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

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)

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

.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

*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.

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

• Windows users: Ensure you have NVIDIA drivers installed
• WSL2 users: Ensure you have the NVIDIA CUDA drivers for WSL installed on Windows

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

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

• faster-whisper GitHub
• Distil-Whisper Paper
• HuggingFace Models