Azure AI Transcription Py

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

• 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.
• 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.
• Enable diarization when multiple speakers are present
• Use batch transcription for long files stored in blob storage
• Capture timestamps for subtitle generation
• Specify language to improve recognition accuracy
• Handle streaming backpressure for real-time transcription
• Close transcription sessions when complete

Reference Files

| File | Contents |

|------|----------|

| references/capabilities.md | Additional non-hero capabilities, operation-group coverage, and production checklists. |

| references/non-hero-scenarios.md | Dedicated non-hero examples for secondary/advanced scenarios. |