whisper-medium
Maintainer: openai
176
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The whisper-medium model is a pre-trained speech recognition and translation model developed by OpenAI. It is part of the Whisper family of models, which demonstrate a strong ability to generalize to many datasets and domains without the need for fine-tuning. The whisper-medium model has 769 million parameters and is trained on either English-only or multilingual data. It can be used for both speech recognition, where it transcribes audio in the same language, and speech translation, where it transcribes audio to a different language. The Whisper models are available in a range of sizes, from the whisper-tiny with 39 million parameters to the whisper-large and whisper-large-v2 with 1.55 billion parameters.
Model inputs and outputs
Inputs
- Audio samples in various formats and sampling rates
Outputs
- Transcriptions of the input audio, either in the same language (speech recognition) or a different language (speech translation)
- Optionally, the model can also output timestamps for the transcribed text
Capabilities
The Whisper models demonstrate strong performance on a variety of speech recognition and translation tasks, including handling accents, background noise, and technical language. They can be used in zero-shot translation, taking audio in one language and translating it to English without any fine-tuning. However, the models can also sometimes generate text that is not actually present in the audio input (known as "hallucination"), and their performance can vary across different languages and accents.
What can I use it for?
The whisper-medium model and the other Whisper models can be useful for developers and researchers working on improving accessibility tools, such as closed captioning or subtitle generation. The models' speed and accuracy suggest they could be used to build near-real-time speech recognition and translation applications. However, users should be aware of the models' limitations, particularly around potential biases and disparate performance across languages and accents.
Things to try
One interesting aspect of the Whisper models is their ability to handle audio of up to arbitrary length through a chunking algorithm. This allows the models to be used for long-form transcription, where the audio is split into smaller segments and then reassembled. Users can experiment with this functionality to see how it performs on their specific use cases.
Additionally, the Whisper models can be fine-tuned on smaller, domain-specific datasets to improve their performance in particular areas. The blog post on fine-tuning Whisper provides a step-by-step guide on how to do this.
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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