
# Whisper Large CGN

This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the kul-speech-lab/CGN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.23932012915611267
- Wer: 9.615871912312803

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- gradient_accumulation_steps: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 15000
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.0
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2

Whisper large model finetuned on Flemish part of Corpus Gesproken Nederlands (CGN).

{"license": "apache-2.0", "tags": ["whisper-event", "generated_from_trainer"], "datasets": ["kul-speech-lab/CGN"], "metrics": ["wer"], "model-index": [{"name": "Whisper Large CGN", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "kul-speech-lab/CGN", "type": "kul-speech-lab/CGN", "config": "cgn-dev.py", "split": "test"}, "metrics": [{"type": "wer", "value": 9.6159, "name": "Wer"}]}]}]}
transformers whisper automatic-speech-recognition whisper-event generated_from_trainer dataset:kul-speech-lab/CGN license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #whisper #automatic-speech-recognition #whisper-event #generated_from_trainer #dataset-kul-speech-lab/CGN #license-apache-2.0 #model-index #endpoints_compatible #region-us 


# Whisper Large CGN

This model is a fine-tuned version of openai/whisper-large-v2 on the kul-speech-lab/CGN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.23932012915611267
- Wer: 9.615871912312803

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- gradient_accumulation_steps: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 15000
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.0
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2

Whisper large model finetuned on Flemish part of Corpus Gesproken Nederlands (CGN).

# Whisper Large CGN

This model is a fine-tuned version of openai/whisper-large-v2 on the kul-speech-lab/CGN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.23932012915611267
- Wer: 9.615871912312803 ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- gradient_accumulation_steps: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 15000
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.0
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2

Whisper large model finetuned on Flemish part of Corpus Gesproken Nederlands (CGN).
TAGS
#transformers #whisper #automatic-speech-recognition #whisper-event #generated_from_trainer #dataset-kul-speech-lab/CGN #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 # Whisper Large CGN

This model is a fine-tuned version of openai/whisper-large-v2 on the kul-speech-lab/CGN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.23932012915611267
- Wer: 9.615871912312803 ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- gradient_accumulation_steps: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 15000
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.0
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2

Whisper large model finetuned on Flemish part of Corpus Gesproken Nederlands (CGN).
TAGS
#transformers #whisper #automatic-speech-recognition #whisper-event #generated_from_trainer #dataset-kul-speech-lab/CGN #license-apache-2.0 #model-index #endpoints_compatible #region-us 
# Whisper Large CGN

This model is a fine-tuned version of openai/whisper-large-v2 on the kul-speech-lab/CGN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.23932012915611267
- Wer: 9.615871912312803### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- gradient_accumulation_steps: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 15000
- mixed_precision_training: Native AMP### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.0
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2

Whisper large model finetuned on Flemish part of Corpus Gesproken Nederlands (CGN).