scribe-project/wav2vec2-large-voxrex-300m-stortinget

Model Card for Model ID

This is a wav2vec2 model fined tuned on a Norwegian dataset from the Norwegian parliament proceedings.

Model Details

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

Model Description

• 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]

• 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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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

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Training Procedure

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Training Hyperparameters

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Evaluation

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Results

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Summary

Model Examination [optional]

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Environmental Impact

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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