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>
Authentication
Use subscription key authentication (DefaultAzureCredential is not supported for this client):
import os
from azure.ai.transcription import TranscriptionClient
with TranscriptionClient(
endpoint=os.environ["TRANSCRIPTION_ENDPOINT"],
credential=os.environ["TRANSCRIPTION_KEY"],
) as client:
transcriptions = list(client.list_transcriptions())
Transcription (Batch)
import os
from azure.ai.transcription import TranscriptionClient
with TranscriptionClient(
endpoint=os.environ["TRANSCRIPTION_ENDPOINT"],
credential=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.ai.transcription import TranscriptionClient
with TranscriptionClient(
endpoint=os.environ["TRANSCRIPTION_ENDPOINT"],
credential=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
---
name: azure-ai-transcription-py
description: Transcribe audio to text using Azure AI Transcription SDK with real-time and batch support, including timestamps and diarization.
license: MIT
---
# Azure AI Transcription SDK for Python
Client library for Azure AI Transcription (speech-to-text) with real-time and batch transcription.
## Installation
```bash
pip install azure-ai-transcription
```
## Environment Variables
```bash
TRANSCRIPTION_ENDPOINT=https://<resource>.cognitiveservices.azure.com
TRANSCRIPTION_KEY=<your-key>
```
## Authentication
Use subscription key authentication (DefaultAzureCredential is not supported for this client):
```python
import os
from azure.ai.transcription import TranscriptionClient
with TranscriptionClient(
endpoint=os.environ["TRANSCRIPTION_ENDPOINT"],
credential=os.environ["TRANSCRIPTION_KEY"],
) as client:
transcriptions = list(client.list_transcriptions())
```
## Transcription (Batch)
```python
import os
from azure.ai.transcription import TranscriptionClient
with TranscriptionClient(
endpoint=os.environ["TRANSCRIPTION_ENDPOINT"],
credential=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)
```python
import os
from azure.ai.transcription import TranscriptionClient
with TranscriptionClient(
endpoint=os.environ["TRANSCRIPTION_ENDPOINT"],
credential=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