Best Speech Recognition Software for OpenAI
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Compare the Top Speech Recognition Software that integrates with OpenAI as of June 2025
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This a list of Speech Recognition software that integrates with OpenAI. Use the filters on the left to add additional filters for products that have integrations with OpenAI. View the products that work with OpenAI in the table below.
What is Speech Recognition Software for OpenAI?
Speech recognition software uses artificial intelligence to interpret and recognize human speech. It is used in a variety of applications, such as transcription services, voice command systems, and automated customer service programs. The technology works by analyzing input sound waves and mapping them to a database of known words or phrases to generate an output. Compare and read user reviews of the best Speech Recognition software for OpenAI currently available using the table below. This list is updated regularly.
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Line 21
Line 21
Line 21 provides AI-powered live captions and subtitles, ensuring seamless accessibility for live events, streaming platforms, and digital content. Our hybrid approach combines AI automation with human expertise, delivering high-accuracy captions that adapt to industry-specific terminology, accents, and niche references. By leveraging our AI Proofreader, we enhance real-time captions, reducing errors and making live experiences more inclusive and engaging. Our solution is designed for event organizers, broadcasters, and language service providers who need scalable, cost-effective, and high-quality captions. Traditional human captioning is expensive and non-scalable, while ASR solutions often lack accuracy. Line 21 bridges this gap by offering real-time AI-enhanced captions that integrate seamlessly into event tech and streaming workflows.Starting Price: $0.09/min -
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Whisper
OpenAI
We’ve trained and are open-sourcing a neural net called Whisper that approaches human-level robustness and accuracy in English speech recognition. Whisper is an automatic speech recognition (ASR) system trained on 680,000 hours of multilingual and multitask supervised data collected from the web. We show that the use of such a large and diverse dataset leads to improved robustness to accents, background noise, and technical language. Moreover, it enables transcription in multiple languages, as well as translation from those languages into English. We are open-sourcing models and inference code to serve as a foundation for building useful applications and for further research on robust speech processing. The Whisper architecture is a simple end-to-end approach, implemented as an encoder-decoder Transformer. Input audio is split into 30-second chunks, converted into a log-Mel spectrogram, and then passed into an encoder.
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