OpenAI Whisper API

Use Cases

• Meeting transcription: record → transcribe → extract action items
• Podcast processing: transcribe episodes for show notes and search
• Voice memo workflow: speak → transcribe → save to notes
• Video subtitle generation: transcribe audio track → SRT/VTT output
• Content repurposing: transcribe talks/lectures into blog posts
• Multilingual transcription: handle audio in 50+ languages
• Automated pipeline: watch folder → transcribe new audio → notify

Tips

• Split large files into segments: `ffmpeg -i input.mp3 -f segment -segment_time 600 -c copy out_%03d.mp3`
• Use `response_format: 'verbose_json'` to get word-level timestamps
• Set the `language` parameter explicitly when you know the language — improves accuracy significantly
• For meetings with multiple speakers, pair with speaker diarization (not built into Whisper API)
• Use SRT/VTT output format for subtitle generation
• Combine with OpenClaw's summarization for a full pipeline: transcribe → summarize → action items
• For privacy-sensitive audio, self-host Whisper locally instead of using the API
• Pre-process noisy audio with ffmpeg noise reduction before transcribing

Known Issues & Gotchas

• Maximum file size is 25MB — split larger files before uploading
• Audio is sent to OpenAI's servers — not suitable for highly confidential recordings
• No real-time/streaming transcription — batch only (send file, get result)
• Whisper can 'hallucinate' text on silent or very noisy segments — review critical transcriptions
• The API uses the large-v2 model — you can't select smaller/faster models via API
• Cost is per minute of audio, not per API call — a 60-minute file costs ~$0.36
• Language auto-detection works but explicit `language` parameter improves accuracy
• GPT-4o Transcribe is newer and potentially better but at a higher price point

Alternatives

• Deepgram
• AssemblyAI
• Whisper (self-hosted)
• Google Speech-to-Text
• GPT-4o Transcribe (OpenAI)

Community Feedback

Comparative Review of Speech-to-Text APIs (2025): GPT-4o Transcribe is the new contender, but Whisper remains the reliable workhorse at $0.006/min.

— Reddit r/speechtech

What we like most about OpenAI Whisper is its high accuracy and strong multilingual support. It performs well with different accents and noisy audio.

— G2 Reviews

Unbiased test results show Whisper scoring 98% accuracy with low hallucination rates. Real-world speed and cost make it competitive with enterprise alternatives.

— DIY AI

Deepgram is 36% more accurate, up to 5x faster, and has lower TCO than OpenAI Whisper for real-time streaming use cases.

— Deepgram Blog

Configuration Examples

Basic transcription

curl -s https://api.openai.com/v1/audio/transcriptions \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -F file=@meeting.mp3 \
  -F model=whisper-1 \
  -F response_format=text

Transcription with timestamps

curl -s https://api.openai.com/v1/audio/transcriptions \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -F file=@podcast.mp3 \
  -F model=whisper-1 \
  -F response_format=verbose_json \
  -F language=en | jq '.segments[] | {start, end, text}'

Generate SRT subtitles

curl -s https://api.openai.com/v1/audio/transcriptions \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -F file=@video_audio.mp3 \
  -F model=whisper-1 \
  -F response_format=srt > subtitles.srt

Split large files first

# Split into 10-minute segments
ffmpeg -i long_meeting.mp3 -f segment -segment_time 600 -c copy /tmp/seg_%03d.mp3

# Transcribe each segment
for f in /tmp/seg_*.mp3; do
  curl -s https://api.openai.com/v1/audio/transcriptions \
    -H "Authorization: Bearer $OPENAI_API_KEY" \
    -F file=@"$f" -F model=whisper-1 -F response_format=text
done

Installation

# Requires OPENAI_API_KEY env var