azure-ai-transcription-py

Batch transcription of audio files in Python is performed using the TranscriptionClient, which processes large audio archives and outputs text with optional diarization and timestamps. Initialize the client with your Azure endpoint and key to start processing.

Real-time speech-to-text streaming in Python works by using the TranscriptionClient to handle asynchronous results and event handling for live audio. It enables scenarios like live meeting transcription by continuously converting streaming audio input to text.

Yes, you need an active Azure endpoint and key to authenticate the TranscriptionClient for speech-to-text. You must also install the azure-ai-transcription package via pip before setting up the client to access batch and streaming transcription features.

Real-time streaming transcription handles live audio with asynchronous event handling, while batch transcription processes pre-recorded audio files. Both methods use the TranscriptionClient to convert speech to text, but batch transcription targets large audio archives rather than live meetings.

Yes, you can process large audio archives with diarization using Python by utilizing the batch transcription capabilities of the TranscriptionClient. This identifies different speakers in the audio and provides timestamps for scalable speech processing across projects.