Skills
skills/openclaw/skills/faster-whisper

faster-whisper

SKILL.md

Faster Whisper

Local speech-to-text using faster-whisper — a CTranslate2 reimplementation of OpenAI's Whisper that runs 4-6x faster with identical accuracy. With GPU acceleration, expect ~20x realtime transcription (a 10-minute audio file in ~30 seconds).

When to Use

Use this skill when you need to:

  • Transcribe audio/video files — meetings, interviews, podcasts, lectures, YouTube videos
  • Convert speech to text locally — no API costs, works offline (after model download)
  • Batch process multiple audio files — efficient for large collections
  • Generate subtitles/captions — word-level timestamps available
  • Multilingual transcription — supports 99+ languages with auto-detection

Trigger phrases: "transcribe this audio", "convert speech to text", "what did they say", "make a transcript", "audio to text", "subtitle this video"

When NOT to use:

  • Real-time/streaming transcription (use streaming-optimized tools instead)
  • Cloud-only environments without local compute
  • Files <10 seconds where API call latency doesn't matter

Quick Reference

Task Command Notes
Basic transcription ./scripts/transcribe audio.mp3 Uses default distil-large-v3
Faster English ./scripts/transcribe audio.mp3 --model distil-medium.en --language en English-only, 6.8x faster
Maximum accuracy ./scripts/transcribe audio.mp3 --model large-v3-turbo --beam-size 10 Slower but best quality
Word timestamps ./scripts/transcribe audio.mp3 --word-timestamps For subtitles/captions
JSON output ./scripts/transcribe audio.mp3 --json -o output.json Programmatic access
Multilingual ./scripts/transcribe audio.mp3 --model large-v3-turbo Auto-detects language
Remove silence ./scripts/transcribe audio.mp3 --vad Voice activity detection

Model Selection

Choose the right model for your needs:

digraph model_selection {
    rankdir=LR;
    node [shape=box, style=rounded];

    start [label="Start", shape=doublecircle];
    need_accuracy [label="Need maximum\naccuracy?", shape=diamond];
    multilingual [label="Multilingual\ncontent?", shape=diamond];
    resource_constrained [label="Resource\nconstraints?", shape=diamond];

    large_v3 [label="large-v3\nor\nlarge-v3-turbo", style="rounded,filled", fillcolor=lightblue];
    large_turbo [label="large-v3-turbo", style="rounded,filled", fillcolor=lightblue];
    distil_large [label="distil-large-v3\n(default)", style="rounded,filled", fillcolor=lightgreen];
    distil_medium [label="distil-medium.en", style="rounded,filled", fillcolor=lightyellow];
    distil_small [label="distil-small.en", style="rounded,filled", fillcolor=lightyellow];

    start -> need_accuracy;
    need_accuracy -> large_v3 [label="yes"];
    need_accuracy -> multilingual [label="no"];
    multilingual -> large_turbo [label="yes"];
    multilingual -> resource_constrained [label="no (English)"];
    resource_constrained -> distil_small [label="mobile/edge"];
    resource_constrained -> distil_medium [label="some limits"];
    resource_constrained -> distil_large [label="no"];
}

Model Table

Standard Models (Full Whisper)

Model Size Speed Accuracy Use Case
tiny / tiny.en 39M Fastest Basic Quick drafts
base / base.en 74M Very fast Good General use
small / small.en 244M Fast Better Most tasks
medium / medium.en 769M Moderate High Quality transcription
large-v1/v2/v3 1.5GB Slower Best Maximum accuracy
large-v3-turbo 809M Fast Excellent Recommended for accuracy

Distilled Models (~6x Faster, ~1% WER difference)

Model Size Speed vs Standard Accuracy Use Case
distil-large-v3 756M ~6.3x faster 9.7% WER Default, best balance
distil-large-v2 756M ~5.8x faster 10.1% WER Fallback
distil-medium.en 394M ~6.8x faster 11.1% WER English-only, resource-constrained
distil-small.en 166M ~5.6x faster 12.1% WER Mobile/edge devices

.en models are English-only and slightly faster/better for English content.

Setup

Linux / macOS / WSL2

# Run the setup script (creates venv, installs deps, auto-detects GPU)
./setup.sh

Windows (Native)

# Run from PowerShell (auto-installs Python & ffmpeg if missing via winget)
.\setup.ps1

The Windows setup script will:

  • Auto-install Python 3.12 via winget if not found
  • Auto-install ffmpeg via winget if not found
  • Detect NVIDIA GPU and install CUDA-enabled PyTorch
  • Create venv and install all dependencies

Requirements:

  • Linux/macOS/WSL2: Python 3.10+, ffmpeg
  • Windows: Nothing! Setup auto-installs prerequisites via winget

Platform Support

Platform Acceleration Speed Auto-Install
Windows + NVIDIA GPU CUDA ~20x realtime 🚀 ✅ Full
Linux + NVIDIA GPU CUDA ~20x realtime 🚀 Manual prereqs
WSL2 + NVIDIA GPU CUDA ~20x realtime 🚀 Manual prereqs
macOS Apple Silicon CPU* ~3-5x realtime Manual prereqs
macOS Intel CPU ~1-2x realtime Manual prereqs
Windows (no GPU) CPU ~1x realtime ✅ Full
Linux (no GPU) CPU ~1x realtime Manual prereqs

*faster-whisper uses CTranslate2 which is CPU-only on macOS, but Apple Silicon is fast enough for practical use.

GPU Support (IMPORTANT!)

The setup script auto-detects your GPU and installs PyTorch with CUDA. Always use GPU if available — CPU transcription is extremely slow.

Hardware Speed 9-min video
RTX 3070 (GPU) ~20x realtime ~27 sec
CPU (int8) ~0.3x realtime ~30 min

If setup didn't detect your GPU, manually install PyTorch with CUDA:

Linux/macOS/WSL2:

# For CUDA 12.x
uv pip install --python .venv/bin/python torch --index-url https://download.pytorch.org/whl/cu121

# For CUDA 11.x
uv pip install --python .venv/bin/python torch --index-url https://download.pytorch.org/whl/cu118

Windows:

# For CUDA 12.x
.venv\Scripts\pip install torch --index-url https://download.pytorch.org/whl/cu121

# For CUDA 11.x
.venv\Scripts\pip install torch --index-url https://download.pytorch.org/whl/cu118

Usage

Linux/macOS/WSL2:

# Basic transcription
./scripts/transcribe audio.mp3

# With specific model
./scripts/transcribe audio.wav --model large-v3-turbo

# With word timestamps
./scripts/transcribe audio.mp3 --word-timestamps

# Specify language (faster than auto-detect)
./scripts/transcribe audio.mp3 --language en

# JSON output
./scripts/transcribe audio.mp3 --json

Windows (cmd or PowerShell):

# Basic transcription
.\scripts\transcribe.cmd audio.mp3

# With specific model
.\scripts\transcribe.cmd audio.wav --model large-v3-turbo

# With word timestamps (PowerShell native syntax also works)
.\scripts\transcribe.ps1 audio.mp3 -WordTimestamps

# JSON output
.\scripts\transcribe.cmd audio.mp3 --json

Options

--model, -m        Model name (default: distil-large-v3)
--language, -l     Language code (e.g., en, es, fr - auto-detect if omitted)
--word-timestamps  Include word-level timestamps
--beam-size        Beam search size (default: 5, higher = more accurate but slower)
--vad              Enable voice activity detection (removes silence)
--json, -j         Output as JSON
--output, -o       Save transcript to file
--device           cpu or cuda (auto-detected)
--compute-type     int8, float16, float32 (default: auto-optimized)
--quiet, -q        Suppress progress messages

Examples

# Transcribe YouTube audio (after extraction with yt-dlp)
yt-dlp -x --audio-format mp3 <URL> -o audio.mp3
./scripts/transcribe audio.mp3

# Batch transcription with JSON output
for file in *.mp3; do
  ./scripts/transcribe "$file" --json > "${file%.mp3}.json"
done

# High-accuracy transcription with larger beam size
./scripts/transcribe audio.mp3 \
  --model large-v3-turbo --beam-size 10 --word-timestamps

# Fast English-only transcription
./scripts/transcribe audio.mp3 \
  --model distil-medium.en --language en

# Transcribe with VAD (removes silence)
./scripts/transcribe audio.mp3 --vad

Common Mistakes

Mistake Problem Solution
Using CPU when GPU available 10-20x slower transcription Check nvidia-smi on Windows/Linux; verify CUDA installation
Not specifying language Wastes time auto-detecting on known content Use --language en when you know the language
Using wrong model Unnecessary slowness or poor accuracy Default distil-large-v3 is excellent; only use large-v3 if accuracy issues
Ignoring distilled models Missing 6x speedup with <1% accuracy loss Try distil-large-v3 before reaching for standard models
Forgetting ffmpeg Setup fails or audio can't be processed Setup script handles this; manual installs need ffmpeg separately
Out of memory errors Model too large for available VRAM/RAM Use smaller model or --compute-type int8
Over-engineering beam size Diminishing returns past beam-size 5-7 Default 5 is fine; try 10 for critical transcripts

Performance Notes

  • First run: Downloads model to ~/.cache/huggingface/ (one-time)
  • GPU: Automatically uses CUDA if available (~10-20x faster)
  • Quantization: INT8 used on CPU for ~4x speedup with minimal accuracy loss
  • Memory:
    • distil-large-v3: ~2GB RAM / ~1GB VRAM
    • large-v3-turbo: ~4GB RAM / ~2GB VRAM
    • tiny/base: <1GB RAM

Why faster-whisper?

  • Speed: ~4-6x faster than OpenAI's original Whisper
  • Accuracy: Identical (uses same model weights)
  • Efficiency: Lower memory usage via quantization
  • Production-ready: Stable C++ backend (CTranslate2)
  • Distilled models: ~6x faster with <1% accuracy loss

Troubleshooting

"CUDA not available — using CPU": Install PyTorch with CUDA (see GPU Support above) Setup fails: Make sure Python 3.10+ is installed Out of memory: Use smaller model or --compute-type int8 Slow on CPU: Expected — use GPU for practical transcription Model download fails: Check ~/.cache/huggingface/ permissions (Linux/macOS) or %USERPROFILE%\.cache\huggingface\ (Windows)

Windows-Specific

"winget not found": Install App Installer from Microsoft Store, or install Python/ffmpeg manually "Python not in PATH after install": Close and reopen your terminal, then run setup.ps1 again PowerShell execution policy error: Run Set-ExecutionPolicy -Scope CurrentUser -ExecutionPolicy RemoteSigned or use transcribe.cmd instead nvidia-smi not found but have GPU: Install NVIDIA drivers — the Game Ready or Studio drivers include nvidia-smi

References

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