whisper-tiny

Maintainer: openai

Total Score

199

Last updated 5/28/2024

🖼️

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-tiny model is a pre-trained artificial intelligence (AI) model for automatic speech recognition (ASR) and speech translation, created by OpenAI. Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalize to many datasets and domains without the need for fine-tuning. The whisper-tiny model is the smallest of the Whisper checkpoints, with only 39 million parameters. It is available in both English-only and multilingual versions.

Similar models include the whisper-large-v3, a general-purpose speech recognition model, the whisper model by OpenAI, the incredibly-fast-whisper model, and the whisperspeech-small model, which is an open-source text-to-speech system built by inverting Whisper.

Model inputs and outputs

Inputs

  • Audio data, such as recordings of speech

Outputs

  • Transcribed text in the same language as the input audio (for speech recognition)
  • Transcribed text in a different language than the input audio (for speech translation)

Capabilities

The whisper-tiny model can transcribe speech and translate speech to text in multiple languages, demonstrating strong generalization abilities without the need for fine-tuning. It can be used for a variety of applications, such as transcribing audio recordings, adding captions to videos, and enabling multilingual communication.

What can I use it for?

The whisper-tiny model can be used in various applications that require speech recognition or speech translation, such as:

  • Transcribing lectures, interviews, or other audio recordings
  • Adding captions or subtitles to videos
  • Enabling real-time translation in video conferencing or other communication tools
  • Developing voice-controlled interfaces for various devices and applications

Things to try

You can experiment with the whisper-tiny model by trying it on different types of audio data, such as recordings of speeches, interviews, or conversations in various languages. You can also explore how the model performs on audio with different levels of noise or quality, and compare its results to other speech recognition or translation models.



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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