I use Deepgram for audio transcriptions and speech recognition. I am working on a feedback survey app where users provide verbal feedback that Deepgram transcribes into text. 

We receive the results and implement features like punctuation and Smart Format.

Deepgram has significantly improved our transcription process in terms of speed and accuracy. It has allowed us to efficiently convert verbal feedback into text, enabling quicker analysis and implementation of new features. 

Integrating Deepgram has streamlined our workflow, enhancing productivity and delivering more accurate transcription results.

We previously used IBM Watson, which was slow and had limitations in accurately transcribing words. After evaluating OpenAI's Whisper model, we discovered Deepgram, which incorporates Whisper and adds the powerful Nova model.

Deepgram's latency is impressively low, around 0.5 to 1 second, making it a superior choice.

Live transcription could be improved. Sometimes, Deepgram's WebSocket is disposed of due to redundancy issues. Enhanced stability in live transcription would be beneficial.

I have been using Deepgram for one and a half years.

Initially, we encountered some stability issues, but Deepgram has since improved its architecture. With the addition of hooks for status updates, the accuracy has improved to approximately 90 to 95%, which is better than other models we've tested.

It's scalable. Our platform handles 50 to 60 users simultaneously without compromising accuracy. For instance, a 20-minute audio file was transcribed within a second, demonstrating its ability to handle large volumes of audio data effectively.

My experience with customer service and support has been positive. They are responsive and helpful, and they provide timely resolutions to any issues.

Positive

We previously used IBM Watson, but it didn't deliver appropriate results. We searched for alternatives and found OpenAI's Whisper model, which was initially slow. After thorough analysis, we discovered Deepgram. It proved to be superior, leading to our decision to migrate. We used a detailed spreadsheet to compare various models before making the switch.

Thanks to clear documentation, the initial setup was very easy. If you have prerequisite knowledge of the programming language you're using, it’s straightforward to follow the documentation and implement it into your system. When I started, I closely followed the documentation, which made the process very manageable.

Deployment model: We last deployed it on the Google Cloud Platform (GCP).

The implementation was done in-house.

Our ROI has increased due to enhanced transcription accuracy and speed, leading to more efficient workflows and better user satisfaction.

The pricing is moderate. While live transcription may incur some charges when the connection is open, they become minimal over time. So, it's a balanced option—neither cheap nor overly expensive.

Yes, besides IBM Watson, we evaluated OpenAI's Whisper model.

Deepgram is highly recommended. Users don’t need to do anything special before using it, as the documentation is comprehensive. I am a Node.js developer and have used Deepgram packages for Node.js. Understanding your programming language is key, whether it's Node.js, Python, or others.

AI Features:

I have integrated various AI models into our application. Deepgram's sentiment analysis feature allows us to create graphs and analyses to determine if words are positive, negative, or neutral. This helps us summarize feedback and derive actionable insights.

My ratings:

I would rate it an eight out of ten. The live transcription feature needs improvement as the WebSocket sometimes gives errors or breaks down during live streams.

We use the solution for TTS (Text-to-Speech) and STT (Speech-to-Text) purposes.

The solution's Speech-to-Text conversion feature is really awesome.

Deepgram is currently restricted to only the English variants, but it should include other languages, such as German or French.

I have been using Deepgram for five to six months.

Deepgram is a stable solution.

The Deepgram cloud can handle large volumes of audio data. Around three to four people use the solution in our organization.

The solution’s initial setup is easy.

Deepgram is a cheap solution. We can create an account for $200, which we can initially use for the Deepgram services.

I have used Deepgram with Twilio for the calling system. I would recommend Deepgram to users who want to use it for speech-to-text purposes.

Overall, I rate the solution an eight out of ten.