whispertest / README.md
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 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
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.
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