Best Speech Recognition Software for Unremot

Symbl is an API platform for developers and businesses to rapidly deploy conversational intelligence at scale – on any channel of communication. Our comprehensive suite of APIs unlock proprietary machine learning algorithms that can ingest any form of conversation data to identify actionable insights across domains and channels (voice, email, chat, social) contextually – without the need for any upfront training data, wake words, or custom classifiers. Symbl is democratizing conversational tech to make collaboration effortless at scale. We provide the technology for organizations to deploy at scale our proprietary workplace productivity API so brands can optimize key workflows for knowledge workers or enhance the customer experience. Whether you are a seasoned developer or just starting to explore how to harness employee collaboration to fit your organization’s needs, our API can be customized for your specific applications.

Deploy accurate speech recognition at scale while continuously improving model performance by labeling data and training from a single console. We deliver state-of-the-art speech recognition and understanding at scale. We do it by providing cutting-edge model training and data-labeling alongside flexible deployment options. Our platform recognizes multiple languages, accents, and words, dynamically tuning to the needs of your business with every training session. The fastest, most accurate, most reliable, most scalable speech transcription, with understanding — rebuilt just for enterprise. We’ve reinvented ASR with 100% deep learning that allows companies to continuously improve accuracy. Stop waiting for the big tech players to improve their software and forcing your developers to manually boost accuracy with keywords in every API call. Start training your speech model and reaping the benefits in weeks, not months or years.

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.