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The Deepgram API covers the languages we need (and then some), integrates easily with our audio source, is accurate enough, and delivers results quickly. The documentation made it easy to design our code, and the very helpful support engineers were quick to respond to questions and to help us debug our initial efforts. Of the available engines, we chose Deepgram because it was very good at transcribing our phone calls and integrated easily into our chosen Java back-end environment. As an added benefit, Deepgram was willing to create a custom model for us. Deepgram would have been the best choice for us, even if it wasn’t cheaper than the closest alternatives.

As a software developer, there is plenty to like about Deepgram – complete and easy to follow documentation; easy to use API that allows for quick language-independent implementation; great follow-up support; multiple models including one specifically for telephone-based dictation; not only one of the best but also one of the least expensive speech rec services available; a generous free number of credits are provided at sign-up – plenty enough for experimentation and testing of your application

When evaluating Deepgram’s Speech-To-Text service for a conversational agent, their service provided top-level recognition rates, competitive latencies and a set of features perfectly suited for our use cases such as keyword spotting or punctuation imputation. Best of all, evaluation was incredibly easy due to their excellent developer site with code samples, playgrounds and generous evaluation credits. Deepgram also supported us in finetuning their solution for our product and continually communicated with us regularly throughout the process.

I have been using Deepgram’s API for a couple of months now, and I am beyond impressed with the accuracy. It is so much better than other voice recognition services that I have tried in the past. I love that it supports so many languages, which is perfect for me because I work with clients worldwide. The best part is that its API is pretty intuitive, which means it doesn’t require any training, which saves me tons of time. I would recommend this to anyone who needs a speech-to-text service!

We provide Fraud Detection services to the insurance industry using intelligent and compliant AI-driven Digital Speech DNA solutions over Blockchain. Using Deepgram allowed us to process a large volume of data quickly and accurately. In addition, Deepgram has the ability to detect different accents which improved the overall accuracy of our scoring module.

The low latency of the response with high accuracy from the websocket connection is the most distinguishing feature from other providers. If this feature was not there then it’s yet another Speech to Text service. I really love the community around it and the team which is driving it, kudos to the DevRel team.

We have tested a number of transcription APIs, and Deepgram has consistently come out as the most accurate for our use case. whilst offering a nice Python interface for batch operations. The API schemas are also excellent.

My favorite part about using Deepgram was the ease of learning. The API documentation is complete and intuitive, and the tutorials in the console left me feeling confident that I could use the API and SDK in either Node or Python projects.