whisperx

WhisperX provides local speech-to-text transcription using OpenAI Whisper, with high-quality offline recognition, no API key required, word-level timestamps,...

niuzb

@niuzb

What This Skill Does

Transcribes audio files to text locally using OpenAI Whisper, with word-level timestamps and optional speaker diarization. Runs fully offline with no API key required.

Replaces cloud-based transcription services like Google Speech-to-Text or OpenAI Whisper API by providing faster, private, offline speech recognition with no usage costs.

When to Use It

  • Transcribe meeting recordings or lecture audio to searchable text
  • Generate subtitles or captions with precise word-level timing for videos
  • Convert voice memos or dictations into written notes without internet
  • Transcribe multilingual audio files with automatic language detection
  • Separate and label speakers in a conversation or interview recording

Install

$ openclaw skills install @niuzb/whisperx

WhisperX Speech Recognition Skill

Local offline speech-to-text - A WhisperX-powered speech recognition skill for OpenClaw. Up to 30x faster than standard OpenAI Whisper, runs fully offline with no API key required.

Features

  • Pure ASR: Converts voice messages to text only — no voice replies generated
  • Fully offline: Model runs locally, no internet or API key needed
  • Word-level timestamps: Precise per-word time alignment
  • 90+ languages: Includes auto language detection
  • Speaker diarization: Optional, requires a HuggingFace token

Installation

# Install ffmpeg (macOS)
brew install ffmpeg

# Install ffmpeg (Ubuntu/Debian)
apt-get install ffmpeg

# Install WhisperX
pip install whisperx
# or using uvx:
uvx whisperx

GPU users: ensure CUDA 12.8 is installed for faster inference.

Usage

# Basic transcription (auto-detect language)
whisperx path/to/audio.wav

# Specify model and language
whisperx  --model small --language zh path/to/audio.wav

# CPU mode (low memory)
whisperx --model small --device cpu --compute_type int8  path/to/audio.wav

Notes

  • Dependencies: whisperx, ffmpeg
  • Supported formats: MP3, WAV, OGG, FLAC, M4A, OPUS, AAC, and all other ffmpeg-supported formats
  • Model cache: Downloaded automatically to ~/.cache/whisper/ on first run
  • Recommended models: base or small for CPU; large-v3 for GPU

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