azure-ai-transcription-py
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".
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SKILL.md
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:
- Two auth modes are supported:
AzureKeyCredential(os.environ["TRANSCRIPTION_KEY"])for key-based auth, orDefaultAzureCredential()/ anyTokenCredentialfor Entra ID. PreferDefaultAzureCredentialin production; never hardcode credentials in code.- 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.xxxsync clients withazure.xxx.aioasync 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) orasync with Client(...) as client:(async). For asyncDefaultAzureCredentialfromazure.identity.aio, also useasync 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. |
Files
3- SKILL.md
3e2f90f3724.0 KB - references/capabilities.md
5a0810b1e61.1 KB - references/non-hero-scenarios.md
f664a134223.6 KB
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