Whisper

OpenAI's speech recognition model that handles 99 languages and was trained on 680,000 hours of audio. You get six model sizes from tiny (39M params) to turbo (809M params), with turbo being the sweet spot for speed and quality. It's straightforward for transcribing podcasts, generating subtitles, or translating foreign audio to English text. The main gotchas are hallucinations on long audio and no speaker diarization, so if you need to identify who's talking when, look at AssemblyAI instead. Works great with an initial prompt to help with technical terms, and runs 10-20x faster on GPU. MIT licensed with 72,900+ GitHub stars.

skills.sh installs: 1K
GitHub stars: 14.4K
last updated: Aug 21, 2026

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Install

$ npx -y skills add orchestra-research/ai-research-skills --skill whisper --agent claude-code

Installs into .claude/skills of the current project.

Files

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

# 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

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']}")