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# whisper-medium-ar-no_diacritics

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: 0.1762
- Wer: 7.4970

## 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1114        | 1.01  | 400  | 0.1300          | 9.9764 |
| 0.0682        | 2.02  | 800  | 0.1157          | 8.9138 |
| 0.0302        | 3.03  | 1200 | 0.1274          | 8.2645 |
| 0.0151        | 4.04  | 1600 | 0.1277          | 7.7922 |
| 0.0104        | 5.05  | 2000 | 0.1304          | 7.7922 |
| 0.0069        | 6.06  | 2400 | 0.1476          | 8.4416 |
| 0.0033        | 7.07  | 2800 | 0.1307          | 7.7332 |
| 0.0026        | 8.08  | 3200 | 0.1425          | 8.3235 |
| 0.001         | 9.09  | 3600 | 0.1530          | 8.2054 |
| 0.0006        | 10.1  | 4000 | 0.1586          | 7.9693 |
| 0.0008        | 11.11 | 4400 | 0.1601          | 7.6151 |
| 0.001         | 12.12 | 4800 | 0.1647          | 8.0874 |
| 0.001         | 13.13 | 5200 | 0.1650          | 7.7332 |
| 0.0001        | 14.14 | 5600 | 0.1671          | 7.4380 |
| 0.0001        | 15.15 | 6000 | 0.1694          | 7.2609 |
| 0.0001        | 16.16 | 6400 | 0.1726          | 7.4970 |
| 0.0002        | 17.17 | 6800 | 0.1744          | 7.4380 |
| 0.0001        | 18.18 | 7200 | 0.1752          | 7.4970 |
| 0.0           | 19.19 | 7600 | 0.1758          | 7.4970 |
| 0.0           | 20.2  | 8000 | 0.1762          | 7.4970 |


### 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-no_diacritics", "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": 7.4970484061393154, "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-no\_diacritics
================================


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: 0.1762
* Wer: 7.4970


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