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--- |
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language: |
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- no |
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license: apache-2.0 |
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tags: |
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- whisper-event |
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- norwegian |
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datasets: |
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- NbAiLab/NCC_S |
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- NbAiLab/NPSC |
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- NbAiLab/NST |
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- google/fleurs |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Tiny Norwegian Bokmål |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: FLEURS |
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type: google/fleurs |
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config: nb_no |
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split: test |
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args: nb_no |
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metrics: |
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- name: Wer |
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type: wer |
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value: 47.08 |
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--- |
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# Whisper Tiny Norwegian Bokmål |
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This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) trained on several datasets. |
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It is currently in the middle of a large trainingi. Currently achieves the following results on the evaluation set: |
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- Loss: 1.464 |
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- Wer: 47.08 |
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## Model description |
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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. |
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## Intended uses & limitations |
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The model will be free for everyone to use when it is finished. |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-06 |
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- train_batch_size: 128 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- training_steps: 100.000 (currently 4.000) |
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- mixed_precision_training: fp16 |
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### Training results |
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See [Tensorboad Metrics](https://huggingface.co/NbAiLab/whisper-tiny-nob/tensorboard) |
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