![]() For years before I got my cochlear implants, I never ordered anything more than a tall coffee. I loved the scene where Kanevsky orders at Starbucks. Mohammed Obiedat, a deaf professor at Gallaudet University, play a board game with his kids, chat with them about their schoolwork, and actively participate in a parent-teacher conference. Additional WER/CER metrics corresponding to the other models and datasets can be found in Appendix D.1, D.2, and D.4 of the paper, as well as the BLEU (Bilingual Evaluation Understudy) scores for translation in Appendix D.3.Watch Dimitri Kanevsky, a deaf Google research scientist, use the Android app to order tea at Starbucks and chat with a colleague about a weekend chili party.Īnd watch Dr. ![]() ![]() ![]() The figure below shows a performance breakdown of large-v3 and large-v2 models by language, using WERs (word error rates) or CER (character error rates, shown in Italic) evaluated on the Common Voice 15 and Fleurs datasets. Whisper's performance varies widely depending on the language. We observed that the difference becomes less significant for the small.en and medium.en models. en models for English-only applications tend to perform better, especially for the tiny.en and base.en models. Below are the names of the available models and their approximate memory requirements and inference speed relative to the large model actual speed may vary depending on many factors including the available hardware. There are five model sizes, four with English-only versions, offering speed and accuracy tradeoffs. Pip install setuptools-rust Available models and languages You can download and install (or update to) the latest release of Whisper with the following command: The codebase also depends on a few Python packages, most notably OpenAI's tiktoken for their fast tokenizer implementation. We used Python 3.9.9 and PyTorch 1.10.1 to train and test our models, but the codebase is expected to be compatible with Python 3.8-3.11 and recent PyTorch versions. The multitask training format uses a set of special tokens that serve as task specifiers or classification targets. 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. ApproachĪ Transformer sequence-to-sequence model is trained on various speech processing tasks, including multilingual speech recognition, speech translation, spoken language identification, and voice activity detection. It is trained on a large dataset of diverse audio and is also a multitasking model that can perform multilingual speech recognition, speech translation, and language identification. Whisper is a general-purpose speech recognition model.
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