Whisper is an open-source automatic speech recognition (ASR) system developed by OpenAI, trained on 680,000 hours of multilingual and multitask supervised data collected from the web. Released in September 2022, Whisper approaches human-level robustness and accuracy on English speech recognition and demonstrates strong performance across many languages, accents, and audio conditions without the need for fine-tuning. The model is designed as a general-purpose speech recognition system capable of multilingual speech recognition, speech translation to English, and language identification. Whisper employs a transformer-based encoder-decoder architecture and was trained using a large-scale weak supervision approach, learning from a diverse dataset of audio paired with transcripts sourced from the internet. This training methodology enables Whisper to handle a wide variety of audio conditions including background noise, technical jargon, accented speech, and low-quality recordings that challenge traditional ASR systems. The model is available in multiple sizes to accommodate different computational requirements: Tiny (39M parameters), Base (74M), Small (244M), Medium (769M), and Large (1.55B parameters), with larger models providing higher accuracy at the cost of increased inference time and memory usage. Whisper supports transcription and translation across approximately 100 languages, with particularly strong performance in English, Spanish, French, German, Italian, Portuguese, Japanese, Korean, Chinese, and many other widely spoken languages. The model includes automatic punctuation, capitalization, and timestamp generation at both the segment and word level. As open-source software released under the MIT license, Whisper can be run locally on consumer hardware with a compatible GPU, integrated into applications through Python, or accessed through OpenAI's commercial API for managed inference. The open-source release has spawned a large ecosystem of tools, integrations, and optimized variants including faster-whisper, whisper.cpp, and WhisperX that offer improved inference speed and additional features.

AI Audio Models

Whisper is a foundational open-source audio model that processes speech using a transformer-based encoder-decoder architecture trained on 680,000 hours of multilingual data. Available in five sizes from 39M to 1.55B parameters, it serves as a core audio understanding model for speech recognition, translation, and language identification tasks across the AI ecosystem.

AI Research Tools

Whisper serves as a foundational tool for speech and audio research, providing a high-quality open-source baseline for benchmarking ASR systems. Researchers use Whisper and its variants for studying multilingual speech recognition, audio processing techniques, and as a component in multimodal AI systems combining speech understanding with other modalities.

AI Transcription

Whisper is one of the most capable and widely used AI transcription systems available, providing automatic speech recognition across approximately 100 languages with near-human accuracy in English. It handles diverse audio conditions including background noise, accented speech, and technical terminology, generating transcripts with automatic punctuation, capitalization, and timestamps.

Open Source LLMs

While technically a speech model rather than a language model, Whisper is one of OpenAI's most significant open-source AI releases under the MIT license. Its open availability has enabled an entire ecosystem of derivative tools and optimized implementations, making it a cornerstone of the open-source AI community for audio processing tasks.