wav2vec2-large-voxrex-300m-combined-long

Model Card for wav2vec2-large-voxrex-300m-combined-long

This is a wav2vec2 model fined tuned on a Norwegian dataset combining data from the Norwegian parliament proceedings and broadcast news.

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

Model Sources

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

  @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

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How to Get Started with the Model

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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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Evaluation

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Testing Data, Factors & Metrics

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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]

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