perfect.
• The entire web app experience is thoughtfully designed and implemented. Using it is a pleasure.
• A sweet feature: the ability to share editable transcripts with guests through a share link.

• One of the reason to use Keras is that it is easy to use. Implementing neural network is very easy in this, with just one line of code we can add one layer in the neural network with all it's configurations.
• It provides lot of inbuilt thing like cov2d, conv2D, maxPooling layers. So it makes fast development as you don't need to write everything on your own. It comes with lot of data processing libraries in it like one hot encoder which also makes your development easy and fast.
• It also provides functionality to develop models on mobile device.

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• There's not much to dislike!

• As it is a kind of wrapper library it won't allow you to modify everything of its backend
• Unlike other deep learning libraries, it lacks a pre-defined trained model to use
• Errors thrown are not always very useful for debugging. Sometimes it is difficult to know the root cause just with the logs

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I am giving this rating depending on my experience so far with Keras, I didn't face any issue far. I would like to recommend it to the new developers.

Keras have really good support along with the strong community over the internet. So in case you stuck, It won't so hard to get out from it.

For our use, Happy Scribe's machine translation is nearly as accurate as the human translation services we used before at considerably lower cost. The web app is better designed than either of the others we tried, though we haven't used those alternatives in the past several years

Keras is good to develop deep learning models. As compared to TensorFlow, it's easy to write code in Keras. You have more power with TensorFlow but also have a high error rate because you have to configure everything by your own. And as compared to MATLAB, I will always prefer Keras as it is easy and powerful as well.

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• Happy Scribe enables us to make videos and audio accessible to people who prefer not to watch or listen.
• Happy Scribe saves us time and money.
• Happy Scribe just works.

• Easy and faster way to develop neural network.
• It would be much better if it is available in Java.
• It doesn't allow you to modify the internal things.

Incentivized

Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info