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:

dot

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

bash

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

Requirements:

•Python 3.10+, ffmpeg

Platform Support

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

*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:

bash

# 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

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

Usage

bash

# 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

Options

code

--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

bash

# 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`; 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

Faster Whisper

When to Use

Quick Reference

Model Selection

Model Table

Standard Models (Full Whisper)

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

Setup

Linux / macOS / WSL2

Platform Support

GPU Support (IMPORTANT!)

Usage

Options

Examples

Common Mistakes

Performance Notes

Why faster-whisper?

Troubleshooting

References