| --- |
| language: |
| - 'no' |
| license: apache-2.0 |
| tags: |
| - whisper-event |
| - norwegian |
| datasets: |
| - NbAiLab/NCC_S |
| - NbAiLab/NPSC |
| - NbAiLab/NST |
| metrics: |
| - wer |
| model-index: |
| - name: Whisper Large 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: 10.718635559082031 |
| duplicated_from: NbAiLab/whisper-large-v2-nob |
| --- |
| |
| # Whisper Large Norwegian Bokmål |
|
|
| This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) 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: 0.2477 |
| - Wer: 10.718635559082031 |
|
|
| ## 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: 64 |
| - gradient_accumulation_steps: 2 |
| - eval_batch_size: 32 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: constant with warmpu |
| - lr_scheduler_warmup_steps: 1000 |
| - training_steps: 50.000 (currently @1.000) |
| - mixed_precision_training: fp16 |
| - deepspeed: true |
| |
| ### Live Training results |
| See [Tensorboad Metrics](https://huggingface.co/NbAiLab/whisper-large-v2-nob/tensorboard) |
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