transcription

plugin: video-editing
updated: 2026-01-20

# Transcription with Whisper

Production-ready patterns for audio/video transcription using OpenAI Whisper.

## System Requirements

### Installation Options

**Option 1: OpenAI Whisper (Python)**
```bash
# macOS/Linux/Windows
pip install openai-whisper

# Verify
whisper --help
```

**Option 2: whisper.cpp (C++ - faster)**
```bash
# macOS
brew install whisper-cpp

# Linux - build from source
git clone https://github.com/ggerganov/whisper.cpp
cd whisper.cpp && make

# Windows - use pre-built binaries or build with cmake
```

**Option 3: Insanely Fast Whisper (GPU accelerated)**
```bash
pip install insanely-fast-whisper
```

### Model Selection

| Model | Size | VRAM | Accuracy | Speed | Use Case |
|-------|------|------|----------|-------|----------|
| tiny | 39M | ~1GB | Low | Fastest | Quick previews |
| base | 74M | ~1GB | Medium | Fast | Draft transcripts |
| small | 244M | ~2GB | Good | Medium | General use |
| medium | 769M | ~5GB | Better | Slow | Quality transcripts |
| large-v3 | 1550M | ~10GB | Best | Slowest | Final production |

**Recommendation:** Start with `small` for speed/quality balance. Use `large-v3` for final delivery.

## Basic Transcription

### Using OpenAI Whisper

```bash
# Basic transcription (auto-detect language)
whisper audio.mp3 --model small

# Specify language and output format
whisper audio.mp3 --model medium --language en --output_format srt

# Multiple output formats
whisper audio.mp3 --model small --output_format all

# With timestamps and word-level timing
whisper audio.mp3 --model small --word_timestamps True
```

### Using whisper.cpp

```bash
# Download model first
./models/download-ggml-model.sh base.en

# Transcribe
./main -m models/ggml-base.en.bin -f audio.wav -osrt

# With timestamps
./main -m models/ggml-base.en.bin -f audio.wav -ocsv
```

## Output Formats

### SRT (SubRip Subtitle)
```
1
00:00:01,000 --> 00:00:04,500
Hello and welcome to this video.

2
00:00:05,000 --> 00:00:0