--- license: apache-2.0 language: - nb --- # 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 [More Information Needed] ### Downstream Use [optional] [More Information Needed] ### Out-of-Scope Use [More Information Needed] ## Bias, Risks, and Limitations [More Information Needed] ### 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. [More Information Needed] ## Training Details ### Training Data [More Information Needed] ### Training Procedure #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] #### Speeds, Sizes, Times [optional] [More Information Needed] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data [More Information Needed] #### Factors [More Information Needed] #### Metrics [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] [More Information Needed] ## Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]