In this article, we show how employing AI pipeline easily allows you to solve complex use cases requiring and combining Speech-to-Text and sentiment analysis (NLP).
With AI services, you can build pipelines that solve common issues. When you need an OCR engine to detect speech in your data, you often need other engines to analyze or transcribe the speech detected. To solve this problem, you have multiple options using AI:
But you can’t be sure that the engines from your cloud provider offer the best performance, rapidity and prices. Moreover, it is possible that your cloud provider does not provide the engine you are looking for because they do not provide all AI services available on the market.
The third option is the multi-cloud strategy, which we recommend. Depending on your data (quantity, type, quality, etc.) and the technology you require (object detection, OCR invoice, explicit content detection, syntax analysis, text-to-speech, etc.), the performance rankings amongst the various suppliers will always change. Each sort of engine has a large number of providers, including large cloud providers and AI experts. Here are some instances of rankings made using various data sets:
The only method to identify the best provider is to compare the engines of many providers with your data and pick the best Speech to Text Recogniton combination of results from various providers. If cost is one of your top concerns, you can also compare prices and do so for speed. The Speech to Text engine and sentiment analysis engines that are most suited and most powerful for your data and project can be built into powerful AI pipelines using this technique.
This method is the best in terms of performance and optimization but it presents many inconveniences:
Eden AI is really helpful in this situation. Simply sign up and create an Eden AI account to gain access to the engines of numerous providers for a variety of technologies (vision, NLP, speech, OCR, translation, and prediction). You may compare and visualize the results from several engines using the platform, and you can also have a consolidated pricing for using various providers.
Here is an example of a pipeline:
Eden AI provides the same easy to use API with the same documentation for every technology. You can use Eden AI API to call Speech-to-Text, Sentiment Analysis and Translation for example, with provider as a simple parameter. With only few lines, you can set up your project in production :
The pipeline is built very easily, and Eden AI allows you to go further. Provider is a parameter that allows you to set up with 2 lines of code a fallback provider in case the first one is down. You can also combine providers' results if you can’t get the performance you are looking for with only one provider’s engine.
Here's a video showing how Eden AI works:
There are hundreds of AI engines available on the market: it's impossible to know all of them, to know those who provide good performance. Most of the time, you don’t use only one engine, you combine them as a pipeline to process your data (Speech + NLP for example). The best way to build this pipeline is the multi-cloud approach that guarantees you to reach the best performance and prices for each technology. This approach seems to be complex but it is simplified by Eden AI which centralizes best providers APIs for each technology.
You are a solution provider and want to integrate Eden AI, contact us at : firstname.lastname@example.org
This article is brought to you by the Eden AI team. We allow you to test and use in production a large number of AI engines from different providers directly through our API and platform. In this article, we expose how using AI pipeline easily allows you to solve complex use cases requiring and combining OCR and text analysis (NLP).