
# Whisper Medium CGN

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the kul-speech-lab/CGN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2639
- Wer: 10.7278

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 32
- 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

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer     |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.1116        | 1.01  | 1000  | 0.2978          | 15.2127 |
| 0.0786        | 2.03  | 2000  | 0.2842          | 13.4852 |
| 0.2042        | 3.04  | 3000  | 0.2656          | 13.3590 |
| 0.1183        | 4.05  | 4000  | 0.2667          | 12.6977 |
| 0.0584        | 6.01  | 5000  | 0.2604          | 12.0993 |
| 0.0126        | 7.02  | 6000  | 0.2776          | 12.1477 |
| 0.0837        | 8.04  | 7000  | 0.2541          | 11.9397 |
| 0.0229        | 9.05  | 8000  | 0.2663          | 11.3221 |
| 0.042         | 11.01 | 9000  | 0.2549          | 11.4863 |
| 0.0075        | 12.02 | 10000 | 0.2775          | 11.0780 |
| 0.008         | 13.03 | 11000 | 0.2499          | 10.9759 |
| 0.0739        | 14.05 | 12000 | 0.2308          | 10.9441 |
| 0.0379        | 16.01 | 13000 | 0.2423          | 10.7926 |
| 0.02          | 17.02 | 14000 | 0.2629          | 10.7699 |
| 0.0111        | 18.03 | 15000 | 0.2639          | 10.7278 |


### Framework versions

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

Whisper medium 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 Medium 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": 10.727751271110364, "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 Medium CGN
==================


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


* Loss: 0.2639
* Wer: 10.7278


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 64
* eval\_batch\_size: 32
* 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


### Training results



### Framework versions


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


Whisper medium 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: 64
* eval\_batch\_size: 32
* 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 ### Training results ### Framework versions


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


Whisper medium 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: 64
* eval\_batch\_size: 32
* 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 ### Training results ### Framework versions


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


Whisper medium 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: 64
* eval\_batch\_size: 32
* 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### Training results### Framework versions


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


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