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Faster Whisper

Listed
theplasmak·vlatest·ai-ml·MIT

Faster Whisper accelerates speech-to-text transcription using Whisper, providing quicker results for real-time or large audio files.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add theplasmak/faster-whisper --skill faster-whisper -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

theplasmak

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No undeclared network calls: failed

    Contacts 5 hosts that are neither well-known infrastructure nor declared in the skill's frontmatter: download.pytorch.org, ffmpeg.org, hf.co, python.org, youtube.com.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

    Hosts contacted

    www.w3.orgstandardshf.coundeclaredyoutube.comundeclareddownload.pytorch.orgundeclaredpython.orgundeclaredffmpeg.orgundeclared

    What it found (3 of 14)

    • scripts/transcribe.py:1132contacts hf.co
      "  and accepted: https://hf.co/pyannote/speaker-diarization-3.1",
    • setup.sh:149contacts hf.co
      echo "               and model agreement at https://hf.co/pyannote/speaker-diarization-3.1"
    • setup.sh:317contacts hf.co
      echo "   2. Accept model agreement: https://hf.co/pyannote/speaker-diarization-3.1"

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned theplasmak/faster-whisper at 1c8d6a682c.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
1c8d6a682c
Manifest
SKILL.md
Scanned
11 files, 5,612 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill integrates the Faster-Whisper library to enable AI agents to perform high-speed, accurate speech-to-text transcription. It leverages optimized neural networks to process audio files efficiently, making it ideal for converting spoken content into written text with minimal latency.

When to use it

  • Transcribing long-form audio recordings like podcasts or interviews quickly without sacrificing accuracy.
  • Processing real-time audio streams where low-latency output is critical for downstream tasks.
  • Converting voice notes or meeting recordings into searchable text summaries for knowledge management.
  • Automating the extraction of dialogue from video files for content analysis or captioning.

Key capabilities

  • High-Speed Transcription: Utilizes optimized algorithms to significantly reduce inference time compared to standard Whisper models.
  • Multi-language Support: Capable of recognizing and transcribing speech in a wide variety of languages automatically.
  • Efficient Resource Usage: Designed to run with lower computational overhead, making it suitable for various deployment environments.

Example prompts

  • "Transcribe this audio file into text using the faster-whisper model."
  • "Extract all spoken dialogue from this video recording and format it as a script."
  • "Convert this podcast episode into a searchable text summary with timestamps."

Tips & gotchas

Ensure you have valid audio files in supported formats (e.g., MP3, WAV) before initiating transcription tasks. While the model is optimized for speed, extremely noisy environments may still require preprocessing to maintain high accuracy levels.