We briefly outline the steps of the workflow as follows: The following diagram presents the solution architecture. To complete this use case, use the notebook A2I-Video-Transcription-with-Amazon-Transcribe.ipynb on the Amazon A2I Sample Jupyter Notebook GitHub repo.
Amazon transcribe how to#
We measure the word error rate (WER) of transcriptions and number of correctly-transcribed terms to demonstrate how to use custom vocabularies to improve transcription of domain-specific terms in Amazon Transcribe. This post shows you how to use Amazon Augmented AI (Amazon A2I) to help generate this list of domain-specific terms by sending low-confidence predictions from Amazon Transcribe to humans for review. To use this feature, you create a list of domain-specific terms and reference that vocabulary file when running transcription jobs. In the case of transcribing domain-specific terminologies in fields such as legal, financial, construction, higher education, or engineering, the custom vocabularies feature can improve transcription quality. Powered by machine learning (ML), Amazon Transcribe is a speech-to-text service that delivers high-quality, low-cost, and timely transcripts for business use cases and developer applications.
Traditional speech-to-text transcription methods typically involve manual, time-consuming, and expensive human labor. As the volume of multimedia content generated by these activities proliferates, businesses are demanding high-quality transcripts of video and audio to organize files, enable text queries, and improve accessibility to audiences who are deaf or hard of hearing ( 466 million with disabling hearing loss worldwide) or language learners ( 1.5 billion English language learners worldwide). Businesses and organizations are increasingly using video and audio content for a variety of functions, such as advertising, customer service, media post-production, employee training, and education.
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