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Whisper by OpenAI - AI Audio Models Tool

Overview

Whisper by OpenAI is an open-source, general-purpose speech recognition system built on a transformer encoder-decoder architecture. Trained on a large, diverse multilingual and multitask dataset, Whisper can perform automatic speech recognition (ASR), language identification, and direct speech-to-English translation. It ships with a simple command-line interface and a Python API for programmatic use, and provides multiple pretrained model sizes to trade off speed and accuracy. Whisper is widely used for offline transcription pipelines, research, and as a baseline for production systems where on-device or self-hosted transcription is required. According to the project README, the model family was trained on roughly 680,000 hours of supervised audio data, enabling robust handling of accents, background noise, and multiple languages. The project has a large, active community that produces optimized runtimes and ports (for example, whisper.cpp and faster-whisper) to enable faster CPU or mobile inference and smaller memory footprints.

GitHub Statistics

  • Stars: 92,958
  • Forks: 11,637
  • Contributors: 78
  • License: MIT
  • Primary Language: Python
  • Last Updated: 2025-06-26T01:05:47Z
  • Latest Release: v20250625

According to the GitHub repository, Whisper has 92,958 stars, 11,637 forks, and 78 contributors, and is licensed under the MIT License. The project receives regular commits (last recorded commit: 2025-06-26T01:05:47Z) and attracts many community-driven forks and ports. High star and fork counts plus numerous downstream projects indicate healthy adoption, active ecosystem development, and substantial community interest in optimization and platform ports.

Installation

Install via pip:

pip install -U openai-whisper
pip install -U git+https://github.com/openai/whisper.git
pip install -U torch torchvision torchaudio  # install appropriate PyTorch wheel for your CUDA/CPU
sudo apt-get update && sudo apt-get install -y ffmpeg  # system dependency for audio I/O
whisper audio.mp3 --model large --task transcribe  # example CLI usage

Key Features

  • Multilingual transcription across dozens of languages with automatic language detection
  • Direct speech-to-English translation (speech translation) via a single model
  • Multiple pretrained sizes (tiny, base, small, medium, large) for speed/accuracy trade-offs
  • CLI and Python API for quick integration into pipelines and batch processing
  • Offline inference enabling self-hosted transcription without external APIs

Community

Whisper has a large, active community evident from its ~92.9k GitHub stars and many forks. Users praise its transcription accuracy and easy CLI/Python integration, while noting compute cost for larger models. The ecosystem includes optimized third-party ports (e.g., whisper.cpp, faster-whisper) for faster CPU/mobile inference and quantized runtimes. The MIT license and ongoing commits foster broad reuse, contributions, and tooling around model deployment and performance improvements.

Last Refreshed: 2026-01-09

Key Information

  • Category: Audio Models
  • Type: AI Audio Models Tool
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