My main use case for AssemblyAI was for a voice-based calling system that we created. A specific example of how I used AssemblyAI in my voice-based calling system is that it was used for the text-to-speech part and also speech-to-text. For speech-to-text, I don't remember if we also used something else, but for text-to-speech, we definitely relied on AssemblyAI because the voices were significantly better. I don't have anything else to add about my use case with AssemblyAI.
I used AssemblyAI for a lot of agents. I used AssemblyAI when I had to control multiple agents. With AssemblyAI, I was doing research while controlling or managing multiple agents. Regarding how I used AssemblyAI for research, I was conducting multi-agent research.
Consultant at a tech vendor with 10,001+ employees
Real User
Top 10
Jun 17, 2026
I use AssemblyAI for audio transcription in multiple different languages. It has the capability of translating and transcribing into multiple different languages of both India as well as in the world. It also has good diarization capabilities, which is why I use AssemblyAI. I had a customer use case problem where I had to transcribe lots of customer support calls into transcriptions in Hindi and multiple different Indic languages, as well as in foreign languages. AssemblyAI was helpful for this purpose. AssemblyAI has been integrated into multiple different clients' use cases, and it was one of the core features in the AWS pipeline audio analytics pipeline that we created. It has benefited us significantly in saving costs of transcription.
Level 2 Software Engineer at a consultancy with 51-200 employees
Real User
Top 20
Jun 16, 2026
In my personal project, I used AssemblyAI for audio entity recognition. I gave it some audio files and AssemblyAI processed them to provide entity recognition. For example, if the audio contained names of someone, it highlighted them as person names and these types of entities. In the freelance project that I made recently, I used it for transcribing audio interviews. We were making an audio and video interviewing system and we needed an API to transcribe audio into text. AssemblyAI was used for speech-to-text translation because it was the fastest and the best option for our use case. In the audio and video project I was making for a freelance client, our use case was speed. The main thing that would differentiate us from our competitors was speed. We needed a quick solution that was also cost-effective. AssemblyAI stood out and it provided us quick results that helped us transcribe the audio stream quite instantly and use it to process and show results to the user.
My main use case for AssemblyAI is meeting and interview transcriptions. We are a culture operating system, so we track organization culture. Our bot joins the meetings of employees, and we convert the calls, interviews, or meetings into text. AssemblyAI supports our async and real-time transcription, and when we have the text, we pass it through our internal LLM to create culture scores.
I use AssemblyAI only with audio files, not for real-time transcription. I mainly use only US English, and I have not tried other languages. I upload audio files through AssemblyAI API, and they provide the transcription script with speaker identification and the dialogues.
AssemblyAI offers advanced speech recognition technology tailored for developers. Its robust API facilitates easy integration into existing systems, making it a versatile option for many applications.AssemblyAI proficiency in speech-to-text conversion is highly regarded. By leveraging state-of-the-art machine learning models, it provides reliable transcription and voice processing capabilities. Its adaptable API design supports integration across desktop, mobile, and web platforms. This...
My main use case for AssemblyAI was for a voice-based calling system that we created. A specific example of how I used AssemblyAI in my voice-based calling system is that it was used for the text-to-speech part and also speech-to-text. For speech-to-text, I don't remember if we also used something else, but for text-to-speech, we definitely relied on AssemblyAI because the voices were significantly better. I don't have anything else to add about my use case with AssemblyAI.
I used AssemblyAI for a lot of agents. I used AssemblyAI when I had to control multiple agents. With AssemblyAI, I was doing research while controlling or managing multiple agents. Regarding how I used AssemblyAI for research, I was conducting multi-agent research.
I use AssemblyAI for audio transcription in multiple different languages. It has the capability of translating and transcribing into multiple different languages of both India as well as in the world. It also has good diarization capabilities, which is why I use AssemblyAI. I had a customer use case problem where I had to transcribe lots of customer support calls into transcriptions in Hindi and multiple different Indic languages, as well as in foreign languages. AssemblyAI was helpful for this purpose. AssemblyAI has been integrated into multiple different clients' use cases, and it was one of the core features in the AWS pipeline audio analytics pipeline that we created. It has benefited us significantly in saving costs of transcription.
In my personal project, I used AssemblyAI for audio entity recognition. I gave it some audio files and AssemblyAI processed them to provide entity recognition. For example, if the audio contained names of someone, it highlighted them as person names and these types of entities. In the freelance project that I made recently, I used it for transcribing audio interviews. We were making an audio and video interviewing system and we needed an API to transcribe audio into text. AssemblyAI was used for speech-to-text translation because it was the fastest and the best option for our use case. In the audio and video project I was making for a freelance client, our use case was speed. The main thing that would differentiate us from our competitors was speed. We needed a quick solution that was also cost-effective. AssemblyAI stood out and it provided us quick results that helped us transcribe the audio stream quite instantly and use it to process and show results to the user.
My main use case for AssemblyAI is meeting and interview transcriptions. We are a culture operating system, so we track organization culture. Our bot joins the meetings of employees, and we convert the calls, interviews, or meetings into text. AssemblyAI supports our async and real-time transcription, and when we have the text, we pass it through our internal LLM to create culture scores.
I use AssemblyAI only with audio files, not for real-time transcription. I mainly use only US English, and I have not tried other languages. I upload audio files through AssemblyAI API, and they provide the transcription script with speaker identification and the dialogues.