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# Whisper Small German SBB all SNR - v6

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the SBB Dataset 05.01.2023 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0426
- Wer: 0.0266

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- 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: 100
- training_steps: 400
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.7233        | 0.04  | 100  | 0.4161          | 0.2232 |
| 0.1932        | 0.09  | 200  | 0.0665          | 0.0361 |
| 0.0615        | 0.13  | 300  | 0.0666          | 0.0361 |
| 0.0677        | 0.18  | 400  | 0.0426          | 0.0266 |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1
- Datasets 2.8.0
- Tokenizers 0.12.1

{"language": ["de"], "license": "apache-2.0", "tags": ["sbb-asr", "generated_from_trainer"], "datasets": ["marccgrau/sbbdata_allSNR"], "metrics": ["wer"], "model-index": [{"name": "Whisper Small German SBB all SNR - v6", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "SBB Dataset 05.01.2023", "type": "marccgrau/sbbdata_allSNR", "args": "config: German, split: train, test, val"}, "metrics": [{"type": "wer", "value": 0.02663284717818643, "name": "Wer"}]}]}]}
transformers pytorch whisper automatic-speech-recognition sbb-asr generated_from_trainer de dataset:marccgrau/sbbdata_allSNR license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #whisper #automatic-speech-recognition #sbb-asr #generated_from_trainer #de #dataset-marccgrau/sbbdata_allSNR #license-apache-2.0 #model-index #endpoints_compatible #region-us 

Whisper Small German SBB all SNR - v6
=====================================


This model is a fine-tuned version of openai/whisper-small on the SBB Dataset 05.01.2023 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0426
* Wer: 0.0266


Model description
-----------------


More information needed


Intended uses & limitations
---------------------------


More information needed


Training and evaluation data
----------------------------


More information needed


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 4
* 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: 100
* training\_steps: 400
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1
* Datasets 2.8.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 4
* 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: 100
* training\_steps: 400
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1
* Datasets 2.8.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #whisper #automatic-speech-recognition #sbb-asr #generated_from_trainer #de #dataset-marccgrau/sbbdata_allSNR #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: 4
* 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: 100
* training\_steps: 400
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1
* Datasets 2.8.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #whisper #automatic-speech-recognition #sbb-asr #generated_from_trainer #de #dataset-marccgrau/sbbdata_allSNR #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: 4
* 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: 100
* training\_steps: 400
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1
* Datasets 2.8.0
* Tokenizers 0.12.1