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)
# macOS/Linux/Windows
pip install openai-whisper
# Verify
whisper --help
Option 2: whisper.cpp (C++ - faster)
# 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)
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
# 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
# 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 --> 00:00:08,200
Today we'll discuss video editing.
VTT (WebVTT)
WEBVTT
00:00:01.000 --> 00:00:04.500
Hello and welcome to this video.
00:00:05.000 --> 00:00:08.200
Today we'll discuss video editing.
JSON (with word-level timing)
{
"text": "Hello and welcome to this video.",
"segments": [
{
"id": 0,
"start": 1.0,
"end": 4.5,
"text": " Hello and welcome to this video.",
"words": [
{"word": "Hello", "start": 1.0, "end": 1.3},
{"word": "and", "start": 1.4, "end": 1.5},
{"word": "welcome", "start": 1.6, "end": 2.0},
{"word": "to", "start": 2.1, "end": 2.2},
{"word": "this", "start": 2.3, "end": 2.5},
{"word": "video", "start": 2.6, "end": 3.0}
]
}
]
}
Audio Extraction for Transcription
Before transcribing video, extract audio in optimal format:
# Extract audio as WAV (16kHz, mono - optimal for Whisper)
ffmpeg -i video.mp4 -ar 16000 -ac 1 -c:a pcm_s16le audio.wav
# Extract as high-quality WAV for archival
ffmpeg -i video.mp4 -vn -c:a pcm_s16le audio.wav
# Extract as compressed MP3 (smaller, still works)
ffmpeg -i video.mp4 -vn -c:a libmp3lame -q:a 2 audio.mp3
Timing Synchronization
Convert Whisper JSON to FCP Timing
import json
def whisper_to_fcp_timing(whisper_json_path, fps=24):
"""Convert Whisper JSON output to FCP-compatible timing."""
with open(whisper_json_path) as f:
data = json.load(f)
segments = []
for seg in data.get("segments", []):
segments.append({
"start_time": seg["start"],
"end_time": seg["end"],
"start_frame": int(seg["start"] * fps),
"end_frame": int(seg["end"] * fps),
"text": seg["text"].strip(),
"words": seg.get("words", [])
})
return segments
Frame-Accurate Timing
# Get exact frame count and duration
ffprobe -v error -count_frames -select_streams v:0 \
-show_entries stream=nb_read_frames,duration,r_frame_rate \
-of json video.mp4
Speaker Diarization
For multi-speaker content, use pyannote.audio:
pip install pyannote.audio
from pyannote.audio import Pipeline
pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization@2.1")
diarization = pipeline("audio.wav")
for turn, _, speaker in diarization.itertracks(yield_label=True):
print(f"{turn.start:.1f}s - {turn.end:.1f}s: {speaker}")
Batch Processing
#!/bin/bash
# Transcribe all videos in directory
MODEL="small"
OUTPUT_DIR="transcripts"
mkdir -p "$OUTPUT_DIR"
for video in *.mp4 *.mov *.avi; do
[[ -f "$video" ]] || continue
base="${video%.*}"
# Extract audio
ffmpeg -i "$video" -ar 16000 -ac 1 -c:a pcm_s16le "/tmp/${base}.wav" -y
# Transcribe
whisper "/tmp/${base}.wav" --model "$MODEL" \
--output_format all \
--output_dir "$OUTPUT_DIR"
# Cleanup temp audio
rm "/tmp/${base}.wav"
echo "Transcribed: $video"
done
Quality Optimization
Improve Accuracy
- Noise reduction before transcription:
ffmpeg -i noisy_audio.wav -af "highpass=f=200,lowpass=f=3000,afftdn=nf=-25" clean_audio.wav
- Use language hint:
whisper audio.mp3 --language en --model medium
- Provide initial prompt for context:
whisper audio.mp3 --initial_prompt "Technical discussion about video editing software."
Performance Tips
- GPU acceleration (if available):
whisper audio.mp3 --model large-v3 --device cuda
- Process in chunks for long videos:
# Split audio into 10-minute chunks
# Transcribe each chunk
# Merge results with time offset adjustment
Error Handling
# Validate audio file before transcription
validate_audio() {
local file="$1"
if ffprobe -v error -select_streams a:0 -show_entries stream=codec_type -of csv=p=0 "$file" 2>/dev/null | grep -q "audio"; then
return 0
else
echo "Error: No audio stream found in $file"
return 1
fi
}
# Check Whisper installation
check_whisper() {
if command -v whisper &> /dev/null; then
echo "Whisper available"
return 0
else
echo "Error: Whisper not installed. Run: pip install openai-whisper"
return 1
fi
}
Related Skills
- ffmpeg-core - Audio extraction and preprocessing
- final-cut-pro - Import transcripts as titles/markers