Update README.md

README.md

@@ -130,6 +130,15 @@ asr(
 #    'text': ' Først så kan vi ta og henge dem kjemme, og så får vi gjøre vårt valget når vi kommer dit.'}]}
 ```
 
+## Training Data
+Trained data comes from Språkbanken and the digital collection at the National Library of Norway. Training data includes:
+
+- NST Norwegian ASR Database (16 kHz), and its corresponding dataset
+- Transcribed speeches from the Norwegian Parliament produced by Språkbanken
+- TV broadcast (NRK) subtitles (NLN digital collection)
+- Audiobooks (NLN digital collection)
+
+
 ## Environmental Impact
 
 <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
@@ -145,7 +154,7 @@ Carbon emissions estimated using the [Machine Learning Impact calculator](https:
 
 #### Software
 
-The model is trained using Jax/Flax. The final model is converted to Pytorch, whisper.cpp and ONXX. Please tell us if you would like future models to be converted to other format. 
+The model is trained using Jax/Flax. The final model is converted to Pytorch, Tensorflow, whisper.cpp and ONXX. Please tell us if you would like future models to be converted to other format. 
 
 ## Citation & Authors
 This model was developed within the scope of the _NoSTram_ project, led by _Per Egil Kummervold_. The Jax code and training scripts were crafted by _Javier de la Rosa_, _Freddy Wetjen_, _Rolv-Arild Braaten_, and _Per Egil Kummervold_. Dataset curation was carried out by _Freddy Wetjen_, _Rolv-Arild Braaten_, and _Per Egil Kummervold_. Documentation was composed by _Javier de la Rosa_ and _Per Egil Kummervold_. The AiLab is under the direction of _Svein Arne Brygfjeld_. Each author contributed to the development and deliberations on the optimal way to train a Norwegian ASR model using Whisper. The work on this model was conducted as part of our professional roles at the National Library of Norway.