mms-1b-all
Maintainer: facebook
88
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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 mms-1b-all model is a massively multilingual speech recognition model developed by Facebook as part of their Massive Multilingual Speech project. This model is based on the Wav2Vec2 architecture and has been fine-tuned on 1162 languages, making it capable of transcribing speech in over 1,000 different languages. The model consists of 1 billion parameters and can be used with the Transformers library for speech transcription.
Model inputs and outputs
Inputs
- Audio: The model takes audio input in the form of 16kHz waveforms.
Outputs
- Transcribed text: The model outputs transcribed text in the language of the input audio.
Capabilities
The mms-1b-all model is capable of transcribing speech in over 1,000 different languages, making it a powerful tool for multilingual speech recognition. This model can be particularly useful for applications that require support for a wide range of languages, such as international call centers, multilingual content creation, or language learning platforms.
What can I use it for?
The mms-1b-all model can be used for a variety of applications that require transcription of speech in multiple languages. For example, it could be used to automatically generate captions or subtitles for videos in a wide range of languages, or to enable voice-controlled interfaces that work across multiple languages. Additionally, the model could be used as a starting point for fine-tuning on specific domains or languages to further improve performance.
Things to try
One interesting aspect of the mms-1b-all model is its ability to handle a large number of languages. You could experiment with transcribing speech samples in different languages to see how the model performs across a diverse set of linguistic backgrounds. Additionally, you could try fine-tuning the model on a specific language or domain to see if you can improve its performance for your particular use case.
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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