The main use cases involve clients handling various calls day-to-day who have a quality analyzer or auditor wanting to verify what representatives spoke with specific clients. This piece of technology comes into play because the auditor cannot go and listen to long audios for call recordings that span 10 to 20 or more hours. They won't be checking individual calls, but using Google Cloud Speech-to-Text, we can easily transcribe the call with respect to who has spoken what, with specific speaker diarization. We can ask any open-source AI, or even paid AIs such as ChatGPT AI, to provide the transcription and the context of representative conversations with clients. From that, we will get a complete overview of the call in a few seconds. We can transcribe multiple calls, and if we want to check our representative's productivity per day, we can easily transcribe all the calls and get an overall understanding of what has occurred in the calls. This is the broader scope of the Google Cloud Speech-to-Text solution I developed for my client.
For development purposes, my company uses Python in the back end, like the FastAPI framework, and then we utilize the clients of Google Cloud Platform.
We need IT corporate chatbots to help new people with some things. When you are new in a company, you need a lot of things, such as access. We want to make a chat for auto consulting. You can say, "Oh, hello, I'm new, I need an account in GitHub, please, and GitLab." And we want to do the chatbot with integration with all the systems for may access another common task for users automatically in the chatbot. We want to make a chatbot to resolve common problems for people. In that way, we will save time as it will allow support to help with other problems, harder problems. You can resolve common problems with a bot and non-common problems with humans.
Google Speech-to-Text enables developers to convert audio to text by applying powerful neural network models in an easy-to-use API. The API recognizes 120 languages and variants to support your global user base. You can enable voice command-and-control, transcribe audio from call centers, and more. It can process real-time streaming or prerecorded audio, using Google’s machine learning technology.
The main use cases involve clients handling various calls day-to-day who have a quality analyzer or auditor wanting to verify what representatives spoke with specific clients. This piece of technology comes into play because the auditor cannot go and listen to long audios for call recordings that span 10 to 20 or more hours. They won't be checking individual calls, but using Google Cloud Speech-to-Text, we can easily transcribe the call with respect to who has spoken what, with specific speaker diarization. We can ask any open-source AI, or even paid AIs such as ChatGPT AI, to provide the transcription and the context of representative conversations with clients. From that, we will get a complete overview of the call in a few seconds. We can transcribe multiple calls, and if we want to check our representative's productivity per day, we can easily transcribe all the calls and get an overall understanding of what has occurred in the calls. This is the broader scope of the Google Cloud Speech-to-Text solution I developed for my client.
For development purposes, my company uses Python in the back end, like the FastAPI framework, and then we utilize the clients of Google Cloud Platform.
We need IT corporate chatbots to help new people with some things. When you are new in a company, you need a lot of things, such as access. We want to make a chat for auto consulting. You can say, "Oh, hello, I'm new, I need an account in GitHub, please, and GitLab." And we want to do the chatbot with integration with all the systems for may access another common task for users automatically in the chatbot. We want to make a chatbot to resolve common problems for people. In that way, we will save time as it will allow support to help with other problems, harder problems. You can resolve common problems with a bot and non-common problems with humans.