Local Speech-to-Text with Whisper

    1

    Generate optimized OpenAI Whisper CLI commands for local, private audio transcription and translation.

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    Works with the AI tools you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIGitHub CopilotGitHub CopilotGemini CLIGemini CLI+20 more

    Local Speech-to-Text with Whisper

    Example session with this skill installed

    I need to transcribe conference_call.mp3. I want a high accuracy transcript in srt format saved to the current directory.

    • Read your context and instructions
    • Compiled the local speech-to-text

    I have generated the command for a high-accuracy transcription of your audio file using the medium model to balance precision and performance.

    Transcription Command

    
# Ensure you have whisper installed: pip install openai-whisper
# This command uses the medium model for high accuracy and outputs srt.

whisper conference_call.mp3 \
  --model medium \
  --output_format srt \
  --output_dir . \
  --verbose False

    Configuration Details

    ParameterValueDescription
    ModelmediumHigh accuracy, requires ~5GB VRAM.
    FormatsrtSubRip Subtitle file with timestamps.
    TasktranscribeStandard speech-to-text.
    Output Dir.Current working directory.

    Next steps

    1. Run the command in your terminal where the audio file is located.
    2. If the first run is slow, check ~/.cache/whisper to ensure the model has finished downloading.
    3. If you experience memory issues, swap --model medium for --model small.

    Connects securely to your tools. The creator never sees your data.

    What you get

    Generate SRT subtitles for video files locally.Translate foreign language audio to English text.Automate batch transcription of meeting recordings.Configure model sizes for different hardware specs.

    About this skill

    The problem

    Transcribing audio files usually involves expensive API costs or sending sensitive data to third-party servers. Managing local Whisper CLI flags for different file formats and translation tasks is tedious and error-prone.

    What it does

    • Generates precise Whisper CLI commands for local audio transcription.
    • Handles language translation tasks from audio to English text.
    • Configures model selection based on your hardware constraints and accuracy requirements.
    • Specifies output formats including SRT, TXT, and VTT for various workflows.

    Frameworks & tools

    OpenAI Whisper CLI, FFmpeg, and Python.

    Why this beats prompting it yourself

    This skill eliminates the need to memorize complex CLI flags or look up model names. It ensures you use the correct syntax for local paths and task types, preventing failed runs on large audio files.

    Use cases

    • Convert podcast recordings or meetings into text transcripts locally.
    • Generate SRT subtitle files for video production workflows.
    • Translate non-English audio files directly into English text.
    • Batch process audio files using optimized model selection.

    Known limitations

    Requires Whisper CLI and FFmpeg installed on the local system. Large models require significant RAM and GPU resources.

    How to install

    Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

    ~30 seconds
    1. 1

      Download the ZIP

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    2. 2

      Unzip into your skills folder

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    3. 3

      Ask your agent to use it

      Restart the agent if it was already running. It picks the skill up automatically - no config needed.

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    Recently published to Agensi

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    Trust & safety

    Security scanned

    Verified clean 25 days ago

    • Passed all security checks, Safe to install

    Listed25 days ago

    What's inside

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