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1. #Azure speech to text phraselistgrammar node js how to#
2. #Azure speech to text phraselistgrammar node js free#

Rev works in 31 different languages, including diverse options such as German, French, Spanish, Russian, Japanese, Chinese, Korean, Arabic, and Turkish.Īzure performs similarly with support for 44 total languages for speech-to-text use cases, though that number drops to 30 languages for translating speech in real-time. If you want to serve an international customer base or if you’re building anything involving translation, supporting multiple languages is essential. Rev AI, on the other hand, promises support for 8 English speakers or 6 non-English speakers.īoth solutions can identify speakers equally well. Microsoft claims their tech supports diarization, but they don’t ever say how many speakers it can handle. Identifying who is talking and when is a key feature for high performance ASR systems.

#Azure speech to text phraselistgrammar node js free#

Try Rev AI Free Speaker ID and Diarization Winner: Rev AI The reason that Rev’s AI outperforms others is because our network of over 60,000 human transcriptionists contribute data that we use to constantly improve our models. In our podcast transcription benchmarks, we compared Rev AI to Microsoft’s ASR for 30 podcasts and found that Rev’s WER, 14.22%, is about 2% lower than Microsoft’s, which came in at 16.51%. A 20% WER, for instance, means that it got 20% of the words wrong. The gold standard for accuracy benchmarking is word error rate (WER), which measures how many words the ASR tech deletes, inserts, or substitutes as an overall percentage. After all, if the ASR engine messes up too many words, using it will be difficult at best and impossible at worst. Accuracy Winner: Rev AIīy far the most important point of comparison is accuracy. No matter what, you want to get a holistic picture of each technology and how they stack up against each other. Depending on your unique needs and uses, some will be more important than others.

If you’re picking between Azure Cognitive Services and Rev.ai for your project, you want to compare these solutions along several metrics. There’s nothing that turns users off more than the frustration of trying to talk to a device that just can’t understand them, no matter how hard they try. endpoints/base/:test operation (includes ':') in version 3.1.Whether you have a great idea for the next Internet of Things (IoT) device, you want to add live-captioning to your media streaming service, or you’re creating a hands-free voice user interface for a mobile application, you’re going to need an automatic speech recognition (ASR) solution that’s up for the job.

This table includes all the operations that you can perform on endpoints.

#Azure speech to text phraselistgrammar node js how to#

See Deploy a model for examples of how to manage deployment endpoints. You must deploy a custom endpoint to use a Custom Speech model. PathĮndpoints are applicable for Custom Speech. This table includes all the operations that you can perform on datasets. See Upload training and testing datasets for examples of how to upload datasets. For example, you can compare the performance of a model trained with a specific dataset to the performance of a model trained with a different dataset. You can use datasets to train and test the performance of different models. You can register your webhooks where notifications are sent.ĭatasets are applicable for Custom Speech.