azure-ai-transcription-py

name: azure-ai-transcription-pydescription: |  Azure AI Transcription SDK for Python. Use for real-time and batch speech-to-text transcription with timestamps and diarization.  Triggers: "transcription", "speech to text", "Azure AI Transcription", "TranscriptionClient".license: MITmetadata:   author: Microsoft  version: "1.0.0"  package: azure-ai-transcription

Azure AI Transcription SDK for Python

Client library for Azure AI Transcription (speech-to-text) with real-time and batch transcription.

Installation

pip install azure-ai-transcription

Environment Variables

TRANSCRIPTION_ENDPOINT=https://<resource>.cognitiveservices.azure.com
TRANSCRIPTION_KEY=<your-key>  # For key auth; not needed when using DefaultAzureCredential/TokenCredential

Authentication & Lifecycle

🔑 Two rules apply to every code sample below:

1. Two auth modes are supported: AzureKeyCredential(os.environ["TRANSCRIPTION_KEY"]) for key-based auth, or DefaultAzureCredential() / any TokenCredential for Entra ID. Prefer DefaultAzureCredential in production; never hardcode credentials in code.
2. Wrap every client in a context manager so HTTP transports and sockets are released deterministically:
   • Sync: with <Client>(...) as client:
   • Async: async with <Client>(...) as client:

Snippets may abbreviate this setup, but production code should always follow both rules.

Use subscription key authentication:

import os
from azure.core.credentials import AzureKeyCredential
from azure.ai.transcription import TranscriptionClient

with TranscriptionClient(
    endpoint=os.environ["TRANSCRIPTION_ENDPOINT"],
    credential=AzureKeyCredential(os.environ["TRANSCRIPTION_KEY"]),
) as client:
    transcriptions = list(client.list_transcriptions())

Transcription (Batch)

import os
from azure.core.credentials import AzureKeyCredential
from azure.ai.transcription import TranscriptionClient

with TranscriptionClient(
    endpoint=os.environ["TRANSCRIPTION_ENDPOINT"],
    credential=AzureKeyCredential(os.environ["TRANSCRIPTION_KEY"]),
) as client:
    job = client.begin_transcription(
        name="meeting-transcription",
        locale="en-US",
        content_urls=["https://<storage>/audio.wav"],
        diarization_enabled=True,
    )
    result = job.result()
    print(result.status)

Transcription (Real-time)

import os
from azure.core.credentials import AzureKeyCredential
from azure.ai.transcription import TranscriptionClient

with TranscriptionClient(
    endpoint=os.environ["TRANSCRIPTION_ENDPOINT"],
    credential=AzureKeyCredential(os.environ["TRANSCRIPTION_KEY"]),
) as client:
    stream = client.begin_stream_transcription(locale="en-US")
    stream.send_audio_file("audio.wav")
    for event in stream:
        print(event.text)

Best Practices

1. Pick sync OR async and stay consistent. Do not mix azure.xxx sync clients with azure.xxx.aio async clients in the same call path. Choose one mode per module.
2. Always use context managers for clients and async credentials. Wrap every client in with Client(...) as client: (sync) or async with Client(...) as client: (async). For async DefaultAzureCredential from azure.identity.aio, also use async with credential: so tokens and transports are cleaned up.
3. Enable diarization when multiple speakers are present
4. Use batch transcription for long files stored in blob storage
5. Capture timestamps for subtitle generation
6. Specify language to improve recognition accuracy
7. Handle streaming backpressure for real-time transcription
8. Close transcription sessions when complete

Reference Files