whisper-large-v3

Maintainer: openai

Total Score

2.5K

Last updated 5/19/2024

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PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The whisper-large-v3 model is a general-purpose speech recognition model developed by OpenAI. It is the latest version of the Whisper model, building on the previous Whisper large models. The whisper-large-v3 model has a few minor architectural differences from the previous large models, including using 128 Mel frequency bins instead of 80 and adding a new language token for Cantonese.

The Whisper model was trained on a massive 680,000 hours of audio data, with 65% English data, 18% non-English data with English transcripts, and 17% non-English data with non-English transcripts covering 98 languages. This allows the model to perform well on a diverse range of speech recognition and translation tasks, without needing to fine-tune on specific datasets.

Similar Whisper models include the Whisper medium, Whisper tiny, and the whisper-large-v3 model developed by Nate Raw. There is also an incredibly fast version of the Whisper large model by Vaibhav Srivastav.

Model inputs and outputs

The whisper-large-v3 model takes audio samples as input and generates text transcripts as output. The audio can be in any of the 98 languages covered by the training data. The model can also be used for speech translation, where it generates text in a different language than the audio.

Inputs

  • Audio samples in any of the 98 languages the model was trained on

Outputs

  • Text transcripts of the audio in the same language
  • Translated text transcripts in a different language

Capabilities

The whisper-large-v3 model demonstrates strong performance on a variety of speech recognition and translation tasks, with 10-20% lower error rates compared to the previous Whisper large model. It is robust to accents, background noise, and technical language, and can perform zero-shot translation from multiple languages into English.

However, the model's performance is uneven across languages, with lower accuracy on low-resource and low-discoverability languages where less training data was available. It also has a tendency to generate repetitive or hallucinated text that is not actually present in the audio input.

What can I use it for?

The primary intended use of the Whisper models is for AI researchers studying model capabilities, robustness, and limitations. However, the models can also be quite useful as a speech recognition solution for developers, especially for English transcription tasks.

The Whisper models could be used to build applications that improve accessibility, such as closed captioning or voice-to-text transcription. While the models cannot be used for real-time transcription out of the box, their speed and size suggest that others may be able to build near-real-time applications on top of them.

Things to try

One interesting aspect of the Whisper models is their ability to perform speech translation, generating text transcripts in a different language than the audio input. Developers could experiment with using the model for tasks like simultaneous interpretation or multilingual subtitling.

Another avenue to explore is fine-tuning the pre-trained Whisper model on specific datasets or domains. The blog post Fine-Tune Whisper with Transformers provides a guide on how to fine-tune the model with as little as 5 hours of labeled data, which can improve performance on particular languages or use cases.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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