As we know, the meaning of a whisper is “A soft or confidential tone of voice.” The huge hype of the ChatGPT and Dalle in the past year has taken all the other OpenAI releases out of the spotlight. I am sure you will agree to this. However, OpenAI Whisper stans out. It is an automatic speech recognition system that can transcribe any audio file in around 100 languages of the world and, if needed, translate it into English. Isn’t it amazing? Want to know more about OpenAI Whisper? Keep reading to know more.
What is ASR (Automated Speech Recognition)?
Folks, I am sure you must have heard about this term, but do you know what it means? Let me tell you its meaning. It refers to the task, which is an expected algorithm or system that can, first, differentiate human speech from the background noise and the other sounds and, secondly, generate the corresponding text for the recognized speech. This task can further be divided into online and offline ASR, depending on the use case and the available resources.
Many use cases are available for online ASR systems. They can be any real-time speech-to-text, including generating subtitles on the fly for live streams. It automatically generates the protocol of the judicial process in the court along with assistance in the contact centers and content moderation, among many others.
Along with this, it can also be used in the part of some pipelines for accomplishing even more sophisticated tasks. You can imagine this scenario where you will have a platform that will first apply an ASR technology to your voice input, and then it will feed the resulting text into the ChatGPT. The outcome will be that the users could quickly ask the questions to the ChatGPT even without typing. Pretty cool, Right? You can also proceed further and be able to add a voice generation model at the end of the pipeline this would take the ChatGPT’s answer, and it will generate a speech that the users would like to hear.
Benefits of ASR Technology:
Well, the benefit of this technology would be that the users will hold a close conversation with the ChatGPT, and it will be allowed to be used as the most modern voice assistant. However, for such use cases, there is a need for the big requirement that the ASR technology will work in real-time. And hence it should be computationally as lightweight as possible while maintaining high transcription accuracy.
On the other hand, Offline ASR does not pose this requirement of strong speed on the system. Therefore, they are generally more accurate and will also be bulkier. A speech-to-text task will not require real-time performance is a use case for this type of system. This includes voice search, song lyrics extraction, subtitle generation for video files, etc.
Therefore, we must understand that openai whisper is mostly an offline automated speech recognition technology; however, with a sufficiently powerful Graphics Processing Unit(GPU), it can achieve real-time performance if it is not applied to Eminem’s “Rap God.”
The Whisper:
The progress was made in the direction of end-to-end training; the blocker which kicked in was the lack of labeled speech recognition data. The most hyped AI models which we have seen in the previous year are Dall-E 2 and ChatGPT, and they were trained on large quantities of data.
In this matter, the ChatGPT was so huge that they used around 1000 GPUs for the training process, and the whole thing cost them around 4.8 million US dollars. In the case of computer vision and natural language processing, these big databases exist in the area of speech recognition, researchers were not considered to be that lucky.
For addressing this issue, the OpenAI Whisper had been proposed, and there was also an argument that the public database will not be sufficient for the needs of the automated speech recognition models because the researchers usually target a single task in a single language.
OpenAI also proposed to perform multitask learning where the same model will be learning to do multiple tasks, and these tasks will be typically comprised of non-English transcription, speech detection, English Transcription, and any other language-to-English translation.
The whisper open ai will be adding a similar database as its previous automatic speech recognition model, and also it will be in multiple languages.
How to Use the Open AI Whisper:
Folks, you must have at least a basic understanding of Python because the model’s codes are only shared publicly, and this narrows down the possible users to people who have a basic understanding of Python.
When we consider the trend of making Artificial Intelligence models as easy to use as possible, and this could be applied to anyone, however, one will not be waiting long until the speech recognition technology is deployed somewhere with an easy-to-use user interface and experience.
Do you folks know about the Whisper Notebook?
It can run on Google Colab entirely, and this feature makes it usable for people who are from a non-tech background. For employing the model to the exact use case, you will be needing to just set some variables in the notebook and it should be in clearly indicated spaces. The beauty of this notebook lies in its feature that all the coding parts are done in the notebook cells, and there is also no need for the users to have any coding skills, Instead, they can also just run the cells sequentially for satisfying their speech-to-text use case.
Conclusion:
That’s all, folks. I hope the article helped you in getting all the information you needed.
