whisper

name	whisper
description	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.
version	1.0.0
author	Orchestra Research
license	MIT
dependencies	["openai-whisper","transformers","torch"]
platforms	["linux","macos"]
metadata	{"sonic":{"tags":["Whisper","Speech Recognition","ASR","Multimodal","Multilingual","OpenAI","Speech-To-Text","Transcription","Translation","Audio Processing"]}}

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']}")

Model sizes

models = [, , , , , ]


model = whisper.load_model()