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# Whisper Small Sv - Riksdag 100h

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4977
- Wer: 1118.4718

## 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: 8
- 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_steps: 500
- training_steps: 20000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer       |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|
| 0.1384        | 0.11  | 1000  | 0.4747          | 380.8335  |
| 0.1186        | 0.22  | 2000  | 0.4513          | 1032.3900 |
| 0.1056        | 0.33  | 3000  | 0.4385          | 582.0427  |
| 0.0824        | 0.43  | 4000  | 0.4465          | 574.8907  |
| 0.0961        | 0.54  | 5000  | 0.4199          | 1004.9138 |
| 0.0939        | 0.65  | 6000  | 0.4478          | 866.2979  |
| 0.0758        | 0.76  | 7000  | 0.4384          | 907.9496  |
| 0.0741        | 0.87  | 8000  | 0.4264          | 641.1371  |
| 0.0692        | 0.98  | 9000  | 0.4206          | 1142.6550 |
| 0.0257        | 1.08  | 10000 | 0.4707          | 1152.4312 |
| 0.0273        | 1.19  | 11000 | 0.4789          | 1100.2058 |
| 0.021         | 1.3   | 12000 | 0.4763          | 1236.1719 |
| 0.0163        | 1.41  | 13000 | 0.5035          | 924.8006  |
| 0.0183        | 1.52  | 14000 | 0.4911          | 1285.1814 |
| 0.024         | 1.63  | 15000 | 0.4861          | 1140.8284 |
| 0.0158        | 1.73  | 16000 | 0.4793          | 1181.7597 |
| 0.0167        | 1.84  | 17000 | 0.4759          | 1207.3064 |
| 0.0231        | 1.95  | 18000 | 0.4801          | 1139.6964 |
| 0.0054        | 2.06  | 19000 | 0.4934          | 1114.4842 |
| 0.006         | 2.17  | 20000 | 0.4977          | 1118.4718 |


### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1
- Datasets 2.10.1
- Tokenizers 0.13.2

{"language": ["sv"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "Whisper Small Sv - Riksdag 100h", "results": []}]}
transformers pytorch tensorboard whisper automatic-speech-recognition generated_from_trainer sv license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #generated_from_trainer #sv #license-apache-2.0 #endpoints_compatible #region-us 

Whisper Small Sv - Riksdag 100h
===============================


This model is a fine-tuned version of openai/whisper-small on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4977
* Wer: 1118.4718


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: 8
* 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\_steps: 500
* training\_steps: 20000
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 8
* 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\_steps: 500
* training\_steps: 20000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #generated_from_trainer #sv #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 8
* 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\_steps: 500
* training\_steps: 20000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #generated_from_trainer #sv #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 8
* 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\_steps: 500
* training\_steps: 20000
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1
* Datasets 2.10.1
* Tokenizers 0.13.2