Whisper Transcribe

Transcribe audio files to text using OpenAI Whisper. Supports speech-to-text with auto language detection, multiple output formats (txt, srt, vtt, json), batch processing, and model selection (tiny to large). Use when transcribing audio recordings, podcasts, voice messages, lectures, meetings, or any audio/video file to text. Handles mp3, wav, m4a, ogg, flac, webm, opus, aac formats.

Whisper Transcribe

Transcribe audio with scripts/transcribe.sh:

# Basic (auto-detect language, base model)
scripts/transcribe.sh recording.mp3

# German, small model, SRT subtitles
scripts/transcribe.sh --model small --language de --format srt lecture.wav

# Batch process, all formats
scripts/transcribe.sh --format all --output-dir ./transcripts/ *.mp3

# Word-level timestamps
scripts/transcribe.sh --timestamps interview.m4a

Models

| Model | RAM | Speed | Accuracy | Best for | |-------|-----|-------|----------|----------| | tiny | ~1GB | ⚡⚡⚡ | ★★ | Quick drafts, known language | | base | ~1GB | ⚡⚡ | ★★★ | General use (default) | | small | ~2GB | ⚡ | ★★★★ | Good accuracy | | medium | ~5GB | 🐢 | ★★★★★ | High accuracy | | large | ~10GB | 🐌 | ★★★★★ | Best accuracy (slow on Pi) |

Output Formats

txt — Plain text transcript
srt — SubRip subtitles (for video)
vtt — WebVTT subtitles
json — Detailed JSON with timestamps and confidence
all — Generate all formats at once

Requirements

whisper CLI (pip install openai-whisper)
ffmpeg (for audio decoding)
First run downloads the model (~150MB for base)