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# whisper-small-wolof

This model is a fine-tuned version of [cifope/whisper-small-wolof](https://huggingface.co/cifope/whisper-small-wolof) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.501167
- Wer: 27.1604

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

the model was trained on 9000 examples and tested on 1500 examples

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- 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_steps: 500
- training_steps: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0071        | 5.32  | 1000 | 0.501167        | 27.1604 |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
{"language": ["wo"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "pipeline_tag": "automatic-speech-recognition", "base_model": "cifope/whisper-small-wolof", "model-index": [{"name": "whisper-small-wolof", "results": []}]}
transformers pytorch tensorboard safetensors whisper automatic-speech-recognition generated_from_trainer wo base_model:cifope/whisper-small-wolof license:apache-2.0 endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #wo #base_model-cifope/whisper-small-wolof #license-apache-2.0 #endpoints_compatible #has_space #region-us 

whisper-small-wolof
===================


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


* Loss: 0.501167
* Wer: 27.1604


Model description
-----------------


More information needed


Intended uses & limitations
---------------------------


More information needed


Training and evaluation data
----------------------------


the model was trained on 9000 examples and tested on 1500 examples


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* 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\_steps: 500
* training\_steps: 1000
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* 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\_steps: 500
* training\_steps: 1000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #wo #base_model-cifope/whisper-small-wolof #license-apache-2.0 #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* 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\_steps: 500
* training\_steps: 1000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3