How to Use ASR System for Accurate Transcription Properties of Your Digital Product

How to Use ASR System for Accurate Transcription Properties of Your Digital Product

In this tutorial, we'll learn how to Use ASR System for Accurate Transcription Properties of Your Digital Product. Let's explore it with us now.

Thanks to advances in speech recognition, companies can now build a whole range of products with accurate transcription capabilities at their heart. Conversation intelligence platforms, personal assistants and video and audio editing tools, for example, all rely on speech to text transcription. However, you often need to train these systems for every domain you want to transcribe, using supervised data. In practice, you need a large body of transcribed audio that’s similar to what you are transcribing just to get started in a new domain.

Recently, Facebook released wav2vec 2.0 which goes some way towards addressing this challenge. wav2vec 2.0 allows you to pre-train transcription systems using _audio only — _with no corresponding transcription — and then use just a tiny transcribed dataset for training.

In this blog, we share how we worked with wav2vec 2.0, with great results.

speech-to-text-recognition speech-recognition machine-learning artificial-intelligence python pytorch speech-recognition-in-python hackernoon-top-story

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