wav2vec2-large-960h-lv60-self
Maintainer: facebook
118
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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
Facebook's Wav2Vec2 is a large model pretrained and fine-tuned on 960 hours of Libri-Light and Librispeech on 16kHz sampled speech audio. The model was trained with a Self-Training objective. wav2vec2-large-960h-lv60-self demonstrates state-of-the-art performance on speech recognition tasks, outperforming the previous best semi-supervised methods while using a simpler approach.
Similar models include wav2vec2-base-960h, which is a smaller base model pretrained on the same Librispeech data, and wav2vec2-xls-r-300m, a large multilingual version of Wav2Vec2 pretrained on 436k hours of speech data across 128 languages.
Model inputs and outputs
Inputs
- Audio: The model takes raw speech audio as input, which must be sampled at 16kHz.
Outputs
- Transcription: The model outputs a text transcription of the input speech audio.
Capabilities
The wav2vec2-large-960h-lv60-self model demonstrates state-of-the-art performance on speech recognition tasks, achieving 1.8/3.3 WER on the clean/other Librispeech test sets when using all labeled data. It can also achieve strong results with limited labeled data, outperforming previous methods on the 100 hour Librispeech subset while using 100 times less labeled data.
What can I use it for?
The wav2vec2-large-960h-lv60-self model is well-suited for building speech recognition systems, particularly for applications that require high accuracy on a variety of speech inputs. It can be used as a standalone acoustic model to transcribe audio files, or integrated into larger speech processing pipelines.
Things to try
One interesting aspect of the wav2vec2-large-960h-lv60-self model is its ability to perform well with limited labeled data. Developers could experiment with fine-tuning the model on domain-specific datasets to adapt it for specialized use cases, potentially achieving strong results even when only a small amount of labeled data is available.
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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