Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
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".
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".
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Client library for Azure AI Transcription (speech-to-text) with real-time and batch transcription.
pip install azure-ai-transcription
TRANSCRIPTION_ENDPOINT=https://<resource>.cognitiveservices.azure.com TRANSCRIPTION_KEY=<your-key>
Use subscription key authentication (DefaultAzureCredential is not supported for this client): import os from azure.ai.transcription import TranscriptionClient client = TranscriptionClient( endpoint=os.environ["TRANSCRIPTION_ENDPOINT"], credential=os.environ["TRANSCRIPTION_KEY"] )
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)
stream = client.begin_stream_transcription(locale="en-US") stream.send_audio_file("audio.wav") for event in stream: print(event.text)
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
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Largest current source with strong distribution and engagement signals.