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# whisper-medium-ar-original

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1852
- Wer: 14.1086

## 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: 24
- eval_batch_size: 24
- 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: 8000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.115         | 1.01  | 400  | 0.1204          | 18.6541 |
| 0.0774        | 2.02  | 800  | 0.1074          | 15.5844 |
| 0.0438        | 3.03  | 1200 | 0.1160          | 16.4699 |
| 0.0233        | 4.04  | 1600 | 0.1279          | 15.1122 |
| 0.0131        | 5.05  | 2000 | 0.1350          | 15.5254 |
| 0.0051        | 6.06  | 2400 | 0.1455          | 14.9941 |
| 0.0035        | 7.07  | 2800 | 0.1464          | 14.1677 |
| 0.0032        | 8.08  | 3200 | 0.1545          | 14.8170 |
| 0.0013        | 9.09  | 3600 | 0.1623          | 13.8725 |
| 0.0013        | 10.1  | 4000 | 0.1543          | 13.4002 |
| 0.0006        | 11.11 | 4400 | 0.1653          | 14.1677 |
| 0.0006        | 12.12 | 4800 | 0.1699          | 13.7544 |
| 0.0003        | 13.13 | 5200 | 0.1705          | 13.4593 |
| 0.0001        | 14.14 | 5600 | 0.1733          | 13.6954 |
| 0.0002        | 15.15 | 6000 | 0.1768          | 13.8725 |
| 0.0001        | 16.16 | 6400 | 0.1786          | 13.7544 |
| 0.0           | 17.17 | 6800 | 0.1826          | 13.9906 |
| 0.0           | 18.18 | 7200 | 0.1839          | 14.0496 |
| 0.0           | 19.19 | 7600 | 0.1848          | 14.0496 |
| 0.0           | 20.2  | 8000 | 0.1852          | 14.1086 |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["audiofolder"], "metrics": ["wer"], "model-index": [{"name": "whisper-medium-ar-original", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "audiofolder", "type": "audiofolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "wer", "value": 14.108618654073199, "name": "Wer"}]}]}]}
transformers pytorch tensorboard whisper automatic-speech-recognition generated_from_trainer dataset:audiofolder license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #generated_from_trainer #dataset-audiofolder #license-apache-2.0 #model-index #endpoints_compatible #region-us 

whisper-medium-ar-original
==========================


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


* Loss: 0.1852
* Wer: 14.1086


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: 24
* eval\_batch\_size: 24
* 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: 8000
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.25.1
* Pytorch 1.12.1
* Datasets 2.8.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 24
* eval\_batch\_size: 24
* 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: 8000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.12.1
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #generated_from_trainer #dataset-audiofolder #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: 24
* eval\_batch\_size: 24
* 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: 8000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.12.1
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #generated_from_trainer #dataset-audiofolder #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: 24
* eval\_batch\_size: 24
* 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: 8000
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.25.1
* Pytorch 1.12.1
* Datasets 2.8.0
* Tokenizers 0.13.2