Best Speech to Text Software for Spark NLP
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Compare the Top Speech to Text Software that integrates with Spark NLP as of November 2025
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This a list of Speech to Text software that integrates with Spark NLP. Use the filters on the left to add additional filters for products that have integrations with Spark NLP. View the products that work with Spark NLP in the table below.
What is Speech to Text Software for Spark NLP?
Speech-to-text software is software that converts spoken language into written text, allowing users to dictate instead of typing. These platforms typically use speech recognition algorithms and natural language processing (NLP) to transcribe spoken words into accurate text in real time. Speech-to-text software is commonly used in various industries for tasks such as transcription, note-taking, dictation, and accessibility. It can be integrated with other tools like word processors, customer service software, and medical or legal documentation systems. Many of these tools also offer features like punctuation insertion, voice commands, speaker identification, and multi-language support to enhance transcription accuracy and productivity. Compare and read user reviews of the best Speech to Text software for Spark NLP currently available using the table below. This list is updated regularly.
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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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