readme
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README.md
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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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metrics:
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- wer
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model-index:
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- name: Whisper Small 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: 23.93
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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 training. Currently achieves the following results on the evaluation set:
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- Loss: 0.4780
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- Wer: 23.93
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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 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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- gradient_accumulation_steps: 2
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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: constant with warmup
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- lr_scheduler_warmup_steps: 1000
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- training_steps: 50.000 (currently @1.000)
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- mixed_precision_training: fp16
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- deepspee: true
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### Training results
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See [Tensorboad Metrics](https://huggingface.co/NbAiLab/whisper-small-nob/tensorboard)
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runs/Dec15_19-44-15_dante/events.out.tfevents.1671129887.dante.1198326.0
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:bb2820a34dec31732207a6a3ce568ff74e20f309d306614381064482cad718d5
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size 13230
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