
# Whisper Small Norwegian Bokmål

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) trained on NCC_S_3-NRKonly.

It is currently in the middle of a large training. 

## Model description

The model is trained on a large corpus of roughly 4.000 hours of voice. The sources are subtitles from the Norwegian broadcaster NRK.

## 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
- 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 warmup
- 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-small-3NRKonly-nob/tensorboard)





{"language": [false], "license": "apache-2.0", "tags": ["whisper-event", "norwegian"], "datasets": ["NbAiLab/NCC_S", "NbAiLab/NPSC", "NbAiLab/NST"], "metrics": ["wer"], "model-index": [{"name": "Whisper Small Norwegian Bokm\u00e5l", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "FLEURS", "type": "google/fleurs", "config": "nb_no", "split": "validation", "args": "nb_no"}, "metrics": [{"type": "wer", "value": 15.56, "name": "Wer"}]}]}]}
transformers whisper automatic-speech-recognition whisper-event norwegian no dataset:NbAiLab/NCC_S dataset:NbAiLab/NPSC dataset:NbAiLab/NST license:apache-2.0 model-index endpoints_compatible has_space region:us
TAGS
#transformers #whisper #automatic-speech-recognition #whisper-event #norwegian #no #dataset-NbAiLab/NCC_S #dataset-NbAiLab/NPSC #dataset-NbAiLab/NST #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 


# Whisper Small Norwegian Bokmål

This model is a fine-tuned version of openai/whisper-small trained on NCC_S_3-NRKonly.

It is currently in the middle of a large training. 

## Model description

The model is trained on a large corpus of roughly 4.000 hours of voice. The sources are subtitles from the Norwegian broadcaster NRK.

## 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
- 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 warmup
- lr_scheduler_warmup_steps: 1000
- training_steps: 50.000 (currently @1.000)
- mixed_precision_training: fp16
- deepspeed: true

### Live Training results
See Tensorboad Metrics





# Whisper Small Norwegian Bokmål

This model is a fine-tuned version of openai/whisper-small trained on NCC_S_3-NRKonly.

It is currently in the middle of a large training. ## Model description

The model is trained on a large corpus of roughly 4.000 hours of voice. The sources are subtitles from the Norwegian broadcaster NRK. ## 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
- 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 warmup
- lr_scheduler_warmup_steps: 1000
- training_steps: 50.000 (currently @1.000)
- mixed_precision_training: fp16
- deepspeed: true ### Live Training results
See Tensorboad Metrics
TAGS
#transformers #whisper #automatic-speech-recognition #whisper-event #norwegian #no #dataset-NbAiLab/NCC_S #dataset-NbAiLab/NPSC #dataset-NbAiLab/NST #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 
 # Whisper Small Norwegian Bokmål

This model is a fine-tuned version of openai/whisper-small trained on NCC_S_3-NRKonly.

It is currently in the middle of a large training. ## Model description

The model is trained on a large corpus of roughly 4.000 hours of voice. The sources are subtitles from the Norwegian broadcaster NRK. ## 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
- 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 warmup
- lr_scheduler_warmup_steps: 1000
- training_steps: 50.000 (currently @1.000)
- mixed_precision_training: fp16
- deepspeed: true ### Live Training results
See Tensorboad Metrics
TAGS
#transformers #whisper #automatic-speech-recognition #whisper-event #norwegian #no #dataset-NbAiLab/NCC_S #dataset-NbAiLab/NPSC #dataset-NbAiLab/NST #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 
# Whisper Small Norwegian Bokmål

This model is a fine-tuned version of openai/whisper-small trained on NCC_S_3-NRKonly.

It is currently in the middle of a large training.## Model description

The model is trained on a large corpus of roughly 4.000 hours of voice. The sources are subtitles from the Norwegian broadcaster NRK.## 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
- 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 warmup
- lr_scheduler_warmup_steps: 1000
- training_steps: 50.000 (currently @1.000)
- mixed_precision_training: fp16
- deepspeed: true### Live Training results
See Tensorboad Metrics