

# Whisper Small CGN

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the kul-speech-lab/CGN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3386
- Wer: 15.1972

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer     |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.1967        | 1.01  | 1000  | 0.4085          | 21.8459 |
| 0.1355        | 2.03  | 2000  | 0.3752          | 18.6212 |
| 0.2952        | 3.04  | 3000  | 0.3535          | 18.5841 |
| 0.1876        | 4.05  | 4000  | 0.3464          | 17.5097 |
| 0.1037        | 6.01  | 5000  | 0.3396          | 16.7360 |
| 0.0473        | 7.02  | 6000  | 0.3526          | 16.4131 |
| 0.1605        | 8.04  | 7000  | 0.3284          | 16.4012 |
| 0.0537        | 9.05  | 8000  | 0.3386          | 15.9454 |
| 0.0928        | 11.01 | 9000  | 0.3315          | 15.9568 |
| 0.0144        | 12.02 | 10000 | 0.3532          | 15.5387 |
| 0.0267        | 13.04 | 11000 | 0.3261          | 15.7577 |
| 0.0936        | 14.05 | 12000 | 0.3155          | 15.3380 |
| 0.0825        | 16.01 | 13000 | 0.3198          | 15.2653 |
| 0.0498        | 17.02 | 14000 | 0.3386          | 15.1972 |
| 0.0338        | 18.03 | 15000 | 0.3413          | 15.1972 |


### Framework versions

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

Whisper small 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 Small 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": 15.197170132057957, "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 Small CGN
=================


This model is a fine-tuned version of openai/whisper-small on the kul-speech-lab/CGN dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3386
* Wer: 15.1972


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* 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


### Training results



### Framework versions


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


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



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* 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 ### Training results ### Framework versions


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


Whisper small 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 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* 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 ### Training results ### Framework versions


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


Whisper small 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 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* 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### Training results### Framework versions


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


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