whisper
OpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes from tiny (39M params) to large (1550M params). Use for speech-to-text, podcast transcription, or multilingual audio processing. Best for robust, multilingual ASR.
npx skills add Orchestra-Research/AI-Research-SKILLs --skill whisper --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Whisper - Robust Speech Recognition OpenAI's multilingual speech recognition model. ## When to use Whisper **Use when:** - Speech-to-text transcription (99 languages) - Podcast/video transcription - Meeting notes automation - Translation to English - Noisy audio transcription - Multilingual audio processing **Metrics**: - **72,900+ GitHub stars** - 99 languages supported - Trained on 680,000 hours of audio - MIT License **Use alternatives instead**: - **AssemblyAI**: Managed API, speaker diarization - **Deepgram**: Real-time streaming ASR - **Google Speech-to-Text**: Cloud-based ## Quick start ### Installation ```bash # Requires Python 3.8-3.11 pip install -U openai-whisper # Requires ffmpeg # macOS: brew install ffmpeg # Ubuntu: sudo apt install ffmpeg # Windows: choco install ffmpeg ``` ### Basic transcription ```python import whisper # Load model model = whisper.load_model("base") # Transcribe result = model.transcribe("audio.mp3") # Print text print(result["text"]) # Access segments for segment in result["segments"]: print(f"[{segment['start']:.2f}s - {segment['end']:.2f}s] {segment['text']}") ``` ## Model sizes ```python # Available models models = ["tiny", "base", "small",
- When to use Whisper
- Quick start
- Installation
- Basic transcription
- Model sizes
- Transcription options
- Language specification
- Task selection
- Initial prompt
- Timestamps
- Temperature fallback
- Command line usage
- Batch processing
- Real-time transcription
Requires Python 3.8-3.11 pip install -U openai-whisper Requires ffmpeg Basic transcription whisper audio.mp3 Specify model whisper audio.mp3 --model turbo Output formats whisper audio.mp3 --output_format txt # Plain text whisper audio.mp3 --output_format srt # Subtitles
What does the whisper skill do?
OpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes from tiny (39M params) to large (1550M params). Use for speech-to-text, podcast transcription, or multilingual audio processing. Best for robust, multilingual ASR.
How do I install it?
Run `npx skills add Orchestra-Research/AI-Research-SKILLs --skill whisper --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From Orchestra-Research/AI-Research-SKILLs, a repository with 11,391 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.