wav2vec2-xls-r-300m
Maintainer: facebook
69
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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 wav2vec2-xls-r-300m model is Facebook's large-scale multilingual pretrained model for speech. It uses the wav2vec 2.0 objective and is pretrained on 436,000 hours of unlabeled speech data across 128 languages, including datasets like VoxPopuli, MLS, CommonVoice, BABEL, and VoxLingua107. This model demonstrates strong performance on a wide range of speech tasks and languages, including speech recognition, translation, and language identification. Compared to the wav2vec2-base-960h model, which is pretrained on 960 hours of English speech data, the wav2vec2-xls-r-300m model leverages significantly more multilingual data to achieve better cross-lingual generalization.
Model inputs and outputs
Inputs
- Audio waveform sampled at 16kHz
Outputs
- Text transcription of the input speech
- (Optional) Speech translation to a target language
- (Optional) Language identification
Capabilities
The wav2vec2-xls-r-300m model exhibits strong performance on a variety of speech tasks, including automatic speech recognition (ASR), speech translation, and language identification. It achieves state-of-the-art results on benchmarks like BABEL, MLS, CommonVoice, and VoxLingua107, outperforming previous models by a significant margin.
What can I use it for?
The wav2vec2-xls-r-300m model can be used as a powerful multilingual speech processing tool for a variety of applications, such as:
- Automatic speech recognition: Transcribe speech in multiple languages with high accuracy.
- Speech translation: Translate spoken content between languages.
- Voice-based user interfaces: Enable voice-based interactions in a wide range of languages.
- Accessibility tools: Provide spoken content transcription and translation to improve accessibility.
Things to try
One interesting aspect of the wav2vec2-xls-r-300m model is its ability to perform well on low-resource languages, thanks to the large-scale multilingual pretraining. You could try fine-tuning the model on a specific low-resource language dataset and observe the performance improvement compared to training from scratch. Additionally, you could explore the model's cross-lingual capabilities by using it to translate speech between languages, even when the input and output languages differ from the ones used during pretraining.
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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