I use Deepgram as the STT layer in my clinical notes app, and it handles real-time transcription of therapy sessions really well. I like its fidelity and accuracy at transcribing, and its fast processing speed. It's very good, with low latency, especially for transcribing. The initial setup was very easy and simple, and I have no complaints there.

I use Deepgram for building voice-enabled applications and handling speech-to-text in real time. I like that Deepgram has high accuracy for real-time speech-to-text. Smart formatting is great as it makes the output ready to use without extra processing, and keyword boosting is valuable for domain-specific use cases. Deepgram converts audio into text quickly and enables voice features without a complex speech recognition system. The initial setup is easy, straightforward, and based on a simple API key.

I use Deepgram for accurate and fast speech-to-text transcription in my application, which saves time by quickly converting audio into accurate text, reducing manual transcription effort. I like its accuracy and fast processing and how easy the API integration is. Its real-time transcription and reliable performance make it very efficient to use. The initial setup was very easy and quick to implement, and using it with tools like Python makes my workflow seamless. Deepgram's speed and cost efficiency are much better, which is why we switched from other tools.