Amazon Transcribe is an automatic speech recognition (ASR) service that makes it easy for developers to add speech-to-text capability to their applications. Using the Amazon Transcribe API, you can analyze audio files stored in Amazon S3 and have the service return a text file of the transcribed speech.
Amazon Transcribe can be used for lots of common applications, including the transcription of customer service calls and generating subtitles on audio and video content. The service can transcribe audio files stored in common formats, like WAV and MP3, with time stamps for every word so that you can easily locate the audio in the original source by searching for the text. Amazon Transcribe is continually learning and improving to keep pace with the evolution of language.
The Amazon Transcribe API makes it easy to convert speech to text. No complicated programming is required. Just call the API with a few lines of code, and Amazon Transcribe will return the text from your audio file stored in Amazon S3.
Support for a Wide Range of Use Cases
Amazon Transcribe is designed to provide accurate and automated transcripts for a wide range of audio quality. You can generate subtitles for any video or audio files, and even transcribe low quality telephony recordings such as customer service calls.
Most speech recognition systems output a string of text without punctuation. Amazon Transcribe uses deep learning to add punctuation and formatting automatically, so that the output is more intelligible and can be used without any further editing.
Amazon Transcribe gives you the ability to expand and customize the speech recognition vocabulary. You can add new words to the base vocabulary and generate highly-accurate transcriptions specific to your use case, such as product names, domain-specific terminology, or names of individuals.
Amazon Transcribe returns a timestamp for each word, so that you can easily locate the audio in the original recording by searching for the text.
Recognize Multiple Speakers
Amazon Transcribe is able to recognize when the speaker changes and attribute the transcribed text appropriately. This can significantly reduce the amount of work needed to transcribe audio with multiple speakers like telephone calls, meetings, and television shows.