whisper

Maintainer: cjwbw

Total Score

49

Last updated 5/21/2024

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Model LinkView on Replicate
API SpecView on Replicate
Github LinkView on Github
Paper LinkNo paper link provided

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Model overview

whisper is a large, general-purpose speech recognition model developed by OpenAI. It is trained on a diverse dataset of audio and can perform a variety of speech-related tasks, including multilingual speech recognition, speech translation, and spoken language identification. The whisper model is available in different sizes, with the larger models offering better accuracy at the cost of increased memory and compute requirements. The maintainer, cjwbw, has also created several similar models, such as stable-diffusion-2-1-unclip, anything-v3-better-vae, and dreamshaper, that explore different approaches to image generation and manipulation.

Model inputs and outputs

The whisper model is a sequence-to-sequence model that takes audio as input and produces a text transcript as output. It can handle a variety of audio formats, including FLAC, MP3, and WAV files. The model can also be used to perform speech translation, where the input audio is in one language and the output text is in another language.

Inputs

  • audio: The audio file to be transcribed, in a supported format such as FLAC, MP3, or WAV.
  • model: The size of the whisper model to use, with options ranging from tiny to large.
  • language: The language spoken in the audio, or None to perform language detection.
  • translate: A boolean flag to indicate whether the output should be translated to English.

Outputs

  • transcription: The text transcript of the input audio, in the specified format (e.g., plain text).

Capabilities

The whisper model is capable of performing high-quality speech recognition across a wide range of languages, including less common languages. It can also handle various accents and speaking styles, making it a versatile tool for transcribing diverse audio content. The model's ability to perform speech translation is particularly useful for applications where users need to consume content in a language they don't understand.

What can I use it for?

The whisper model can be used in a variety of applications, such as:

  • Transcribing audio recordings for content creation, research, or accessibility purposes.
  • Translating speech-based content, such as videos or podcasts, into multiple languages.
  • Integrating speech recognition and translation capabilities into chatbots, virtual assistants, or other conversational interfaces.
  • Automating the captioning or subtitling of video content.

Things to try

One interesting aspect of the whisper model is its ability to detect the language spoken in the audio, even if it's not provided as an input. This can be useful for applications where the language is unknown or variable, such as transcribing multilingual conversations. Additionally, the model's performance can be fine-tuned by adjusting parameters like temperature, patience, and suppressed tokens, which can help improve accuracy for specific 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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whisper

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Total Score

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