wav2vec2-large-voxrex-300m-combined-short

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Model Details

Model Description

The model is fined tuned from a Swedish model with 300 million parameters trained by the Swedish Royal Library.

• Developed by: The SCRIBE project https://scribe-project.github.io/
• Shared by [optional]: The SCRIBE project https://scribe-project.github.io/
• Model type: wav2vec2
• Language(s) (NLP): Norwegian
• License: Apache 2.0
• Finetuned from model [optional]: KBLab/wav2vec2-large-voxrex

Model Sources [optional]

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• Repository: https://github.com/scribe-project/nodalida_2023_combined_training
• Paper [optional]:

@InProceedings{SolbergEtAlNoDaLiDa2023,
author = {Per Erik Solberg and Pablo Ortiz and Phoebe Parsons and Torbjørn Svendsen and Giampiero Salvi},	 
title = {Improving Generalization of Norwegian ASR with Limited Linguistic Resources},
booktitle = {Proceedings of the 24th Nordic Conference on Computational Linguistics},
year = 	 {2023},
month = 	 {May},
address = 	 {Tórshavn, Faroe Islands},
}

Uses

The model can be used for automatic speech recognition in Norwegian, and other tasks involving speech technology

Direct Use

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Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

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How to Get Started with the Model

Use the code below to get started with the model.

https://github.com/scribe-project/nodalida_2023_combined_training

Training Details

Training Data

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Training Procedure

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Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Factors

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Results

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Summary

Model Examination [optional]

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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Citation [optional]

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Glossary [optional]

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