wav2vec2-large-xlsr-53-russian
Maintainer: jonatasgrosman
42
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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-large-xlsr-53-russian model is a fine-tuned version of the facebook/wav2vec2-large-xlsr-53 model for speech recognition in Russian. It was fine-tuned by jonatasgrosman on the train and validation splits of the Common Voice 6.1 and CSS10 datasets. This model can be used directly (without a language model) for speech recognition tasks in Russian.
Similar models include the wav2vec2-large-xlsr-53-english and wav2vec2-large-xlsr-53-chinese-zh-cn models, which are fine-tuned for English and Chinese speech recognition respectively. The base facebook/wav2vec2-large-xlsr-53 model is also available for use.
Model inputs and outputs
Inputs
- Audio data: The model accepts audio data sampled at 16kHz. This is a requirement for using the model effectively.
Outputs
- Transcribed text: The model outputs transcribed text from the input audio data.
Capabilities
The wav2vec2-large-xlsr-53-russian model can be used for accurate speech recognition in the Russian language. It has been fine-tuned on diverse Russian speech data, allowing it to handle a variety of accents and speaking styles. The model achieves strong performance, as demonstrated by the provided evaluation results.
What can I use it for?
You can use this model for a variety of Russian speech recognition applications, such as:
- Transcribing audio recordings
- Powering voice-enabled interfaces
- Integrating speech recognition into your applications
- Improving accessibility by providing transcripts of audio content
The model's high accuracy and ability to handle diverse speech patterns make it a valuable tool for any project requiring Russian speech recognition capabilities.
Things to try
One interesting thing to try with this model is to experiment with different audio preprocessing techniques, such as applying noise reduction or voice activity detection. These techniques can potentially improve the model's performance on real-world audio data with background noise or non-speech segments.
You could also try combining this model with a language model to further improve the transcription accuracy, especially for common phrases or idioms. The HuggingSound library provides a convenient way to use this model for speech recognition tasks.
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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