In recent years, within the world of Artificial Intelligence, one of the most popular applications is Speech recognition. This popularity is due to the huge diversity of applications and needs : call center, broadcasting, traduction, health care, banking, voice assistant, etc.
Speech recognition includes various functionalities :
This list does not represent an exhaustive list of all speech recognition functionalities. Many solutions are based on several functionalities combined.
Open source engines are available for free, you can often find those solutions on github. You just need to download the library and use these engines directly from your machine. On the contrary, speech-to-text cloud engines are provided by AI providers, they are selling you requests that you can process via their APIs. They can sell requests with a license model (you pay a monthly subscription corresponding to a certain amount of requests) or a pay-per-use model (you pay only for requests you send).
When you are looking for a speech-to-text engine, the first question you need to ask you is: which kind of engine am I going to choose?
Of course, the main advantage of open source speech-to-text engines is that they are open source. It means that this is free to use and you can use the code in the way you want. It allows you to potentially modify the source code, hyperparameterize the model. Moreover, you will have no trouble with data privacy because you will have to host the engine with your own server, which also means that you will need to set up this server, maintain it and insure you that you will have enough computing power to handle all the requests.
On the other hand, cloud speech-to-text engines are paying but the AI provider will handle the server for you, maintain and improve the model. In this case, you have to accept that your data will transit to the provider cloud. In exchange, the provider is processing millions of data to provide a very performant engine. The speech-to-text provider also has servers that can support millions of requests per second without losing performance or rapidity.
Now that you know the pros and cons of open source and cloud engines, please consider that there is a third option: build your own speech-to-text engine. With this option, you can build the engine based on your own data which guarantees you good performance. You will also be able to keep your data safe and private. However, you will have the same constraint of hosting your engine. Of course, this option can be considered only if you have data science abilities in your company. Here is a summary of when to choose between using existing engines (cloud or open source) and build your own one:
There are multiple open source speech-to-text engines available, you can find the majority on github. Here are the most famous ones:
Then you can create a model instance and load model:
Finally, you can perform predictions:
Vosk is an offline open source speech recognition toolkit. It enables speech recognition for 20+ languages and dialects - English, Indian English, German, French, Spanish, Portuguese, Chinese, Russian, Turkish, Vietnamese, Italian, Dutch, Catalan, Arabic, Greek, Farsi, Filipino, Ukrainian, Kazakh, Swedish, Japanese, Esperanto. You can follow the installation tutorial here.
There are many cloud speech-to-text engines on the market and you will have issues choosing the right one. Here are the best providers of the market:
All those speech-to-text providers can provide you good performance for your project. Depending on the language (and accent), the quality, the length, the size of your audios, the best engine can vary between all these providers. The only way to know which provider to choose is to compare the performance with your own data (audios).
By using Eden AI, you will be able to compare all the providers with your data, change the provider whenever you want and call multiple providers at the same time. You will pay the same price per request as if you had subscribed directly to the providers APIs and you will not lose latency performance.
If you want to call another provider, you just need to change the value of the parameter “providers”. You can see all providers available in Eden AI documentation. Of course, you can call multiple providers in the same request in order to compare or combine them. Moreover, Eden AI allows you to use asynchronous speech-to-text for providers that offer this functionality. It avoids you from waiting for the result of the request. You can also access this functionality with some open source engines.
In this article, we explain how the mapping between the input language and the languages supported by the providers is performed to facilitate access to one of our AI engines.