Whisper is a multitasking speech recognition model for multilingual speech recognition, translation, and language identification. Available in 5 sizes.
Whisper is a cutting-edge speech recognition model that is designed to perform multiple tasks, including multilingual speech recognition, speech translation, and language identification. In this article, we will explore Whisper’s approach, setup, available models and languages, command-line usage, Python usage, and more.
Whisper’s approach is based on a Transformer sequence-to-sequence model that is trained on various speech processing tasks. This allows a single model to replace many stages of a traditional speech-processing pipeline. The multitask training format uses a set of special tokens that serve as task specifiers or classification targets.
Whisper requires Python 3.9.9 and PyTorch 1.10.1 to train and test models, but the codebase is expected to be compatible with Python 3.8-3.10 and recent PyTorch versions. The codebase also depends on several Python packages, including OpenAI’s tiktoken for their fast tokenizer implementation and ffmpeg-python for reading audio files. Rust may also need to be installed, in case tiktoken does not provide a pre-built wheel for your platform. Whisper’s package can be installed using pip.
Whisper offers five model sizes, four with English-only versions, each with varying memory requirements and relative speed. Whisper’s performance varies widely depending on the language. Whisper’s available models and their approximate memory requirements and relative speed are listed in a table in the article.
Whisper’s command-line usage allows you to transcribe speech in audio files, specify language, and translate speech into English. Whisper’s default setting selects the small model, which works well for transcribing English. The article provides examples of how to use Whisper’s command-line usage.
Whisper’s Python usage allows you to perform transcription within Python. The article provides an example of how to use whisper.detect_language() and whisper.decode() which provide lower-level access to the model.
The article encourages users to use the 🙌 Show and tell category in Discussions for sharing more example usages of Whisper and third-party extensions such as web demos, integrations with other tools, ports for different platforms, etc.
Whisper’s code and model weights are released under the MIT License.
In conclusion, Whisper is a powerful speech recognition model that can perform multiple speech processing tasks. With its multitasking approach, five available model sizes, and various languages, Whisper provides users with a versatile speech recognition tool. Whisper’s command-line and Python usage provide flexibility and convenience, while its licensing under the MIT License allows for broad usage and modification.
Whisper is a versatile speech recognition model that can perform multilingual speech recognition, speech translation, spoken language identification, and voice activity detection. The model is trained on a large and diverse audio dataset using a Transformer sequence-to-sequence architecture that allows it to replace multiple stages of traditional speech-processing pipelines. The model is available in five different sizes with varying levels of accuracy and speed to suit different applications. The English-only models tend to perform better for English applications, while the multilingual models are useful for non-English languages. Whisper can be used from the command line or within Python and requires Python 3.8-3.10 and recent PyTorch versions. It also depends on a few Python packages, including OpenAI’s tiktoken and ffmpeg-python. Whisper’s code and model weights are released under the MIT License.
Overall, Whisper is a powerful and flexible tool for speech recognition and related tasks. Its ability to perform multiple speech-processing tasks with high accuracy and speed makes it an attractive option for a wide range of applications. The availability of models of different sizes and languages further increases its utility. The ability to use Whisper from the command line or within Python provides flexibility and ease of use. However, as with any speech recognition model, its performance may vary depending on the specific language and context. Therefore, it is important to evaluate its performance for each specific use case. Overall, Whisper is a valuable addition to the speech recognition landscape and is likely to find many practical applications.