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 presented in Lacoste et al. (2019).
- 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]