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# whisper-training-blog

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0050
- Wer: 191.2342

## 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: 7.5e-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.3
- training_steps: 448
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer      |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4111        | 0.1   | 44   | 1.4919          | 245.3457 |
| 1.0501        | 0.2   | 88   | 1.2255          | 225.8822 |
| 0.9032        | 0.29  | 132  | 1.1203          | 211.6558 |
| 0.8141        | 1.06  | 176  | 1.0675          | 184.6240 |
| 0.8029        | 1.16  | 220  | 1.0394          | 178.4129 |
| 0.6325        | 1.25  | 264  | 1.0301          | 216.6374 |
| 0.6971        | 2.02  | 308  | 1.0135          | 184.4004 |
| 0.6051        | 2.12  | 352  | 1.0065          | 194.7150 |
| 0.6047        | 2.21  | 396  | 1.0029          | 166.9328 |
| 0.585         | 2.31  | 440  | 1.0050          | 191.2342 |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["fleurs"], "metrics": ["wer"], "model-index": [{"name": "whisper-training-blog", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "fleurs", "type": "fleurs", "config": "sv_se", "split": "validation", "args": "sv_se"}, "metrics": [{"type": "wer", "value": 191.23423279578478, "name": "Wer"}]}]}]}
transformers pytorch whisper automatic-speech-recognition generated_from_trainer dataset:fleurs license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #whisper #automatic-speech-recognition #generated_from_trainer #dataset-fleurs #license-apache-2.0 #model-index #endpoints_compatible #region-us 

whisper-training-blog
=====================


This model is a fine-tuned version of openai/whisper-tiny on the fleurs dataset.
It achieves the following results on the evaluation set:


* Loss: 1.0050
* Wer: 191.2342


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: 7.5e-06
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.3
* training\_steps: 448
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 7.5e-06
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.3
* training\_steps: 448
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #whisper #automatic-speech-recognition #generated_from_trainer #dataset-fleurs #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 7.5e-06
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.3
* training\_steps: 448
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #whisper #automatic-speech-recognition #generated_from_trainer #dataset-fleurs #license-apache-2.0 #model-index #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 7.5e-06
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.3
* training\_steps: 448
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3