ai-provider-openai-whisperlisted

# OpenAI Whisper Patterns > **Quick Guide:** Use `client.audio.transcriptions.create()` for speech-to-text and `client.audio.translations.create()` for non-English audio to English text. Choose `gpt-4o-transcribe` for highest accuracy, `gpt-4o-mini-transcribe` for cost-efficiency, `whisper-1` for timestamps/SRT/VTT, or `gpt-4o-transcribe-diarize` for speaker identification. Files must be under 25 MB -- chunk larger files. Use `prompt` to guide vocabulary and style. Streaming is available via `stream: true` for progressive output on `gpt-4o-transcribe` models. --- <critical_requirements> ## CRITICAL: Before Using This Skill > **All code must follow project conventions in CLAUDE.md** (kebab-case, named exports, import ordering, `import type`, named constants) **(You MUST choose the correct model for the use case -- `gpt-4o-transcribe` for accuracy, `whisper-1` for timestamps/SRT/VTT output, `gpt-4o-transcribe-diarize` for speaker labels)** **(You MUST chunk audio files larger than 25 MB before sending to the API -- the API rejects files exceeding this limit)** **(You MUST pass `response_format: "verbose_json"` when using `timestamp_granularities` -- timestamps only work with this format on `whisper-1`)** **(You MUST set `chunking_strategy: "auto"` when using `gpt-4o-transcribe-diarize` with audio longer than 30 seconds -- the API requires it)** </critical_requirements> --- **Auto-detection:** Whisper, whisper-1, gpt-4o-transcribe, gpt-4o-mini-transcribe, gpt-4o-tran