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 ```bash pip install azure-ai-transcription ``` ## Environment Variables ```bash 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: ```python 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) ```python 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) ```python 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 | File | Contents | |------|----------| | [references/capabilities.md](references/capabilities.md) | Additional non-hero capabilities, operation-group coverage, and production checklists. | | [references/non-hero-scenarios.md](references/non-hero-scenarios.md) | Dedicated non-hero examples for secondary/advanced scenarios. |