lang-id-voxlingua107-ecapa
Maintainer: speechbrain
71
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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 lang-id-voxlingua107-ecapa model is a spoken language recognition model trained on the VoxLingua107 dataset using the SpeechBrain framework. It utilizes the ECAPA-TDNN architecture, which has previously been used for speaker recognition tasks. The model can classify speech utterances into one of 107 different languages, including Abkhazian, Afrikaans, Amharic, and many more. This model was developed by the speechbrain team.
The xlm-roberta-base-language-detection model is a fine-tuned version of the xlm-roberta-base model on the Language Identification dataset. It can classify text sequences into 20 different languages, including Arabic, English, French, and Chinese. This model was created by papluca.
Model inputs and outputs
Inputs
- Audio waveform (16 kHz, single channel)
Outputs
- Language classification (one of 107 languages)
Capabilities
The lang-id-voxlingua107-ecapa model can accurately classify speech utterances into one of 107 different languages. This can be useful for various applications, such as language identification in multilingual environments, language-specific speech processing, and language-aware user interfaces.
What can I use it for?
The lang-id-voxlingua107-ecapa model can be used as a standalone language identification system or as a feature extractor for creating a custom language ID model on your own data. For example, you could use this model to build a multilingual chatbot or transcription service that can handle a wide variety of languages.
Things to try
One interesting thing to try with the lang-id-voxlingua107-ecapa model is to use it as a feature extractor for downstream tasks. By taking the utterance embeddings produced by the model, you can create a dedicated language ID model tailored to your specific use case, potentially improving performance beyond the general-purpose capabilities of the pre-trained model.
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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