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Whisper is a general-purpose speech recognition model developed by OpenAI. It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition as well as speech translation and language identification. Whisper uses a Transformer sequence-to-sequence model trained on various speech processing tasks, including multilingual speech recognition, speech translation, spoken language identification, and voice activity detection. These tasks are jointly represented as a sequence of tokens to be predicted by the decoder, allowing 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 can be used via the command line or within Python. For command-line usage, transcribe audio files by specifying the file path and the desired model size. For Python usage, load the model and utilize the transcribe() method to process your audio files.
Whisper is a general-purpose speech recognition model trained on a large, diverse dataset of audio. It is capable of multilingual speech recognition, speech translation, and language identification.
You can install Whisper via pip using the command: `pip install -U openai-whisper`. Additionally, you must install ffmpeg and may require Rust.
There are five available model sizes: tiny, base, small, medium, and large. Each size provides different tradeoffs between processing speed and accuracy.
You can transcribe files using the command-line tool (e.g., `whisper audio.flac audio.mp3 audio.wav --model medium`) or by using the Python API.
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