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Antigravity-awesome-skills azure-ai-transcription-py

Azure AI Transcription SDK for Python. Use for real-time and batch speech-to-text transcription with timestamps and diarization.

install
source · Clone the upstream repo
git clone https://github.com/sickn33/antigravity-awesome-skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/sickn33/antigravity-awesome-skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/plugins/antigravity-awesome-skills-claude/skills/azure-ai-transcription-py" ~/.claude/skills/sickn33-antigravity-awesome-skills-azure-ai-transcription-py && rm -rf "$T"
manifest: plugins/antigravity-awesome-skills-claude/skills/azure-ai-transcription-py/SKILL.md
tags
#speech-to-text#azure-sdk#transcription#real-time#batch-processing
safety · automated scan (low risk)
This is a pattern-based risk scan, not a security review. Our crawler flagged:
  • pip install
Always read a skill's source content before installing. Patterns alone don't mean the skill is malicious — but they warrant attention.
source content

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

client = TranscriptionClient(
    endpoint=os.environ["TRANSCRIPTION_ENDPOINT"],
    credential=os.environ["TRANSCRIPTION_KEY"]
)

Transcription (Batch)

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)

stream = client.begin_stream_transcription(locale="en-US")
stream.send_audio_file("audio.wav")
for event in stream:
    print(event.text)

Best Practices

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

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.