
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# base Turkish Whisper (bTW)

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Ermetal Meetings dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1836
- Wer: 1.7109
- Cer: 1.2860

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 1.7878        | 4.74  | 100  | 1.4516          | 0.8560 | 0.5525 |
| 0.6701        | 9.51  | 200  | 0.9194          | 0.8543 | 0.6112 |
| 0.3364        | 14.28 | 300  | 0.8871          | 0.7415 | 0.4992 |
| 0.1228        | 19.05 | 400  | 0.9671          | 0.9052 | 0.6678 |
| 0.0355        | 23.78 | 500  | 1.0515          | 0.8961 | 0.6208 |
| 0.0148        | 28.55 | 600  | 1.0684          | 0.6644 | 0.3694 |
| 0.0056        | 33.32 | 700  | 1.1488          | 1.3315 | 0.8732 |
| 0.0041        | 38.09 | 800  | 1.1700          | 1.7415 | 1.1934 |
| 0.0034        | 42.83 | 900  | 1.1801          | 1.7745 | 1.2643 |
| 0.0032        | 47.6  | 1000 | 1.1836          | 1.7109 | 1.2860 |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.12.0+cu102
- Datasets 2.9.0
- Tokenizers 0.13.2

{"language": ["tr"], "license": "apache-2.0", "tags": ["hf-asr-leaderboard", "generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "base Turkish Whisper (bTW)", "results": []}]}
transformers pytorch tensorboard whisper automatic-speech-recognition hf-asr-leaderboard generated_from_trainer tr license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #tr #license-apache-2.0 #endpoints_compatible #region-us 

base Turkish Whisper (bTW)
==========================


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


* Loss: 1.1836
* Wer: 1.7109
* Cer: 1.2860


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: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 1000
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.26.0
* Pytorch 1.12.0+cu102
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 1000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.12.0+cu102
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #tr #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 1000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.12.0+cu102
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #tr #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 1000
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.26.0
* Pytorch 1.12.0+cu102
* Datasets 2.9.0
* Tokenizers 0.13.2