OpenAI’s Whisper is a general-purpose speech recognition model, which is trained on a large dataset of diverse audio and can perform multilingual speech recognition, speech translation, and language identification. The model uses a simple end-to-end approach and is implemented as an encoder-decoder Transformer. Input audio is split into 30-second chunks, converted into a log-Mel spectrogram, and then passed into an encoder. A decoder is trained to predict the corresponding text caption, intermixed with special tokens that direct the single model.
“Whisper” is a natural language processing system that can “understand” speech and transcribe it into text, powered by deep learning and neural networks. The solution is “trained” on natural language, and it is available as an API that can be accessed through OpenAI’s platform. The API provides convenient on-demand access priced at $0.006 per minute.
There are also third-party tools and applications built on top of this tool. For example, WaaS (Whisper as a Service) is a GUI and API for OpenAI Whisper that allows users to upload and transcribe audio or video files. After the transcription is complete, users receive an email with download links for the Jojo-file, SRT, or text.
Whisper’s GitHub repository includes the code for the model, as well as utilities and applications that can be used with the model. There are also community-contributed code snippets and tools available, such as the whisper-dictation application, which allows users to transcribe speech using it from the command line.
In summary, OpenAI’s Whisper is a powerful speech recognition model that can perform multilingual speech recognition, speech translation, and language identification. It is available as an API through OpenAI’s platform, and there are also third-party tools and applications available that can be used with the model. The model’s code, utilities, and applications are available on its GitHub repository.
