| language: | |
| - no | |
| license: apache-2.0 | |
| tags: | |
| - whisper-event | |
| - norwegian | |
| datasets: | |
| - NbAiLab/NCC_S | |
| - NbAiLab/NPSC | |
| - NbAiLab/NST | |
| metrics: | |
| - wer | |
| model-index: | |
| - name: Whisper Tiny Norwegian Bokmål | |
| results: | |
| - task: | |
| name: Automatic Speech Recognition | |
| type: automatic-speech-recognition | |
| dataset: | |
| name: FLEURS | |
| type: google/fleurs | |
| config: nb_no | |
| split: validation | |
| args: nb_no | |
| metrics: | |
| - name: Wer | |
| type: wer | |
| value: 45.73 | |
| # Whisper Tiny Norwegian Bokmål | |
| This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) trained on several datasets. | |
| It is currently in the middle of a large training. Currently it achieves the following results on the evaluation set: | |
| - Loss: 1.4616 | |
| - Wer: 45.73 | |
| ## Model description | |
| The model is trained on a large corpus of roughly 5.000 hours of voice. The sources are subtitles from the Norwegian broadcaster NRK, transcribed speeches from the Norwegian parliament and voice recordings from Norsk Språkteknologi. | |
| ## Intended uses & limitations | |
| The model will be free for everyone to use when it is finished. | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 3e-06 | |
| - train_batch_size: 128 | |
| - eval_batch_size: 32 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_steps: 1000 | |
| - training_steps: 100.000 (currently @5.000) | |
| - mixed_precision_training: fp16 | |
| ### Live Training results | |
| See [Tensorboad Metrics](https://huggingface.co/NbAiLab/whisper-tiny-nob/tensorboard) | |